query_nodes

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

With no annotations provided, the description fully carries behavioral disclosure. It reveals weighted scoring, exact-match bonuses, and pagination support. It stops short of detailing ordering or empty-result behavior, but provides substantial insight into the tool's behavior.

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 tightly structured, with a one-sentence summary, scoring details, and a bulleted argument list. Every sentence serves a purpose without redundancy.

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?

Despite having an output schema (which could reduce the need to explain returns), the description still lists the return fields. It covers query syntax, filtering, pagination, and project scoping, making it self-sufficient for an agent to invoke the tool 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%, but the description compensates fully by explaining each parameter with examples and defaults. It adds meaning beyond the schema types by describing 'node_type' filtering, pagination, and project context.

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 action ('Search for nodes') and the resource ('knowledge graph'), with a clear keyword-based scope. It distinguishes itself from siblings like search_by_file_path by mentioning weighted fuzzy matching and scoring.

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 clearly explains the kind of search (keyword-based, fuzzy matching) and how scoring works, which implies when to use it. However, it does not explicitly mention alternatives or exclusion cases, so it lacks explicit 'when not to use' guidance.

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