crawl_entity

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

Without annotations, the description carries the burden. It discloses that the tool generates hypotheses, verifies against the code graph, and records findings in a knowledge database. This gives a clear behavioral picture, though it could mention whether the operation is read-only or if it modifies the database.

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 concise with two sentences and an example. It is well-structured and front-loaded with the action. However, it could be slightly more structured with separate sections for behavior and parameters.

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?

The description explains the tool's function and side effects but lacks information about the return value or output format, as there is no output schema. Given the complexity and lack of output schema, more details on what the tool returns would improve completeness.

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 100% with descriptions for all parameters. The description provides an example usage but adds no additional semantic information beyond what the schema already provides. Baseline of 3 is appropriate.

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 the tool's purpose: 'Run ontological crawl on a code entity — generate hypotheses and verify against code graph.' It gives an example and differentiates from siblings by focusing on hypothesis generation and verification, which is unique among tools like analyze_project or find_calls.

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 explains what the tool does but does not explicitly state when to use it versus alternatives. It mentions the code graph and knowledge database but lacks guidance on prerequisites or scenarios where this tool is preferred over similar ones.

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