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79 lines
2.0 KiB
Markdown
79 lines
2.0 KiB
Markdown
# Contributing: new tool
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## What you're adding
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A deterministic Python function that an agent can call. Tools are the
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pure-function layer between agents and data — no LLM calls, no I/O except
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`knowledge/*.yaml` loads and SQL reads.
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## File locations
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- Implementation: `perfxpert/tools/<module>.py`
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- Tests: `tests/test_tools/test_<module>.py`
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## Template
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```python
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"""<module> — <one-line purpose>.
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Tool class: READ_ONLY (MCP-safe) or EXECUTION (in-process only).
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Tool classes are enumerated in the multi-agent design spec (local
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contributor doc, not tracked in the repo) — Appendix A.
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"""
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from typing import Any, Dict
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from perfxpert.knowledge import load_yaml
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from perfxpert.tools._class import ToolClass, tool_class
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@tool_class(ToolClass.READ_ONLY)
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def my_tool(arg: str) -> Dict[str, Any]:
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"""<docstring>
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Args:
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arg: <what>
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Returns:
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<shape>
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Raises:
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KeyError: if <condition>
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"""
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data = load_yaml("my_yaml")
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# ...
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return {...}
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```
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## Schema constraints (CI-enforced)
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- Type hints mandatory on args + return
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- Docstring in Google style
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- No external I/O (filesystem writes, network) without `ToolClass.EXECUTION`
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- < 100 ms per call (p99) — covered by a timing test
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## Tests you must add
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- `test_<name>_returns_expected_shape` — happy path
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- `test_<name>_raises_on_unknown_input` — error path
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- `test_<name>_is_read_only_class` — MCP policy
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- `test_<name>_under_50ms` — performance
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## Review requirements
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- 1 core reviewer
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- CI green (unit + MCP-exposure + type check)
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## Common pitfalls
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- Don't re-implement what a `knowledge/*.yaml` already covers — look up instead
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- `@tool_class` decorator MUST come after docstring (otherwise Pydantic schema generation skips it)
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- Avoid f-string SQL for integer bound-params — use f-strings for INTEGER WHERE/LIMIT
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- Pure Python stdlib + yaml + Pydantic only — no heavy deps
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## Related docs
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- Design spec: Appendix A (tool inventory)
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- Existing tools under `perfxpert/tools/` as references
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