scrape_codebase

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

No annotations provided, so description carries full burden. It discloses walking the directory tree, analyzing files, and optional outputs, but omits side effects (e.g., does it modify files?), permissions, or constraints like supported file types. Adequate but not rich.

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?

Two sentences, front-loaded with the primary action. Efficient but could organize information more clearly (e.g., separating required vs optional behavior).

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 7 parameters and no annotations, the description is incomplete. It fails to explain input formats for 'languages' or 'file_patterns', 'depth' options, or the return value structure (despite output schema existing). Agent needs more to invoke correctly.

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 0%, yet the description does not clarify any parameter semantics. It implicitly references 'build_api_reference' and 'build_dependency_graph' via optional outputs, but does not explain 'directory', 'output', 'depth', 'languages', or 'file_patterns'. Agent would have to infer from parameter names alone.

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 local codebase and extract code knowledge' with specific outputs like signatures, docstrings, API references, and dependency graphs. This distinguishes it from sibling tools like scrape_docs or scrape_github which target external sources.

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 usage for local code analysis, but lacks explicit when-to-use or when-not-to-use guidance. Alternatives are not mentioned, but the name and context differentiate it from siblings.

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