deep

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

With no annotations, the description carries the full burden. It discloses that an LLM is invoked, uses specific data sources, and produces a report. However, it does not mention whether the tool is read-only, performance considerations, or if the operation may affect the repository. This is adequate but not comprehensive.

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 well-structured with a purpose sentence, behavioral details, configuration requirements, and parameter definition. No redundant sentences; it is concise but informative.

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?

Given the tool's complexity (AI analysis), the description covers prerequisites and output type but lacks clarity on whether modifications are made. The output schema exists, so return values are not needed, but behavioral completeness could be enhanced by stating non-destructive nature.

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?

There is only one parameter 'path', and the description explains it as 'Absolute path to the repository root directory', adding meaning beyond the schema's type-only definition. Since schema_description_coverage is 0%, this compensation is valuable and clear.

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 specifies the tool's output as a 'comprehensive Knowledge Extraction Report' and explains it uses an LLM to analyze static extraction data and file contents for architectural analysis. This clearly states what the tool does, distinguishing it from siblings like 'full_extraction' which likely performs raw extraction rather than analysis.

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 mentions prerequisites (API key or local LLM) but provides no guidance on when to use this tool versus alternatives like 'ask' or 'scan'. There is no explicit context for selection, making it harder for an agent to decide.

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