Query the docs knowledge graph

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the answer is generated from retrieved graph context, that source files are returned for grounding, and that the call can take up to several minutes on a local model. These traits materially shape a caller's expectations.

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?

Four sentences, each earning its place: the core action, when to use it, output/grounding expectations, and latency warning. The most important information is front-loaded, with no filler or repetition.

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?

An output schema exists, so the description doesn't need to explain return values. It covers intent, usage guidance, grounding behavior, and a critical latency caveat for a moderately complex tool. The only gap is parameter semantics, which is already scored separately; overall the description is complete enough to guide correct 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 description coverage is 0% and the description does not compensate. It only clarifies that the query is a question about the corpus; mode and top_k are left entirely unexplained, even though defaults exist. An agent has no way to understand what mode='hybrid' means or how top_k affects results.

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 leads with a specific verb and resource: 'Search the indexed documentation knowledge graph and return a grounded answer.' It gives concrete examples of the kind of questions it handles, and the resource and behavior clearly distinguish it from docs_ingest and docs_stats.

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 explicitly states when to use it ('questions about the libraries and tools in the corpus') and when not to ('instead of relying on recall'), and gives a preference directive ('prefer it over an ungrounded answer'). This is clear, actionable guidance even though sibling tools aren't named.

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