{
  "metadata": {
    "timestamp": "2026-02-17T13:21:32+0200",
    "queries": 8,
    "agent": "frontier LLM via MCP CLI client",
    "transport": "HTTP (localhost:8111)"
  },
  "summary": {
    "decomposition_rate": 1.0,
    "avg_search_calls": 3.125,
    "avg_source_coverage": 0.71875,
    "avg_keyword_coverage": 0.90625,
    "full_source_coverage_rate": 0.375,
    "avg_search_latency_ms": 757.0,
    "avg_search_latency_total_ms": 1466.5
  },
  "results": [
    {
      "original_query": "How do you terminate TLS at a load balancer and what cipher suites should you configure?",
      "difficulty": "medium",
      "search_calls": [
        {
          "query": "TLS termination load balancer cipher suites configuration",
          "query_type": "simple",
          "latency_s": 0.81
        },
        {
          "query": "load balancer TLS termination configuration recommended cipher suites",
          "query_type": "simple",
          "latency_s": 0.64
        },
        {
          "query": "ALB ELB NLB HTTPS listener SSL policy recommended",
          "query_type": "simple",
          "latency_s": 0.123
        }
      ],
      "sources_found": [
        "tls-ssl.md"
      ],
      "expected_sources": [
        "tls-ssl.md",
        "load-balancing.md"
      ],
      "keywords_found": [
        "cipher",
        "handshake",
        "certificate"
      ],
      "expected_keywords": [
        "cipher",
        "handshake",
        "layer 7",
        "certificate"
      ],
      "decomposed": true,
      "source_coverage": 0.5,
      "keyword_coverage": 0.75,
      "search_latency_s": 0.81,
      "search_latency_total_s": 1.573,
      "wall_time_s": 30.54,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about TLS termination at load balancers and cipher suite configuration.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"TLS termination load balancer cipher suites configuration\",\n \u22ee    \"n_results\": 5\n \u22ee  }\n - Completed in 0.81s\n\n> Let me search more specifically for load balancer TLS termination:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"load balancer TLS termination configuration recommended cipher suites\"\n \u22ee  }\n - Completed in 0.64s\n\n> The search results are returning general TLS information but not specific load balancer guidance. Let me try a different search:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"ALB ELB NLB HTTPS listener SSL policy recommended\"\n \u22ee  }\n - Completed in 0.123s\n\n> Based on the search results from Candlekeep, I found information about TLS/SSL and cipher suites, but the knowledge base doesn't contain specific documentation about load balancer TLS termination configuration.\n\n## What I Found\n\nSource file: tls-ssl.md\n\nThe document covers:\n\n- **TLS/SSL Handshake process** - How clients and servers establish encrypted connections\n- **Cipher Suites** - Combinations of algorithms for encryption, authentication, and key exchange\n  - Example: TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384\n  - Components: Key exchange (ECDHE), Authentication (ECDSA), Encryption (AES-256-GCM), MAC (SHA-384)\n- **Certificate chains and validation**\n- **Mutual TLS (mTLS)**\n- **Certificate pinning**\n- **OCSP stapling**\n\n## What's Missing\n\nThe knowledge base doesn't contain specific guidance on:\n- Load balancer TLS termination configuration\n- Recommended cipher suites for ALB/ELB/NLB\n- SSL policy selection for AWS load balancers\n- Best practices for production load balancer TLS setup\n\nThe tls-ssl.md document provides foundational TLS concepts but doesn't address the operational aspects of configuring TLS termination at load balancers.\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 22s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How does PostgreSQL's write-ahead log work with primary-replica replication?",
      "difficulty": "medium",
      "search_calls": [
        {
          "query": "PostgreSQL write-ahead log WAL primary replica replication",
          "query_type": "simple",
          "latency_s": 0.83
        },
        {
          "query": "write-ahead log WAL how it works transaction commit durability",
          "query_type": "simple",
          "latency_s": 0.102
        }
      ],
      "sources_found": [
        "database-replication.md",
        "postgresql-internals.md",
        "event-driven-architecture.md"
      ],
      "expected_sources": [
        "postgresql-internals.md",
        "database-replication.md"
      ],
      "keywords_found": [
        "WAL",
        "replica",
        "synchronous"
      ],
      "expected_keywords": [
        "WAL",
        "replica",
        "synchronous"
      ],
      "decomposed": true,
      "source_coverage": 1.0,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.83,
      "search_latency_total_s": 0.932,
      "wall_time_s": 28.18,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about PostgreSQL's write-ahead log and replication.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"PostgreSQL write-ahead log WAL primary replica replication\",\n \u22ee    \"n_results\": 5\n \u22ee  }\n - Completed in 0.83s\n\n> Let me search more specifically for write-ahead log mechanics:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"write-ahead log WAL how it works transaction commit durability\"\n \u22ee  }\n - Completed in 0.102s\n\n> Based on the Candlekeep search results, here's how PostgreSQL's write-ahead log works with primary-replica replication:\n\n## Write-Ahead Log (WAL) Mechanism\n\nPostgreSQL uses WAL to ensure data durability and consistency. Before any changes are made to the database, they're first recorded in the WAL. This ensures that in the event of a system failure, the database can be restored.\n\n## Primary-Replica Replication\n\nIn primary-replica replication (also called master-slave), there's a single primary database that accepts all write queries. Changes are then replicated to one or more read-only replica databases.\n\nThe replication works through WAL streaming - the primary sends WAL records to replicas, which apply them to stay synchronized. PostgreSQL supports both:\n\n- **Asynchronous replication**: Primary doesn't wait for replica acknowledgment\n- **Synchronous replication**: Primary waits for replica to acknowledge receipt and application of changes before confirming the write to the client, ensuring the replica is always fully up-to-date\n\nKey configuration parameters on the primary include:\n- wal_level = logical (or replica)\n- max_replication_slots = 10\n- max_wal_senders = 10\n\n## Source Filenames\n\nThe search results came from these documents:\n\n1. database-replication.md - Contains information about primary-replica architecture, synchronous/asynchronous replication, and PostgreSQL configuration\n2. postgresql-internals.md - Covers the Write-Ahead Logging (WAL) system mechanics\n3. event-driven-architecture.md - Appeared in results but focused on eventual consistency patterns rather than PostgreSQL-specific WAL mechanics\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 20s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How do you implement event sourcing using Kafka topics as the append-only event store?",
      "difficulty": "medium",
      "search_calls": [
        {
          "query": "event sourcing Kafka topics append-only event store",
          "query_type": "hybrid",
          "latency_s": 0.133
        },
        {
          "query": "Kafka event store implementation append-only log partitions",
          "query_type": "hybrid",
          "latency_s": 0.116
        },
        {
          "query": "event sourcing pattern implementation aggregate events replay",
          "query_type": "simple",
          "latency_s": 0.75
        },
        {
          "query": "Kafka as event store topic partition key ordering",
          "query_type": "hybrid",
          "latency_s": 0.84
        }
      ],
      "sources_found": [
        "event-driven-architecture.md",
        "apache-kafka.md"
      ],
      "expected_sources": [
        "apache-kafka.md",
        "event-driven-architecture.md"
      ],
      "keywords_found": [
        "event sourcing",
        "topic",
        "partition"
      ],
      "expected_keywords": [
        "event sourcing",
        "topic",
        "partition"
      ],
      "decomposed": true,
      "source_coverage": 1.0,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.84,
      "search_latency_total_s": 1.839,
      "wall_time_s": 41.54,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about implementing event sourcing with Kafka topics as an event store.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"event sourcing Kafka topics append-only event store\",\n \u22ee    \"n_results\": 5,\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.133s\n\n> The search returned a relevant result but it's truncated. Let me search more specifically for Kafka event sourcing implementation details.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"Kafka event store implementation append-only log partitions\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.116s\n\n> Good, I found relevant documents. Let me search more specifically for event sourcing patterns and implementation.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"event sourcing pattern implementation aggregate events replay\",\n \u22ee    \"query_type\": \"simple\"\n \u22ee  }\n - Completed in 0.75s\n\n> Perfect! I found relevant content. Let me do one more search to see if there's specific information about using Kafka as the event store.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"Kafka as event store topic partition key ordering\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.84s\n\n> Based on the search results from Candlekeep, here's how to implement event sourcing using Kafka topics as the append-only event store:\n\n## Implementation Approach\n\nKafka as Event Store: Kafka topics serve as the append-only event store because:\n- Topics are divided into partitions, which are ordered, immutable sequences of records\n- Kafka functions as a distributed, partitioned, replicated commit log service\n- Records are appended to partitions and cannot be modified (append-only)\n\nKey Implementation Steps:\n\n1. Event Storage: Store domain events in Kafka topics where each topic represents an event stream for a specific aggregate or bounded context\n\n2. Partition Key Strategy: Use aggregate IDs as partition keys to ensure all events for a specific aggregate are stored in the same partition, maintaining ordering\n\n3. Event Replay: The event store provides a complete, chronological record allowing you to replay events to rebuild state or create new projections\n\n4. Auditability: Kafka's immutable log provides a full audit trail of all changes\n\n5. Time Travel: Replay events from any point in time by consuming from specific offsets\n\n## Source Files from Search Results\n\nThe information came from these documents in the Candlekeep knowledge base:\n\n1. event-driven-architecture.md - Contains details on event sourcing patterns, benefits (auditability, time travel, event replay), event stores, and implementation approaches\n\n2. apache-kafka.md - Covers Kafka architecture, topics, partitions, the commit log model, and how Kafka functions as an immutable, ordered log storage system\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 33s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How does Istio's Envoy sidecar proxy collect RED metrics and enable distributed tracing?",
      "difficulty": "medium",
      "search_calls": [
        {
          "query": "Istio Envoy sidecar proxy RED metrics distributed tracing",
          "query_type": "simple",
          "latency_s": 0.81
        },
        {
          "query": "RED metrics rate errors duration Envoy telemetry collection",
          "query_type": "simple",
          "latency_s": 0.78
        },
        {
          "query": "distributed tracing Jaeger Zipkin trace context propagation spans",
          "query_type": "simple",
          "latency_s": 0.96
        },
        {
          "query": "Istio observability telemetry Prometheus metrics tracing Jaeger Envoy filters",
          "query_type": "simple",
          "latency_s": 0.115
        }
      ],
      "sources_found": [
        "service-mesh.md",
        "monitoring-observability.md"
      ],
      "expected_sources": [
        "service-mesh.md",
        "monitoring-observability.md"
      ],
      "keywords_found": [
        "Envoy",
        "sidecar",
        "tracing"
      ],
      "expected_keywords": [
        "Envoy",
        "sidecar",
        "tracing"
      ],
      "decomposed": true,
      "source_coverage": 1.0,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.96,
      "search_latency_total_s": 2.665,
      "wall_time_s": 39.74,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about Istio's Envoy sidecar proxy and how it collects RED metrics and enables distributed tracing.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"Istio Envoy sidecar proxy RED metrics distributed tracing\",\n \u22ee    \"n_results\": 5\n \u22ee  }\n - Completed in 0.81s\n\n> The search returned results but they're truncated. Let me search more specifically for RED metrics and distributed tracing mechanisms:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"RED metrics rate errors duration Envoy telemetry collection\"\n \u22ee  }\n - Completed in 0.78s\n\n> Let me search more specifically for distributed tracing and how Envoy implements it:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"distributed tracing Jaeger Zipkin trace context propagation spans\"\n \u22ee  }\n - Completed in 0.96s\n\n> Let me try a more targeted search combining Istio observability with telemetry:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"Istio observability telemetry Prometheus metrics tracing Jaeger Envoy filters\"\n \u22ee  }\n - Completed in 0.115s\n\n> Based on the Candlekeep search results, here's what I found about how Istio's Envoy sidecar proxy collects RED metrics and enables distributed tracing:\n\n## RED Metrics Collection\n\nEnvoy collects a wide range of metrics including:\n- **Rate**: Request volume\n- **Errors**: Error rates  \n- **Duration**: Latency\n\nThese metrics are exported to monitoring systems like Prometheus. The service mesh architecture enables this by having Envoy intercept all traffic between services, giving it visibility into every request.\n\n## Distributed Tracing\n\nEnvoy enables distributed tracing by:\n1. Collecting traces as requests flow through the sidecar proxy\n2. Aggregating telemetry data (metrics, logs, and traces)\n3. Integrating with Istio's observability components which visualize the data\n\nThe service mesh collects telemetry data to provide visibility into the system's behavior across all services.\n\n## Source Files\n\nThe information came from these documents in the knowledge base:\n\n1. service-mesh.md - Primary source covering Istio observability, Envoy metrics collection, and telemetry aggregation\n2. monitoring-observability.md - Secondary source covering RED metrics framework and distributed tracing concepts\n\nThe search results show that service-mesh.md contains the most relevant technical details about how Envoy specifically implements these observability features within the Istio service mesh architecture.\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 31s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How do you handle backpressure in a Kafka-based streaming data pipeline with windowed aggregations?",
      "difficulty": "hard",
      "search_calls": [
        {
          "query": "Kafka backpressure streaming windowed aggregations",
          "query_type": "simple",
          "latency_s": 0.89
        },
        {
          "query": "backpressure handling techniques streaming pipeline",
          "query_type": "simple",
          "latency_s": 0.69
        }
      ],
      "sources_found": [
        "data-pipeline-design.md",
        "apache-kafka.md"
      ],
      "expected_sources": [
        "apache-kafka.md",
        "stream-processing.md",
        "data-pipeline-design.md"
      ],
      "keywords_found": [
        "backpressure",
        "window"
      ],
      "expected_keywords": [
        "backpressure",
        "window",
        "partition",
        "consumer"
      ],
      "decomposed": true,
      "source_coverage": 0.6667,
      "keyword_coverage": 0.5,
      "search_latency_s": 0.89,
      "search_latency_total_s": 1.58,
      "wall_time_s": 25.92,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about handling backpressure in Kafka-based streaming pipelines with windowed aggregations.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"Kafka backpressure streaming windowed aggregations\",\n \u22ee    \"n_results\": 5\n \u22ee  }\n - Completed in 0.89s\n\n> The search returned results, but the snippets are truncated. Let me search more specifically for backpressure handling techniques:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"backpressure handling techniques streaming pipeline\"\n \u22ee  }\n - Completed in 0.69s\n\n> Based on the search results from Candlekeep, the relevant information about handling backpressure in Kafka-based streaming data pipelines with windowed aggregations is found in these source files:\n\n## Source Files:\n\n1. data-pipeline-design.md - Primary source (appears in 4 out of 5 results)\n2. apache-kafka.md - Secondary source (appears in 3 out of 5 results from first search)\n\nThe data-pipeline-design.md document is described as \"A detailed technical reference on data pipeline design, batch vs streaming, ETL vs ELT, data quality, schema evolution, and backpressure handling\" - making it the most relevant source for your question.\n\nThe apache-kafka.md document covers \"Apache Kafka Architecture and Components\" including Kafka Streams, which is relevant for windowed aggregations in streaming pipelines.\n\nThe search results indicate that the backpressure handling techniques are documented in the streaming processing sections of these files, though the snippets are truncated. The documents cover real-time data ingestion, streaming processing characteristics, and the trade-offs between batch and streaming approaches.\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 18s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How does mutual TLS authentication work in an Istio service mesh running rootless containers?",
      "difficulty": "hard",
      "search_calls": [
        {
          "query": "mutual TLS authentication Istio service mesh rootless containers",
          "query_type": "hybrid",
          "latency_s": 0.75
        },
        {
          "query": "rootless containers security certificates mTLS",
          "query_type": "hybrid",
          "latency_s": 0.142
        },
        {
          "query": "Istio mTLS PeerAuthentication certificate management sidecar",
          "query_type": "simple",
          "latency_s": 0.68
        }
      ],
      "sources_found": [
        "service-mesh.md"
      ],
      "expected_sources": [
        "tls-ssl.md",
        "service-mesh.md",
        "container-security.md"
      ],
      "keywords_found": [
        "mutual TLS",
        "mTLS",
        "Istio",
        "rootless"
      ],
      "expected_keywords": [
        "mutual TLS",
        "mTLS",
        "Istio",
        "rootless"
      ],
      "decomposed": true,
      "source_coverage": 0.3333,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.75,
      "search_latency_total_s": 1.572,
      "wall_time_s": 32.73,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about mutual TLS authentication in Istio service mesh with rootless containers.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"mutual TLS authentication Istio service mesh rootless containers\",\n \u22ee    \"n_results\": 5,\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.75s\n\n> The search found relevant information about mTLS in Istio, but the results are truncated. Let me search for more complete information about rootless containers specifically.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"rootless containers security certificates mTLS\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.142s\n\n> Let me try a more targeted search focusing on Istio mTLS implementation details.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"Istio mTLS PeerAuthentication certificate management sidecar\",\n \u22ee    \"query_type\": \"simple\"\n \u22ee  }\n - Completed in 0.68s\n\n> Based on the search results from Candlekeep, here's what I found about mutual TLS authentication in Istio service mesh:\n\n## How mTLS Works in Istio\n\nIstio provides mutual TLS (mTLS) authentication where both the client and server authenticate each other, providing end-to-end encryption and identity-based authorization. The Envoy sidecar proxy handles this communication security between services.\n\nKey aspects:\n- **Sidecar Proxy**: Envoy proxies deployed alongside each service manage the mTLS connections\n- **Mutual Authentication**: Both client and server verify each other's identities\n- **End-to-End Encryption**: All service-to-service traffic is encrypted\n- **Identity-Based Authorization**: Services are authenticated based on their identities within the mesh\n\nThe search results don't contain specific information about rootless containers in the context of Istio mTLS. The knowledge base appears to cover general Istio mTLS concepts but doesn't address the rootless container scenario specifically.\n\n## Source Files\n\nAll results came from a single source file:\n- **service-mesh.md** - \"Service Mesh Architecture and Istio\" (technical guide covering service mesh architecture, Istio, Envoy, sidecar proxy, traffic management, mTLS, and observability)\n\nThe knowledge base doesn't appear to have documentation specifically addressing how mTLS certificate management works with rootless containers in Istio.\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 24s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How do you prevent SQL injection in a serverless function API that retrieves database credentials from Vault and authenticates users with OAuth 2.0?",
      "difficulty": "hard",
      "search_calls": [
        {
          "query": "SQL injection prevention serverless function Vault OAuth 2.0 database credentials authentication",
          "query_type": "simple",
          "latency_s": 0.145
        },
        {
          "query": "SQL injection parameterized queries prepared statements input validation database security",
          "query_type": "simple",
          "latency_s": 0.122
        },
        {
          "query": "serverless function Vault secrets management database credentials secure storage",
          "query_type": "simple",
          "latency_s": 0.86
        }
      ],
      "sources_found": [
        "authentication.md",
        "api-security.md",
        "security-audit-checklist.md",
        "secrets-management.md",
        "sql-query-optimization.md"
      ],
      "expected_sources": [
        "owasp-top-10.md",
        "serverless-architecture.md",
        "secrets-management.md",
        "api-security.md"
      ],
      "keywords_found": [
        "injection",
        "serverless",
        "Vault",
        "OAuth"
      ],
      "expected_keywords": [
        "injection",
        "serverless",
        "Vault",
        "OAuth"
      ],
      "decomposed": true,
      "source_coverage": 0.5,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.86,
      "search_latency_total_s": 1.127,
      "wall_time_s": 35.88,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about SQL injection prevention in serverless functions with Vault and OAuth 2.0.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"SQL injection prevention serverless function Vault OAuth 2.0 database credentials authentication\",\n \u22ee    \"n_results\": 5\n \u22ee  }\n - Completed in 0.145s\n\n> Let me search more specifically for SQL injection prevention techniques and parameterized queries:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"SQL injection parameterized queries prepared statements input validation database security\"\n \u22ee  }\n - Completed in 0.122s\n\n> Let me search for serverless and Vault-specific security patterns:\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"serverless function Vault secrets management database credentials secure storage\"\n \u22ee  }\n - Completed in 0.86s\n\n> Based on the Candlekeep search results, here's how to prevent SQL injection in a serverless function API that retrieves database credentials from Vault and authenticates users with OAuth 2.0:\n\n## SQL Injection Prevention\n\nUse parameterized queries/prepared statements - Never concatenate user input directly into SQL queries. Use your database library's parameterized query functionality to separate SQL logic from data.\n\nInput validation and sanitization - Validate all input parameters, query strings, and request payloads before processing. Sanitize and encode all user-supplied data.\n\nPrinciple of least privilege - Grant database credentials retrieved from Vault only the minimum permissions needed for the operation.\n\n## Architecture Pattern\n\n1. OAuth 2.0 authentication - Validate the OAuth token before processing any request\n2. Retrieve credentials from Vault - Fetch database credentials dynamically from Vault using the serverless function's identity\n3. Use parameterized queries - Execute database operations using prepared statements with the retrieved credentials\n4. Input validation - Validate all user input before using it in queries\n\n## Source Files from Search Results\n\n1. authentication.md - Covers API security, SQL injection prevention, and authentication patterns\n2. api-security.md - OAuth 2.0 flows, token introspection, and API security best practices\n3. security-audit-checklist.md - Input validation, sanitization, and injection attack prevention\n4. secrets-management.md - Vault and AWS Secrets Manager for database credential management\n5. sql-query-optimization.md - SQL query patterns (though focused on optimization rather than security)\n\nThe key takeaway: parameterized queries are your primary defense against SQL injection, regardless of how you authenticate users or retrieve credentials.\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 26s\n\n",
      "agent_error": null
    },
    {
      "original_query": "How do you maintain exactly-once semantics when using Kafka event sourcing with the saga pattern across replicated databases using optimistic concurrency control?",
      "difficulty": "hard",
      "search_calls": [
        {
          "query": "Kafka event sourcing saga pattern exactly-once semantics replicated databases optimistic concurrency control",
          "query_type": "hybrid",
          "latency_s": 0.114
        },
        {
          "query": "saga pattern exactly-once semantics idempotency distributed transactions",
          "query_type": "hybrid",
          "latency_s": 0.116
        },
        {
          "query": "Kafka exactly-once processing idempotent producer transactional outbox",
          "query_type": "hybrid",
          "latency_s": 0.11
        },
        {
          "query": "optimistic concurrency control version conflict resolution database replication",
          "query_type": "hybrid",
          "latency_s": 0.104
        }
      ],
      "sources_found": [
        "event-driven-architecture.md",
        "apache-kafka.md",
        "database-replication.md"
      ],
      "expected_sources": [
        "apache-kafka.md",
        "event-driven-architecture.md",
        "database-replication.md",
        "concurrency-patterns.md"
      ],
      "keywords_found": [
        "exactly-once",
        "saga",
        "replication",
        "concurrency"
      ],
      "expected_keywords": [
        "exactly-once",
        "saga",
        "replication",
        "concurrency"
      ],
      "decomposed": true,
      "source_coverage": 0.75,
      "keyword_coverage": 1.0,
      "search_latency_s": 0.116,
      "search_latency_total_s": 0.444,
      "wall_time_s": 38.41,
      "raw_output": "> I'll search the Candlekeep knowledge base for information about Kafka event sourcing with saga patterns and exactly-once semantics.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"query\": \"Kafka event sourcing saga pattern exactly-once semantics replicated databases optimistic concurrency control\",\n \u22ee    \"n_results\": 5,\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.114s\n\n> The search returned a low relevance score (0.031), which suggests the knowledge base may not have detailed information on this specific combination of topics. Let me try a more focused search on the saga pattern and exactly-once semantics.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"saga pattern exactly-once semantics idempotency distributed transactions\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.116s\n\n> The search results show the knowledge base contains a document on event-driven architecture that covers saga patterns, but the relevance scores remain low (0.030-0.033). Let me try searching for Kafka-specific content.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"Kafka exactly-once processing idempotent producer transactional outbox\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.110s\n\n> Good, I found a Kafka-specific document. Let me search for optimistic concurrency control and database replication patterns.\nRunning tool search with the param (from mcp server: candlekeep)\n \u22ee  {\n \u22ee    \"n_results\": 5,\n \u22ee    \"query\": \"optimistic concurrency control version conflict resolution database replication\",\n \u22ee    \"query_type\": \"hybrid\"\n \u22ee  }\n - Completed in 0.104s\n\n> Based on the search results, the Candlekeep knowledge base contains relevant documents but doesn't appear to have a comprehensive answer to your specific question about combining all these patterns. The relevance scores are consistently low (0.030-0.033), indicating the documents don't directly address the complete scenario you're asking about.\n\n## Source Files Found\n\nFrom the search results, I found these relevant documents:\n\n1. event-driven-architecture.md - Contains information about saga patterns, eventual consistency, and idempotency\n2. apache-kafka.md - Contains information about Kafka architecture, exactly-once semantics, and Kafka Streams\n3. database-replication.md - Contains information about database replication and conflict resolution\n\n## What the Knowledge Base Lacks\n\nThe knowledge base doesn't appear to have a document that specifically addresses the integration of:\n- Kafka event sourcing\n- Saga pattern implementation\n- Exactly-once semantics\n- Replicated databases with optimistic concurrency control\n\nThis is a highly specialized distributed systems question that would require combining concepts from multiple domains. The low relevance scores suggest the existing documents cover these topics separately but don't provide guidance on implementing them together.\n\nWould you like me to search for more specific subtopics, or would you prefer a general answer based on distributed systems principles (though it wouldn't be sourced from Candlekeep)?\nError: user defined default q_cli_default not found. Falling back to in-memory default\nAll tools are now trusted (!). Kiro will execute tools without asking for confirmation.\nAgents can sometimes do unexpected things so understand the risks.\n\nLearn more at https://kiro.dev/docs/cli/chat/security/#using-tools-trust-all-safely\n\n\n\n\n\n \u25b8 Time: 30s\n\n",
      "agent_error": null
    }
  ]
}