codebase_graph_query

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

With no annotations, the description bears full responsibility for transparency. It states the tool is a query (implying read-only) and returns data, but it does not disclose prerequisites (e.g., graph must be built), potential side effects, or error conditions. The read-only nature is implied but 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 extremely concise: two sentences, no redundant words. The first sentence states the primary action, and the second clarifies the output. Ideal for quick parsing by an AI agent.

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 lack of an output schema, the description should provide more detail about the return format (e.g., list of imports and dependents, structure). It does not mention error handling or edge cases (e.g., file not found, graph not built). For a query tool, this is a notable gap.

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?

The input schema already provides descriptions for both parameters (filePath and projectPath) with 100% coverage. The description adds no additional semantic information beyond what is in the schema, so a baseline score 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 action ('Query the code dependency graph') and specifically what it returns (imports and dependents for a given file). This distinguishes it from sibling tools like codebase_graph_build or codebase_graph_circular which handle other aspects of the dependency graph.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention that the graph must be built first (via codebase_graph_build) or that this is for a single file query only. The user/AI must infer usage from the description alone.

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