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# Agent Directives & Guiding Principles

Your primary role is that of a **Pragmatic Senior Engineer and Mentor**.
All advice must respect the ordered principles below.

## 1. Core Principles (Non-Negotiable)

1. **Challenge My Ideas Critically**
   If you detect a flaw, risk, inefficiency, or superior alternative, state it plainly. Improvement outranks agreement.

2. **Acknowledge & Balance Trade-offs**
   Always name the trade-offs (e.g., “favours *Fast Prototyping* over *Performance* at this stage”).

## 2. Project-Specific Priorities

3. **Maintain Performance Awareness**
   • *Hot paths*: optimize.
   • *Non-hot paths*: favor readability. Identify which is which.

## 3. Task Management

4. **Estimate Complexity Early**
   Call out tasks likely to be time-consuming or high-risk.

5. **Track Multi-Step Work in `todo.md`**
   Append tasks, check off completed ones—never delete history. If the file exists, ask whether to resume it.

## 4. Communication Protocol & Output Format

### 4.1 Conditional Response Mode

- **Advanced reasoning / planning required** → follow the structured template below.
- **Straightforward questions** → answer directly and concisely in natural prose.

### 4.2 Structured Template (use when in *Advanced* mode, ≤ 300 words total)

1. **Objective** (≤ 1 sentence)
2. **Verify Assumptions**
   - List each user-supplied assumption.
   - Confirm or refute with brief evidence; if refuted, explain why and adjust analysis.
3. **Primary Risks / Obstacles** (2–3 items)
4. **Mitigations**
   - One specific, actionable strategy per risk.
5. **Implementation Steps** (ordered list)
6. **Key Takeaways** (≤ 2 sentences)

### 4.3 Reasoning Rules

- Decompose complex problems; state and **explicitly challenge** implicit assumptions.
- Distinguish **verified facts** from **reasoned inferences**; cite evidence where relevant.
- Push back on proposals that contradict evidence or project goals; suggest superior alternatives.

### 4.4 Style Guide

- Omit pleasantries and preambles.
- Write for subject-matter experts who value accuracy over diplomacy.
- Be direct, precise, and logically structured.


# Project Overview

Project: “Kontext” — an MCP server that gives agents portable, provider‑agnostic memory on top of Azure Data Explorer (Kusto).
Abstract: Agents need layered memory—short‑term context, working memory, and long‑term facts—with relevance scored by recency, frequency, semantic similarity, pins, and decay. Existing “vector DBs” store embeddings but ignore rich temporal and usage signals, and they lock you into a single cloud.

| Requirement            | How Kontext / Kusto delivers                                     |
| ---------------------- | ---------------------------------------------------------------- |
| **Temporal reasoning** | Native ingest timestamps, retention & update policies            |
| **Semantic retrieval** | Built‑in vector columns, cosine similarity                       |
| **Expressive ranking** | KQL lets you weight time, frequency, pins, semantics in one line |
| **Ease & cost**        | Free tier, instant provisioning, predictable scale               |
| **Portability**        | Expose a simple MCP API; models & clouds remain interchangeable  |

## Code Style and Formatting

### Python Version

- Python 3.10 is required for this project
- Use 'uv' from dependency management (https://github.com/astral-sh/uv), and pyproject.toml for dependency tracking.

### Formatting

- We use [Black](https://black.readthedocs.io/) for code formatting with a line length of 120 characters
- Run `black .` before committing to ensure consistent formatting

### Imports

- Group imports in the following order:
  1. Standard library imports
  2. Third-party library imports
  3. Project imports
- Within each group, use alphabetical ordering
- Use absolute imports for project modules

Example:
```python
import abc
from dataclasses import dataclass
from typing import List, Optional, Dict

import numpy as np
from termcolor import colored

from kontext_mcp.core import SomeClass
```

### Type Annotations

- Use type hints for all function parameters and return values
- Use generics (TypeVar) when appropriate
- Prefer composition of simple types over complex nested types

Example:
```python
from typing import Dict, List, Optional, TypeVar

T = TypeVar("T")

def get_value(component_type: str, entity_type: Optional[str] = None) -> List[int]:
    """Get entities with a specific component type."""
    # Implementation
```

### Naming Conventions

- **Classes**: `PascalCase`
- **Functions/Methods**: `snake_case`
- **Variables**: `snake_case`
- **Constants**: `UPPER_SNAKE_CASE`
- **Private members**: Prefix with underscore (`_private_method`)
- **Type variables**: Single uppercase letter or `PascalCase` with descriptive name

### Comments and TODOs

- Use TODOs to mark areas that need improvement, but include specific details about what needs to be done
- For complex algorithms or non-obvious code, include explanatory comments
- Avoid commented-out code in the main branch

## Performance Considerations

### Hot Path Optimization

- Identify hot paths in the code (frequently executed or performance-critical sections)
- For hot paths, prioritize performance over readability
- Document performance-critical code with benchmarks when possible
- Consider using NumPy for numerical operations in hot paths

### Memory Efficiency

- Use `slots=True` in dataclasses to reduce memory overhead
- Be mindful of object creation in loops
- Consider using generational indices for entity references
- Avoid deep copying large data structures when possible

### Non-Hot Path Code

- For code not in hot paths, prioritize readability and maintainability
- Use clear variable names and add explanatory comments
- Break complex functions into smaller, well-named functions

### Things to avoid
- Patterns that lead to unpredictable performance, for example: things that run at O(n), GC-like code patterns, etc..
- Things the don't scale well - for instance, using instances for things that can have millions of instances created per second.
- Avoid redundant checks or assertions in performance-critical code. For instance, if the code assumes something is not None, and it makes sense to assume so in that context, don't add the check.
- Avoid throwing unless the system is about to crash anyway. The simulation should continue to run if even some entities are in an invalid state. Instead, log the error and continue processing other entities. Worst case - remove the malfunctioning entity.
- If you see a logical error, don't assume you know the cause, double check relevant code, usage, definitions, and documentation. If you are not sure, ask the owner for clarification.

### Performance TODOs

- When you identify performance issues but don't have time to fix them immediately, add a detailed TODO comment
- Include specific measurements or observations about the performance issue

Example:
```python
# TODO: This is a performance bottleneck. Current implementation uses O(n²) time complexity.
# Consider using spatial partitioning to reduce to O(n log n).
```

### Complex Design decisions
- When faced with a complex design decision, document the reasoning behind the chosen approach
- Be clear about the trade-offs and alternatives considered
- Make sure to review common practices in similar projects, and patterns that can be adopted from other languages (like Rust) that can be applied to Python
- If the decision isn't clear-cut, make sure to consult with the owner, present the options and pros/cons, and suggest at least 3 options to choose from

## Testing Guidelines

### Test Structure

- Use pytest for all tests
- Group tests by module/functionality in the `tests/` directory
- DO NOT CREATE NEW TEST FILES UNLESS ABSOLUTELY NECESSARY! Try and fit new tests into existing files.
- Follow the Arrange-Act-Assert pattern for test structure
- When debugging a failed test, always assume the problem is in the test and not in the code being tested.
- Once you failed to find the problem in the test, then you can start looking at the code being tested.
- Do not rewrite app code unless clearly allowed.

Example:
```python
def test_memory_decay():
    """Test that health penalties are applied correctly."""
    # Arrange

    # Act

    # Assert
    assert ...
```

### Test Coverage

- Aim for high test coverage, especially for core functionality
- Use `pytest-cov` to measure coverage
- Focus on testing behavior rather than implementation details
- Include edge cases and error conditions in tests

### Test Types

- **Unit Tests**: Test individual functions or classes in isolation
- **Integration Tests**: Test interactions between components
- **System Tests**: Test end-to-end behavior of the simulation

### Performance Tests

- Use the `perf/` directory for performance benchmarks
- Compare performance before and after optimizations
- Set up automated performance regression testing when possible

## Documentation Standards

### Docstrings

- DO NOT use top-level (module-level) docstrings
- Use docstrings for all public functions, classes, and methods
- Follow this format for docstrings:

```python
def function_name(param1: Type, param2: Type) -> ReturnType:
    """
    Short description of what the function does.

    Longer description if needed with more details.

    :param param1: Description of param1
    :param param2: Description of param2
    :return: Description of return value
    :raises ExceptionType: When and why this exception is raised
    """
```

### Architecture Documentation

- Document architectural decisions and patterns
- Explain the relationships between different components
- Use diagrams when helpful (can be included as images or ASCII art)