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qinisolabs

companieswise

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by qinisolabs
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Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.1

  • Disambiguation5/5

    Each tool has a clearly distinct and non-overlapping purpose: lookup by number, search by name, and validate number format. No ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (lookup_company, search_company, validate_company_number).

    Tool Count5/5

    With 3 tools, the server is well-scoped for its narrow domain of UK company data lookup. Each tool serves an essential function without redundancy.

    Completeness5/5

    The tool set covers the complete workflow: search for a company to find its number, validate the number format, and retrieve official details. No obvious gaps.

  • Average 4.5/5 across 3 of 3 tools scored. Lowest: 3.9/5.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 13 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior3/5

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

    No annotations are provided, so the description bears full burden. It discloses data sources (monthly snapshot vs live register) and that it matches search words, but does not mention rate limits, pagination, or result structure.

    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 a single sentence that front-loads the action. It is concise and contains no waste, though breaking it into two sentences could improve readability.

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

    Completeness4/5

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

    For a simple two-parameter tool with no output schema, the description covers purpose, data source, and key behavior. It could mention result format or multiple matches, but is fairly complete.

    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 100% with descriptions for both parameters. The tool's description adds minor context about matching search words for the 'query' parameter, but does not significantly enhance understanding beyond the schema.

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

    Purpose5/5

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

    The description clearly states it finds a UK company's number from its name, using a reverse lookup. It distinguishes itself from sibling tools like lookup_company (presumably by number) and validate_company_number.

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

    Usage Guidelines4/5

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

    The description explicitly says 'USE THIS' and provides context (instead of guessing the number). It implies when to use but does not explicitly state when not to use or mention alternatives.

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

  • Behavior5/5

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

    With no annotations, description fully discloses behavior: default monthly snapshot, absence of dissolved companies, real-time option with API key, and honest 'not found' return. Clearly communicates data freshness and scope.

    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?

    Description is informative and flows logically: use case, return fields, data source limitations, optional enhancement. Each sentence adds value, though slightly verbose; could be more concise without losing meaning.

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

    Completeness5/5

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

    For a single-parameter lookup tool, description is remarkably complete: explains return fields, edge cases ('not found'), data source, and optional configuration. No output schema needed as return values are enumerated.

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

    Parameters4/5

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

    Schema already describes the 'number' parameter, but description adds value by providing examples and clarifying that spaces/case are ignored, aiding correct invocation. Schema coverage is 100%, so baseline 3; extras justify 4.

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

    Purpose5/5

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

    Clearly states the tool retrieves a UK company's official registered details by Companies House number, listing specific fields. Distinguishes from siblings (search and validate) by focusing on exact number lookup.

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

    Usage Guidelines4/5

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

    Explicitly advises using this tool instead of relying on model recall, highlighting a key use case. Mentions limitations (monthly snapshot, dissolved companies absent) and optional API key for real-time data. Lacks explicit alternatives but context implies siblings for search/validation.

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

  • Behavior5/5

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

    No annotations provided, so description carries full burden. Discloses that it only checks format, specifies valid patterns (8 digits or prefix+6 digits), notes there is no check digit, and explains what prefixes denote. Clearly states what it does not do (confirm existence).

    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?

    Two sentences, front-loaded with a strong imperative. Every sentence adds essential information with no waste.

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

    Completeness5/5

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

    For a validation tool with one parameter, no output schema, and clear sibling differentiation, the description covers purpose, usage guidelines, format details, and limitations completely.

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

    Parameters4/5

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

    Schema description coverage is 100% (parameter 'number' has description), so baseline is 3. Description adds extra meaning by detailing valid formats and prefixes, going beyond the schema's simple description.

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

    Purpose5/5

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

    The description uses a specific verb ('check'), identifies the resource ('UK company number'), and clearly states it validates format and identifies register/type. It distinguishes itself from lookup_company, which confirms existence.

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

    Usage Guidelines5/5

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

    Explicitly states when to use ('before relying on it') and what not to use it for ('does NOT confirm the company exists'). References the sibling tool lookup_company as the alternative for existence checking.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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