Analyze project keywords to identify and recommend specialized agents for optimal role assignments, enabling efficient deployment of professional expertise across diverse departments.
Recommend agents based on project keywords
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | Yes | List of project keywords (e.g., api, database, ui) |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool 'recommends' agents, implying a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns structured data, or how recommendations are generated (e.g., based on a database or algorithm).
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, efficient sentence with zero waste, front-loading the key action ('Recommend agents') and context ('based on project keywords'). It's appropriately sized for a simple tool.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of agents with details, scores, or just names), how recommendations are ranked, or any error conditions, leaving gaps for a tool that likely involves data processing.
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%, with the parameter 'keywords' well-documented in the schema as 'List of project keywords (e.g., api, database, ui)'. The description adds no additional meaning beyond this, so it meets the baseline of 3 for high schema coverage.
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 verb ('Recommend') and resource ('agents') with the context ('based on project keywords'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'search-agents' or 'list-agents', which might offer similar functionality.
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?
No guidance is provided on when to use this tool versus alternatives such as 'search-agents' or 'list-agents'. The description implies usage for keyword-based recommendations but lacks explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.