verify_provenance

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

No annotations provided; description explains the IPD score range and that it uses BIPT, but does not disclose side effects, error behavior, or performance considerations. Adds some transparency but has gaps.

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?

Well-structured: summary, IPD explanation, usage guidance, and parameter descriptions. Concise but covers necessary information without verbosity.

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 2 parameters, one required, and no annotations, the description provides adequate information including output explanation (IPD score). Could include more on return structure but is sufficient for use.

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 coverage is 0%, so description compensates by explaining 'code: The LLM-generated code to verify' and 'context: The repository context', adding meaning beyond schema titles.

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 'Verify that LLM-generated code is grounded in the provided context' with specific verb 'verify' and resource 'provenance', distinguishing it from sibling tools like verify_response and verify_beliefs.

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?

Explicitly says 'Use this after an LLM generates code to check for hallucinated APIs...before accepting output', giving clear use case. Does not specify when not to use or alternatives, but is sufficient.

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