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DarkhorseOne

companies-house-mcp

by DarkhorseOne

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct aspect of company data: search, profile, officers, and filings. There is no overlap between these operations, and an agent can easily select the correct tool based on the information needed.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case: search_ and get_ prefixed to the resource type. This makes the API predictable and easy to navigate.

    Tool Count4/5

    With only 4 tools, the server is compact but still provides the core look-up operations for a company information API. While not exhaustive, the count is reasonable for a focused, single-domain server.

    Completeness4/5

    The set covers the primary read-only workflows: finding a company, viewing its details, officers, and filings. Notable omissions like charges or persons of significant control might be expected, but for a basic Companies House integration, the surface is largely complete.

  • Average 3.2/5 across 4 of 4 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
    • 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.

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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, the description carries full behavioral disclosure burden. It only states 'Get company filing history' and omits important traits like pagination (items_per_page param), ordering, date filtering, or return format. No additional context is given.

    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 concise sentence with no redundant words. It is front-loaded and easy to parse.

    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?

    Despite having only 2 well-documented parameters and no output schema, the description is too thin. It does not explain what the returned filing history looks like, order, or any limitations. Given the lack of annotations and output schema, more context is needed for 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 100%—both company_number and items_per_page are documented with examples and defaults. The description adds no semantic value beyond the schema, so a baseline score of 3 is appropriate.

    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?

    Description uses a specific verb ('get') and resource ('company filing history'), clearly distinguishing it from sibling tools like search_companies, get_company_profile, and get_company_officers. While it doesn't explicitly list what filings include, the resource is unmistakable.

    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. There is no mention of prerequisites, typical scenarios, or exclusions. The description simply states the operation.

    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, the description carries the full burden of behavioral disclosure. It only states the action ('Get...information') without indicating whether the operation is read-only, requires authentication, how data is returned, or any side effects. This is insufficient for a tool without structured safety hints.

    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 filler or redundancy. It efficiently conveys the core purpose, making it appropriately concise for a simple getter tool.

    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?

    The tool has no output schema, so the description should compensate by explaining what 'detailed company profile' includes (e.g., registered address, status, industry codes). The minimal phrasing leaves the agent guessing about the returned data and how it differs from officers or filings, making it incomplete for effective selection and invocation.

    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 single parameter 'company_number' is fully described in the schema (100% coverage) with an example. The description adds no extra meaning beyond the context of retrieving a profile, which is implicitly clear from the tool name. The baseline of 3 applies because the schema handles parameter 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 tool retrieves 'detailed company profile information', establishing a specific verb and resource. It distinguishes from sibling tools by focusing on profile data rather than search, officers, or filings, though it doesn't enumerate what 'detailed' encompasses.

    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 like search_companies or get_company_officers. The description omits any context about prerequisites, exclusions, or suitability, leaving the agent without decision factors.

    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 the full burden of behavioral disclosure. It only states the search scope and does not mention pagination, result format, rate limits, or read-only nature, leaving significant uncertainty for the agent.

    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 filler. It directly states the core function and is appropriately sized for a simple search tool.

    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?

    With no output schema and only a minimal description, the agent cannot infer the return structure, pagination behavior, or what fields are included in results. The description fails to provide essential context for a complete understanding of the tool.

    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 schema fully describes both parameters (query and items_per_page). The description's 'by name or keyword' mirrors the query parameter description, adding no substantial meaning beyond the schema. Baseline 3 is appropriate.

    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 specifies the verb 'search', the resource 'UK companies', and the scope 'by name or keyword'. This distinguishes it from sibling tools like get_company_profile, which retrieve specific data for known companies.

    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 its use for finding companies by name or keyword, but it does not explicitly state when to prefer this over alternatives (e.g., when the company number is unknown) or provide exclusions. The context is clear but lacks direct guidance.

    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 the full burden. It states what it returns (list of officers) but omits any behavior like required authentication, error cases, pagination, or handling of empty results. Minimal disclosure.

    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?

    Single sentence, front-loaded with the action, and zero wasted words. Concise and easy to scan.

    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?

    No output schema exists, so the description should clarify return structure. It states 'list of company officers' and type examples, but lacks detail on officer object fields. Adequate for a simple one-param tool, but not complete for programmatic use.

    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%: the single parameter company_number is fully described with an example. The description adds no extra semantics, but the schema provides sufficient meaning.

    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+resource: 'Get list of company officers (directors, secretaries, etc.)'. It clearly distinguishes from sibling tools like search_companies (search) and get_company_filings (filings).

    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?

    Usage context is implied: use this to retrieve officer details for a given company. No explicit when/when-not guidance or references to alternatives, but the purpose is clear enough for basic selection.

    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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