python_project_info

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions 'detection with caching,' which adds behavioral info, but does not disclose whether the tool modifies files, requires internet, or any side effects. It likely performs a read-only analysis, but this is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the purpose and includes specific details in parentheses. No wasted words, and every part adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description should provide more context about what the tool returns (e.g., JSON structure, output format). While it covers the detection scope, it lacks clarity on how to interpret the results, leaving the agent partially uninformed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% via the 'directory' parameter description 'Working directory for the command.' The tool description adds context about Python analysis but does not elaborate on the parameter beyond the schema, which already fully describes it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Analyze Python project configuration, dependencies, and structure' and specifies the detected files (pyproject.toml, setup.py, requirements.txt). This distinguishes it from sibling tools like nodejs_project_info or go_project_info, which target other languages.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for Python projects but does not explicitly state when to use it over alternatives like detect_project or project_status. No when-not or exclusion criteria are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.