# Epic 8: Documentation & Release

**Goal**: Create comprehensive documentation covering quick start, API reference, architecture, examples, and contribution guidelines to enable community adoption and establish RabbitMQ MCP Server as the standard tool.

**Value**: Reduces onboarding friction (<5 min first operation, <30 min proficiency), enables community contributions, establishes credibility as production-ready tool, and positions project for **first-mover ecosystem leadership**.

**Product Differentiator**: Documentation quality signals production-readiness - comprehensive API reference, tutorials, security guide, and architecture docs establish this as the reference implementation for RabbitMQ MCP integration.

**Covered FRs**: Supports all FRs indirectly through documentation. Specific deliverables: README (FR-001 to FR-023 overview), API Reference (FR-001 to FR-013 operations), Security Docs (FR-014, FR-017, FR-020), Performance Guide (FR-002, FR-004, FR-016)

---

## Story 8.1: README with Quick Start

As a new user,
I want a clear README with quick start instructions,
So that I can install and execute my first operation within 5 minutes.

**Acceptance Criteria:**

**Given** a developer discovering the project
**When** they read the README
**Then** README includes: project description, key features, badges (build status, coverage, version), quick start (install + first operation in <5 commands), prerequisites (Python 3.12+, RabbitMQ instance)

**And** quick start uses uvx for instant execution: `uvx rabbitmq-mcp-server queue list`

**And** README shows: MCP server usage (Claude Desktop config), CLI usage (standalone commands), configuration options (env vars, config file)

**And** README includes: links to full documentation, API reference, examples, contributing guide, license

**And** README has: table of contents, clear sections, code examples with syntax highlighting, screenshots/terminal recordings

**And** badges show: ![Build Status](https://github.com/.../actions/.../badge.svg), ![Coverage](https://codecov.io/.../badge.svg), ![PyPI version](https://badge.fury.io/py/rabbitmq-mcp-server.svg)

**And** README is beginner-friendly: assumes no RabbitMQ MCP knowledge, explains concepts clearly

**Prerequisites:** All MVP features complete (Epics 1-7)

**Technical Notes:**
- README.md at repository root
- Quick start section: installation (uvx or pip), configuration (minimal env vars), first operation (list queues)
- Code blocks with language tags: ```bash, ```python, ```json
- Terminal recordings: use asciinema for animated CLI examples
- Badges from: GitHub Actions (CI status), Codecov (coverage), PyPI (version), License (MIT/LGPL)
- Links to docs: ./docs/API.md, ./docs/ARCHITECTURE.md, ./docs/EXAMPLES.md
- Keep README concise: <500 lines, detailed docs in separate files

---

## Story 8.2: API Reference Documentation

As a developer,
I want complete API reference documenting all operations, parameters, and responses,
So that I understand how to use each operation without trial-and-error.

**Acceptance Criteria:**

**Given** the API reference documentation
**When** I look up an operation
**Then** each operation is documented with: operation ID, description, HTTP method + path (for HTTP ops) or AMQP details, parameters (name, type, required, description, default, constraints), request body schema (if applicable), response schema, examples (request + response), error codes and meanings

**And** operations organized by category: MCP Tools (search-ids, get-id, call-id), Queue Operations, Exchange Operations, Binding Operations, Message Operations, Connection Operations

**And** parameter details include: location (path/query/header/body), validation rules (regex, min/max, enum values), default values

**And** examples show: minimal usage (required params only), advanced usage (all params), error cases (validation failure, not found)

**And** API reference is generated from: operation registry (operations.json), Pydantic schemas (generated_schemas.py), AMQP schemas (amqp_schemas.py)

**And** API reference is available as: Markdown (docs/API.md), HTML (generated site), OpenAPI spec (for HTTP operations)

**And** API reference includes: authentication requirements, rate limits, best practices

**Prerequisites:** Story 1.4 (operation registry), Story 1.3 (Pydantic schemas), Story 4.8 (AMQP schemas)

**Technical Notes:**
- Generate API reference: python scripts/generate_api_docs.py (reads operations.json + schemas)
- Markdown template: operation heading, description, parameters table, request/response examples, error codes
- HTML generation: use mkdocs or sphinx for searchable documentation site
- OpenAPI spec: export HTTP operations to openapi.yaml for Swagger UI
- Examples: use realistic data (not foo/bar), show common use cases
- Error documentation: list all error codes (-32700, -32600, etc.) with explanations
- Best practices: parameter validation, error handling, rate limiting, security
- Versioning: document API version, breaking changes in CHANGELOG.md

---

## Story 8.3: Architecture Documentation

As a technical decision-maker,
I want architecture documentation explaining design decisions, system components, and data flow,
So that I understand how the system works and can evaluate it for my use case.

**Acceptance Criteria:**

**Given** the architecture documentation
**When** I read it
**Then** documentation includes: system overview diagram (components + interactions), OpenAPI-driven generation pipeline (source → schemas → embeddings → operations → runtime), MCP protocol integration (stdio transport, JSON-RPC, 3-tool pattern), RabbitMQ connectivity (AMQP + HTTP Management API, connection pooling, reconnection), data flow diagrams (MCP request → semantic search → operation execution → RabbitMQ → response)

**And** design decisions documented: why 3-tool pattern (vs 100+ individual tools), why build-time generation (vs runtime), why OpenAPI as source of truth, why structlog for logging, why sentence-transformers for semantic search

**And** component details: MCP server, semantic search engine, operation registry, HTTP client, AMQP client, logging system

**And** diagrams use: C4 model (context, container, component), sequence diagrams (operation execution flow), architecture decision records (ADRs)

**And** architecture document is: ./docs/ARCHITECTURE.md (Markdown), with embedded diagrams (Mermaid or PNG)

**And** security architecture covered: credential sanitization, TLS/SSL, audit trails, rate limiting

**And** scalability architecture covered: connection pooling, async operations, caching, performance characteristics

**Prerequisites:** All implementation complete (Epics 1-7)

**Technical Notes:**
- Use Mermaid for diagrams (renders in GitHub): ```mermaid flowchart TD; ...```
- System overview: show MCP clients → MCP server → RabbitMQ
- Pipeline diagram: OpenAPI → generate_schemas.py → Pydantic models, etc.
- Sequence diagram: AI assistant query → search-ids → semantic search → ranked results
- ADRs: template (Context, Decision, Consequences), examples (ADR-001: 3-tool pattern, ADR-002: build-time generation)
- Reference architecture: position as pattern for API-to-MCP transformations
- Document: thread model (async/await), error handling patterns, retry logic

---

## Story 8.4: Usage Examples & Tutorials

As a new user,
I want practical examples and tutorials for common use cases,
So that I can learn by example and quickly accomplish my goals.

**Acceptance Criteria:**

**Given** the examples documentation
**When** I follow a tutorial
**Then** examples cover: basic queue management (list, create, delete), message publishing (send test message), message consumption (receive and acknowledge), binding configuration (setup routing), health monitoring (check connection), troubleshooting (find stuck queues, purge queue)

**And** each example includes: scenario description, prerequisites (RabbitMQ setup), step-by-step instructions with commands, expected output (terminal screenshots or code blocks), explanation (what happened, why)

**And** tutorials are progressive: beginner (basic operations), intermediate (routing patterns, error handling), advanced (performance tuning, observability)

**And** examples use realistic scenarios: "Debugging stuck messages in production", "Setting up topic-based routing", "Monitoring queue health during incident"

**And** examples cover both: MCP server usage (via Claude Desktop), CLI usage (standalone commands)

**And** examples are tested: CI/CD runs example commands, verifies output

**And** examples document is: ./docs/EXAMPLES.md (Markdown), with code snippets + terminal output

**Prerequisites:** All implementation complete (Epics 1-7)

**Technical Notes:**
- Example structure: scenario → setup → commands → output → explanation
- Code snippets: use fenced code blocks with language tags (```bash, ```json)
- Terminal output: show actual output (copy-paste from terminal)
- Scenario examples:
  - "List all queues and find those with no consumers"
  - "Publish test message to exchange and verify delivery"
  - "Create topic exchange with wildcard bindings"
  - "Consume messages with manual acknowledgment"
  - "Purge stuck queue during incident response"
- MCP examples: show Claude Desktop config, sample AI assistant conversations
- CLI examples: show command syntax, options, output formats
- Test examples: pytest tests that execute example commands, validate output

---

## Story 8.5: Contributing Guide

As a potential contributor,
I want a contributing guide explaining how to contribute code, documentation, or issues,
So that I can participate in the project effectively.

**Acceptance Criteria:**

**Given** the contributing guide
**When** I read it
**Then** guide includes: how to report bugs (issue template, required info), how to request features (feature template, use case description), how to contribute code (fork → branch → PR workflow), development setup (clone, install dependencies, run tests), code standards (black, isort, mypy, ruff), testing requirements (write tests, 80%+ coverage), PR guidelines (description, tests, changelog)

**And** development setup is documented: `git clone`, `uv sync`, `pytest`, pre-commit hooks

**And** code standards are automated: pre-commit runs black/isort/mypy/ruff, CI validates

**And** PR template provided: checklist (tests added, docs updated, changelog updated, code formatted)

**And** issue templates provided: bug report (expected vs actual, steps to reproduce, environment), feature request (use case, proposed solution, alternatives)

**And** contributing guide is: ./CONTRIBUTING.md (Markdown), linked from README

**And** guide welcomes all contributions: code, docs, examples, bug reports, feature requests, discussions

**Prerequisites:** Project repository setup complete

**Technical Notes:**
- CONTRIBUTING.md at repository root
- GitHub templates: .github/ISSUE_TEMPLATE/bug_report.md, feature_request.md, .github/PULL_REQUEST_TEMPLATE.md
- Development workflow: fork → clone → create branch → make changes → run tests → commit → push → open PR
- Code standards: black (line length 100), isort (profile black), mypy (strict mode), ruff (all rules)
- Pre-commit hooks: configured in .pre-commit-config.yaml, install via `pre-commit install`
- Testing: pytest, coverage ≥80%, integration tests require Docker
- Documentation: update docs when adding features, examples for new operations
- Changelog: update CHANGELOG.md with changes (format: [Added], [Changed], [Fixed], [Removed])

---

## Story 8.6: Changelog & Release Notes

As a user,
I want a changelog documenting all changes across versions,
So that I understand what's new, what's fixed, and what breaking changes exist.

**Acceptance Criteria:**

**Given** the changelog
**When** a new version is released
**Then** changelog includes: version number, release date, sections ([Added], [Changed], [Deprecated], [Removed], [Fixed], [Security]), bullet points for each change, links to issues/PRs

**And** changelog follows Keep a Changelog format: https://keepachangelog.com/

**And** versions follow Semantic Versioning: MAJOR.MINOR.PATCH (1.0.0, 1.1.0, 1.1.1)

**And** breaking changes are clearly marked: **BREAKING:** prefix

**And** changelog is maintained: ./CHANGELOG.md (Markdown), updated with every PR

**And** release notes are generated from changelog: GitHub Releases use changelog excerpt

**And** unreleased changes tracked: [Unreleased] section at top, moved to version section on release

**And** changelog is user-focused: describe impact, not implementation details

**Prerequisites:** Project repository setup complete

**Technical Notes:**
- CHANGELOG.md format:
  ```markdown
  # Changelog
  
  ## [Unreleased]
  ### Added
  - New feature X for use case Y
  
  ## [1.0.0] - 2025-11-20
  ### Added
  - Initial release
  - 3-tool MCP pattern: search-ids, get-id, call-id
  - Complete RabbitMQ topology operations
  ...
  ```
- Semantic versioning: MAJOR (breaking changes), MINOR (new features, backward compatible), PATCH (bug fixes)
- Breaking changes: API changes, config format changes, removed features
- Security section: CVE fixes, security improvements (high priority)
- Automated: consider semantic-release for auto-generating releases from commit messages
- GitHub Releases: tag version, use changelog excerpt, attach artifacts (wheels)

---

## Story 8.7: Security & Compliance Documentation

As a security officer,
I want security documentation explaining security features, compliance support, and best practices,
So that I can evaluate the tool for enterprise use and ensure it meets our security requirements.

**Acceptance Criteria:**

**Given** the security documentation
**When** I evaluate security posture
**Then** documentation includes: authentication (username/password, TLS/SSL), credential protection (automatic sanitization, no plaintext storage, secure defaults), audit trails (all operations logged, correlation IDs, 30-day retention), compliance support (GDPR, SOC 2, ISO 27001 audit logging), secure defaults (TLS enabled, certificate verification, secure file permissions), security best practices (credential management, TLS configuration, log security, rate limiting)

**And** threat model documented: attack vectors (credential leakage, unauthorized access, DOS), mitigations (sanitization, authentication, rate limiting)

**And** security testing covered: automated credential detection, penetration testing (future), security scanning (bandit)

**And** vulnerability reporting process: security@example.com, responsible disclosure, CVE assignment

**And** security documentation is: ./docs/SECURITY.md (Markdown), linked from README

**And** security policy for vulnerabilities: ./SECURITY.md (GitHub Security tab)

**Prerequisites:** Story 7.3 (sensitive data sanitization), Story 7.6 (audit trail), Story 7.9 (security logging)

**Technical Notes:**
- SECURITY.md sections: Authentication, Credential Protection, Audit Trails, Compliance, Secure Defaults, Best Practices, Reporting Vulnerabilities
- Threat model: identify assets (RabbitMQ credentials, message data), threats (credential theft, unauthorized ops, DOS), mitigations
- Automated security: bandit scan in CI/CD, credential leak detection tests
- Compliance: document which features support GDPR (audit logs), SOC 2 (access control), ISO 27001 (security logging)
- Best practices: rotate credentials regularly, use TLS in production, restrict log file access, monitor security logs
- Vulnerability reporting: email address, PGP key, expected response time, disclosure policy
- GitHub Security: enable security tab, set up security policy, enable Dependabot alerts

---

## Story 8.8: Performance & Tuning Guide

As a DevOps engineer,
I want performance tuning documentation,
So that I can optimize the MCP server for my workload and troubleshoot performance issues.

**Acceptance Criteria:**

**Given** the performance documentation
**When** I need to tune performance
**Then** documentation includes: performance characteristics (latency targets, throughput limits), bottlenecks (network latency, RabbitMQ server limits, Python GIL), tuning parameters (connection pool size, prefetch count, log buffer size, rate limits), hardware recommendations (CPU, memory, network for different scales), benchmarks (reference hardware, test results, comparison to baseline)

**And** tuning guide covers: connection pooling (when to increase pool size), semantic search (caching, embeddings optimization), logging (async logging, buffer sizing), RabbitMQ configuration (connection limits, prefetch, heartbeat)

**And** troubleshooting section: slow operations (diagnose latency), connection issues (reconnection failures), high memory usage (logging buffers, connection leaks)

**And** monitoring guidance: metrics to track (operation latency, error rate, connection pool usage), dashboards (Grafana examples), alerts (high latency, high error rate)

**And** performance documentation is: ./docs/PERFORMANCE.md (Markdown)

**Prerequisites:** Story 6.6 (performance tests), Story 7.7 (observability instrumentation)

**Technical Notes:**
- Performance targets from requirements: search <100ms (p95), operations <200ms (p95), logging <5ms overhead
- Bottlenecks: network RTT to RabbitMQ (measure with ping), RabbitMQ server CPU/memory, Python async overhead
- Tuning: MAX_HTTP_CONNECTIONS (default 5, increase for high concurrency), PREFETCH_COUNT (default 10, increase for throughput), LOG_BUFFER_SIZE (default 1000, increase if high log volume)
- Hardware: 4-core CPU minimum, 8GB RAM (16GB for high volume), SSD for logs, low-latency network (<10ms RTT to RabbitMQ)
- Benchmarks: document test environment, show results (ops/sec, latency percentiles), compare versions
- Monitoring: track operation_duration_ms (p50/p95/p99), error_rate (errors/minute), connection_pool_usage (active/total)
- Troubleshooting: enable DEBUG logging, use correlation IDs for tracing, check RabbitMQ server metrics

---

## Story 8.9: License & Legal Documentation

As a project maintainer,
I want proper licensing and legal documentation,
So that users understand usage rights and the project meets open source standards.

**Acceptance Criteria:**

**Given** the project repository
**When** users evaluate licensing
**Then** LICENSE file includes: LGPL 3.0 license text (or MIT, depending on choice), copyright notice, license summary

**And** README includes: license badge, link to LICENSE file, short license description

**And** all source files include: license header comment (SPDX-License-Identifier: LGPL-3.0-or-later)

**And** third-party licenses documented: NOTICE file lists dependencies and their licenses, compatible with project license

**And** contributor license agreement (CLA) if required: defines contribution terms, copyright assignment

**And** licensing documentation is: ./LICENSE (license text), ./NOTICE (third-party licenses), headers in source files

**And** license choice rationale: LGPL for enterprise-friendly open source, MIT for maximum permissiveness

**Prerequisites:** Project repository setup complete

**Technical Notes:**
- License choice: LGPL 3.0 (allows proprietary linking, requires source disclosure for modifications) or MIT (most permissive, no copyleft)
- LICENSE file: full license text from https://www.gnu.org/licenses/lgpl-3.0.txt or https://opensource.org/licenses/MIT
- SPDX headers: # SPDX-License-Identifier: LGPL-3.0-or-later (standardized format)
- Add headers: script (scripts/add_license_headers.py) adds headers to all .py files
- NOTICE file: lists dependencies (pydantic, httpx, pika, etc.) with their licenses, any attributions required
- Check compatibility: all dependencies must have compatible licenses (MIT, Apache 2.0, BSD compatible with LGPL)
- CLA: optional, GitHub DCO (Developer Certificate of Origin) as alternative
- License badge: ![License](https://img.shields.io/badge/license-LGPL--3.0-blue.svg)

---

## Story 8.10: Release Preparation & Publishing

As a project maintainer,
I want automated release process for publishing to PyPI,
So that users can install the package easily via pip/uvx and new versions are distributed reliably.

**Acceptance Criteria:**

**Given** a new version ready for release
**When** release process executes
**Then** version is tagged in Git: v1.0.0 (semantic versioning)

**And** changelog is updated: version section created from [Unreleased]

**And** package is built: source distribution (.tar.gz), wheel (.whl)

**And** package is published to PyPI: https://pypi.org/project/rabbitmq-mcp-server/

**And** GitHub Release created: release notes from changelog, artifacts attached

**And** documentation is deployed: GitHub Pages or Read the Docs

**And** release announcement: README updated, blog post (optional), social media (optional)

**And** users can install: `pip install rabbitmq-mcp-server` or `uvx rabbitmq-mcp-server`

**And** release process is automated: GitHub Actions workflow on tag push

**Prerequisites:** All MVP features complete, documentation complete, tests passing, security validated

**Technical Notes:**
- Version bump: update pyproject.toml, src/__init__.py __version__, tag: git tag v1.0.0
- Build package: `uv build` or `python -m build` → generates dist/rabbitmq_mcp_server-1.0.0.tar.gz and .whl
- Publish to PyPI: `uv publish` or `twine upload dist/*` (requires PyPI API token)
- GitHub Actions workflow:
  ```yaml
  name: Release
  on:
    push:
      tags: ['v*']
  jobs:
    release:
      runs-on: ubuntu-latest
      steps:
        - checkout
        - setup python
        - install dependencies
        - run tests
        - build package
        - publish to PyPI (on: push tags)
        - create GitHub Release (release notes from tag)
  ```
- GitHub Release: use gh CLI or GitHub API, attach dist/*.tar.gz and *.whl
- Documentation: deploy to GitHub Pages (gh-pages branch) or Read the Docs (readthedocs.org)
- Announcement: update README with latest version badge, post to PyPI classifiers, announce in communities

---
