Search public KentekenKompas model, answer, term and provenance knowledge using deterministic lexical relevance. relevanceScore is not AI-confidence.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| sources | Yes | ||
| coverage | No | ||
| evidence | Yes | ||
| freshness | Yes | ||
| citationUrl | No | ||
| limitations | Yes | ||
| schemaVersion | Yes | ||
| knowledgeVersion | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: results are based on deterministic lexical relevance, and 'relevanceScore is not AI-confidence' is an important caveat preventing misinterpretation of search results.
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 entire description is one sentence with no filler. It front-loads the core purpose and includes the most important behavioral caveat in a compact second clause. Every word earns its place.
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?
The output schema and annotations cover return structure and safety, while the description covers scope and relevance semantics. However, missing usage guidance and parameter semantics leave the agent to infer when to invoke this tool versus the knowledge-focused siblings, so the context is not fully complete.
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%, so the description carries the burden of explaining 'query' and 'limit'. It does not mention either parameter or clarify how the query is matched or how limit affects results. The parameter names are fairly self-explanatory, but the description adds no semantic value beyond the schema.
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 action ('Search'), the resource ('public KentekenKompas model, answer, term and provenance knowledge'), and the method ('deterministic lexical relevance'). It is specific enough to identify the tool's function, though it does not explicitly distinguish it from sibling knowledge tools.
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 gives no guidance on when to use this tool versus alternatives like explain_dutch_vehicle_term, get_answer_context, or get_model_context. The deterministic-lexical-relevance note implies a use case for exact keyword searches, but no when-to-use or when-not-to-use instructions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.