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T0UGH

MCP Server My Lark Doc

by T0UGH
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

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

  • Latest release: v0.1.6

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves document content by URL, the other searches the wiki by keywords. There is no ambiguity or overlap.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern with snake_case: 'get_lark_doc_content' and 'search_wiki'. The naming convention is uniform and predictable.

    Tool Count2/5

    With only 2 tools, the server feels underdeveloped for a document/wiki domain. Typical servers of this scope would include list, create, update, or delete operations, making this minimal.

    Completeness2/5

    The tool surface is incomplete for Lark Doc: it only provides content retrieval and wiki search, but lacks essential operations like listing documents, editing, or managing wiki pages. Major gaps exist.

  • Average 2.1/5 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

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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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

  • Behavior1/5

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

    The description provides no behavioral information beyond the basic action. With no annotations, the description should disclose safety (e.g., read-only), authentication requirements, or error behavior, but it says nothing. This leaves the agent blind to important execution traits.

    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, but this is under-specification rather than conciseness. It lacks structure, such as separating the main description from parameter details. Every sentence does not earn its place because more essential information is omitted.

    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 no output schema, no annotations, and a single parameter, the description should explain the return value, expected behavior, and constraints (e.g., URL must be accessible). It provides nothing, making it wholly incomplete for an AI agent to use correctly.

    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 input schema has no description for the 'documentUrl' parameter, and the description only repeats the parameter name ('documentUrl: Lark document URL') without clarifying format, validity rules, or examples. Schema coverage is 0%, and the description fails to compensate.

    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') and the resource ('Lark document content'), distinguishing it from the sibling 'search_wiki' which searches rather than retrieves content. However, it does not specify the format or scope of 'content' (e.g., plain text, structured data).

    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 usage guidance is provided. The description does not mention when to use this tool instead of 'search_wiki' or any other alternative. It simply states the function without context.

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

  • Behavior1/5

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

    No annotations are provided, so the description must disclose behavioral traits. It fails to mention any side effects, rate limits, authentication needs, or result structure. The description is completely silent on behavioral aspects beyond the basic action.

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

    Conciseness3/5

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

    The description is very short (two lines for parameters) and follows a docstring format. While it is concise, it may be too sparse, omitting useful information that could fit without being verbose.

    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 no output schema, the description should explain return values. It does not describe what the search results contain. Additionally, with a sibling tool available, no clarification of relationship is provided, leaving the agent with incomplete context.

    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?

    Schema description coverage is 0%, so the description must add meaning. It describes 'query' as 'Search keywords' and 'page_size' as 'Number of results to return (default: 10).' This adds minimal semantics but does not explain expected format or behavior beyond defaults.

    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 'Search Lark Wiki,' which clearly indicates a search verb and resource. However, it does not differentiate from the sibling tool get_lark_doc_content, which might retrieve content of a specific document. The purpose is clear but lacks distinction.

    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 search_wiki versus the sibling tool get_lark_doc_content. The description does not mention alternatives or exclusions, leaving the agent to infer usage context.

    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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  • Evaluate tool definition quality.

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