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crewAIInc

CrewAI Enterprise MCP Server

Official
by crewAIInc
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Python
Hybrid

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one starts a new crew task (kickoff_crew) and the other checks the status of an existing task (get_crew_status). There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (kickoff_crew, get_crew_status) with clear action-oriented verbs and the same noun root. The naming is predictable and follows the same convention throughout.

    Tool Count2/5

    With only two tools, the server feels severely under-equipped for an 'Enterprise MCP Server' that presumably manages crew tasks. There are likely missing operations like listing crews, updating tasks, or handling errors that would be needed for complete workflow coverage.

    Completeness2/5

    The tool surface is significantly incomplete for crew task management. While it covers starting and checking status, there are obvious gaps: no way to list existing crews, update tasks, cancel tasks, or retrieve results beyond status. This will cause agent failures in many scenarios.

  • Average 2.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. While it states this is a read operation ('Get'), it doesn't address important behavioral aspects like whether this requires authentication, rate limits, error conditions, or what specific status values might be returned. The description provides minimal behavioral context beyond the basic operation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clear sections for Args and Returns, and every sentence serves a purpose. It's appropriately sized for a single-parameter tool, though the 'Dictionary containing the crew task status' return description could be more specific about what the dictionary contains.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficiently complete. While it covers the basic operation and parameter, it lacks crucial information about authentication requirements, error handling, status format/details, and relationship to the sibling tool that would help the agent use it effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description explicitly documents the single parameter 'crew_id' and its purpose ('The ID of the crew task to check'), which adds meaningful context beyond the schema's 0% description coverage. However, it doesn't provide format details, examples, or constraints for the crew_id parameter that would be helpful for the agent.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with a specific verb ('Get') and resource ('status of a crew task'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'kickoff_crew', which appears to be a different operation (initiating vs. checking status).

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, timing considerations, or relationship to the sibling 'kickoff_crew' tool, leaving the agent without context for appropriate selection.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool starts a task and returns a crew id for status checking, but doesn't address critical behavioral aspects like whether this is a synchronous or asynchronous operation, what happens if a task fails, or any permission/rate limit requirements.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately concise with three sentences that each serve a purpose: stating the action, describing the input, and explaining the output. However, the structure could be improved by front-loading more critical information about the tool's behavior.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with no annotations, no output schema, and a complex nested parameter with 0% schema coverage, the description is inadequate. It doesn't explain what constitutes valid 'inputs', what the response structure looks like beyond containing a crew id, or the operational characteristics of starting a crew task.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage and 1 parameter that's a nested object, the description only vaguely mentions 'Dictionary containing the query and other input parameters.' This provides minimal semantic value beyond what the bare schema indicates, failing to compensate for the complete lack of schema documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('Start') and resource ('new crew task'), providing a specific purpose. However, it doesn't distinguish this tool from its sibling 'get_crew_status' beyond the obvious difference in action.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage context by mentioning that the returned crew id is needed to check status with 'get_crew_status', suggesting a workflow relationship. However, it doesn't explicitly state when to use this tool versus alternatives or provide any exclusions.

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

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