Search compact passages; weak matches are marked and never presented as an answer.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 the tool as read-only, idempotent, and non-destructive. The description adds valuable context beyond annotations by explaining that weak matches are marked and never presented as an answer, which is a meaningful behavioral nuance not captured by the structured metadata.
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 sentence that is front-loaded with the primary action ('Search compact passages') and includes one additional piece of critical behavior. Every word contributes value, with no filler or redundancy.
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 tool is simple (2 params, no nested objects) and has an output schema, reducing the need to describe return values. However, the description lacks usage guidance and parameter semantics, leaving the agent to infer how to invoke it correctly. This is adequate but not 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?
The input schema defines query (string) and limit (integer with default 8), but the description gives no elaboration on either parameter. Schema description coverage is 0%, so the description was expected to compensate, but it does not clarify the meaning of 'query' or how 'limit' affects results beyond what the schema provides.
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 function: searching compact passages. It specifies the resource type ('compact passages') and adds a unique behavior (weak matches are marked and never presented as an answer), which helps set expectations. However, it does not explicitly differentiate this tool from sibling tools like get_knowledge or retrieve_context, so it falls short of a 5.
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 provides no explicit guidance on when to use this tool versus alternatives. It implies that results are passages and not answers, which could hint at use for retrieval rather than direct answering, but there is no direct comparison to sibling tools or clear 'when to use' statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: problem lifecycle, knowledge reading/search/context, candidate submission/review, and experience verification. Even similar tools like get_knowledge, search_knowledge, and retrieve_context are clearly differentiated by their descriptions.
All tool names follow a consistent snake_case verb_noun pattern with no mixed conventions or vague verbs. Names accurately reflect their actions and objects, making the set predictable and easy to navigate.
At 16 tools, the count is slightly above the typical 3-15 range but still reasonable given the multi-faceted domain (problems, knowledge, candidates, experiences). Each tool appears to have a specific purpose, though a few could potentially be consolidated.
Core workflows are covered: create/claim/manage problems, submit/review candidates, publish/retrieve knowledge, and verify experiences. Minor gaps exist such as no explicit close/cancel operation for problems, but agents can work around these with existing tools.