Semantic Graph Search

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

Discloses use of local sentence embeddings (bge-small-en, 384-dim) and the expansion behavior. Aligns with annotations (readOnlyHint=true) without contradiction.

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?

Three sentences, each adding essential information. Front-loaded with purpose, then usage guidance, then behavioral detail. No redundant or irrelevant content.

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 6 parameters, no output schema, and complex expansion logic, the description is mostly complete. It explains the core functionality and when to use it. Could mention what the returned data looks like (e.g., list of entities with similarity scores and neighbors), but not critical.

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%, so baseline is 3. The description adds overall context but does not provide new per-parameter details beyond what the schema already gives. No independent value added for parameters.

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 finds semantically similar entities to a natural-language query and optionally expands via graph traversal. It differentiates from sibling tools by mentioning falling back to graph_query when embeddings are unavailable.

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?

Explicitly states when to use this tool (user wording doesn't match canonical entity names) and when to fall back to graph_query (no embeddings available). Provides illustrative examples like 'containers' → Docker.

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