code_graph_suggest

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

With no annotations provided, the description discloses key traits: read-only, no embeddings, no LLM, and a deterministic ranking algorithm. It doesn't cover return format or error behavior, but the main behavioral characteristics are revealed.

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 concise sentences front-load the primary purpose and then add two important caveats ('No embeddings, no LLM' and 'Read-only'). No wasted words.

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 tool has 4 parameters and no output schema or annotations, yet the description omits parameter meanings, prerequisites (e.g., whether an index must exist), and return shape. It's insufficient for reliable invocation.

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%, and the description fails to explain parameters like app_id, db_path, or max_results. It only mentions 'task description' indirectly, leaving the other three parameters undefined.

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 a specific verb ('Suggest'), resource ('files'), and method ('keyword overlap with symbol names + file paths'), distinguishing itself from sibling tools like code_graph_search by emphasizing the lightweight heuristic approach.

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?

It sets clear context for when to use: given a task description, find relevant files. However, it doesn't name alternatives or exclusions, leaving the agent to infer when not to use it (e.g., vs. code_graph_search or code_graph_impact).

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