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crunchloop

MCP Teamtailor

by crunchloop
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves a single candidate by ID, the other lists and filters candidates. There is no ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with the prefix 'teamtailor_', making them predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the set feels thin for a typical ATS domain. While basic get and list are covered, a richer set (e.g., create, update, delete) is expected, making the count borderline.

    Completeness2/5

    The tool surface is severely incomplete for candidate management: it lacks create, update, delete operations, and other common entities like jobs or applications are missing.

  • Average 2.7/5 across 2 of 2 tools scored. Lowest: 2/5.

    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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  ]
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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?

    No annotations are present, so the description carries the full burden. It does not disclose any behavioral traits such as pagination behavior, rate limits, or what happens when filters yield no results. The tool's mutability is unclear.

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

    Conciseness2/5

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

    The description is extremely short (4 words) but this is not conciseness; it is under-specification. Important details about parameters and behavior are omitted. A concise description would still cover essential aspects.

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

    Completeness1/5

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

    Given the tool's complexity (nested filter object, pagination, no output schema), the description is completely inadequate. It does not explain return values, filtering syntax, or any other context needed for correct invocation.

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

    Parameters1/5

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

    The schema has 0% description coverage, and the description adds no parameter details. It mentions 'filter' but does not explain the filter object's properties (createdAfter, etc.) or how pagination parameters work. This is insufficient for proper usage.

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

    Purpose3/5

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

    The description states the tool lists and filters candidates, which is a clear verb+resource. However, it does not differentiate from the sibling 'teamtailor_get_candidate', which likely retrieves a single candidate. The purpose is clear but lacks specificity.

    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?

    No guidance is provided on when to use this tool versus alternatives. The sibling tool exists but is not mentioned, and there is no context about appropriate scenarios or preconditions.

    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?

    No annotations are provided, so the description carries full burden. It only states 'Get' with no details on response format, side effects, permissions, or rate limits. For a read operation, more context would be beneficial.

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

    Conciseness5/5

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

    The description is a single, front-loaded sentence with no superfluous words. It efficiently conveys the core function.

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

    Completeness3/5

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

    Given the simple one-parameter tool and no output schema, the description is minimally complete. It states what the tool does but lacks details about the response or any edge cases, which could be helpful for a complete understanding.

    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?

    Schema coverage is 0%, and the description adds minimal meaning ('by their id') beyond the parameter name 'candidateId'. No format, constraints, or examples are given, but the purpose is partially clarified.

    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 action (Get), resource (candidate), and qualification (by id). However, it does not distinguish from sibling tool 'teamtailor_list_candidates' beyond the implied singular vs. plural.

    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 that use is for retrieving a specific candidate by ID, but no explicit guidance on when to use this versus the list tool, or any prerequisites or alternatives.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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