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

Annotations already cover read-only/idempotent/non-destructive, but the description adds substantial context beyond them: the tool never raises protocol errors and instead returns a structured {"error": ...} with a concrete example, plus a cost disclosure ('FREE') and an explicit retry-safety guarantee. It also discloses the match-scoring heuristic (word overlap in name/type/observations), which is real behavioral information an agent cannot get from annotations.

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?

Purpose and cost are front-loaded in the first line, and the sibling routing and error contract follow logically. The inline example JSON is long but earns its space by demonstrating scoring output; the closing 'read-only and idempotent' sentence partly restates the annotations and could be trimmed.

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?

For a 4-parameter tool with nested graph input, rich annotations and an output schema, the description covers purpose, alternatives, scoring behavior, hop expansion, error contract and retry safety. Nothing an agent needs in order to call it correctly or recover from a bad call is missing.

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 100% and every parameter has a description with bounds and defaults, so the baseline is 3. The description goes beyond that by showing a concrete call shape ({"query": "acme renewal", "hops": 1}) and explaining what the resulting 'score' and 'why' fields mean, which makes the query and hops semantics tangible rather than abstract.

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?

States a specific verb+resource ('find what the graph knows about something') and immediately extends it with scope ('plus its neighbourhood'). It also names the two siblings it is not (graph_upsert for writing, graph_at_time for date narrowing), so an agent can discriminate without opening any schema.

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?

Gives an explicit when-to-use ('Use to read memory back before answering') and two explicit when-not-to-use cases mapped to the correct alternatives. The scoring/hop rationale also tells the agent why a hops value is appropriate.

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