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

Beyond the annotations (read-only, idempotent), the description reveals scoring logic, neighbourhood inclusion, exact word matching, and error behavior (never raises protocol errors, returns error objects). It also states 'Every call is read-only and idempotent,' reinforcing the annotations, and explains the rationale behind neighbourhood pulls.

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 concise yet packed with information: a clear opening line, a concrete example, usage guidance, and error handling—all in about 150 words. Every sentence earns its place without redundancy, making it highly effective.

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 and the presence of an output schema, the description covers essential aspects: purpose, scoring, hops semantics, error behavior, and safety guarantees. It addresses potential pitfalls (e.g., invalid input) and provides a complete mental model for correct usage.

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?

While the schema fully describes parameters, the description enriches query semantics with 'Matching is on whole lowercase words' and explains hops with '0 returns matches only.' The example ties graph, query, and hops together, providing practical meaning beyond mere schema definitions.

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 purpose: 'Find what the graph knows about something, plus its neighbourhood.' It uses a specific verb ('Find') and resource ('the graph'), then elaborates with scoring and hop-based expansion. It also distinguishes from siblings by explicitly mentioning graph_upsert and graph_at_time, leaving no ambiguity about its role.

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?

Explicit usage guidance is provided: 'Use to read memory back before answering. Not for writing (graph_upsert) and not for narrowing by date, which graph_at_time does.' This directly tells when to use this tool versus alternatives, and the error-handling note adds when retries are safe.

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