Which certification is this prose name? Returns candidates with ids, issuers, and source basis — candidates rather than a single guess when the name is ambiguous.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional maximum number of candidates | |
| query | Yes | Prose name, exam code, or issuer string to resolve | |
| issuer | No | Optional issuer name to narrow candidates |
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?
With no annotations, the description carries the behavioral disclosure burden. It transparently reveals that the tool returns candidates rather than a definitive single match when the name is ambiguous, and it lists what the candidates include. It does not cover pagination, ordering, or edge-case behavior, but for a read-only search tool the key ambiguity behavior is disclosed.
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 a single, tightly worded sentence that front-loads the user-facing question, states the return contents, and explains the candidate-based behavior. No filler or redundant restatement of the tool name.
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?
With no output schema, the description does enough to convey what the tool returns: candidate IDs, issuers, and source basis, plus the ambiguity-aware behavior. It is slightly light on operational details like empty results or limit handling, but the core usage and response semantics are covered well.
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 100%, so the baseline applies: all three parameters are already documented accurately in the schema. The description reinforces that the query is a prose name but does not add new per-parameter meaning 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 tool's behavior: resolving a prose certification name into candidate certifications with IDs, issuers, and source basis. This distinguishes it from siblings like certifications_for_occupation or get_certification, which address different lookup paths.
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 opening question 'Which certification is this prose name?' establishes a clear use case, and the schema further specifies the query can be an exam code or issuer string. However, it does not explicitly contrast with alternatives such as get_certification for exact ID lookups, so exclusion guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.