The Mine Works

search_companies_house_uk

Search or look up UK companies on the official Companies House register: status, type, incorporation date, registered office address, SIC codes, and optionally the full officer roster. Requires your own free Companies House API key.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	No	Company name/keyword search. Use this OR companyNumbers.
`apiKey`	Yes	Your free Companies House REST API key. Register an application at developer.company-information.service.gov.uk.
`maxResults`	No	Max companies to return (default 50)
`companyNumbers`	No	Exact UK company numbers for direct lookup (e.g. ["09446231"])
`includeOfficers`	No	Fetch the officer roster (directors, secretaries) for each company (default false)

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the data fields returned and the need for an API key, but doesn't mention rate limits, pagination, behavior when both query and companyNumbers are provided, or potential performance hits from includeOfficers. This is adequate but not rich.

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 two sentences: the first conveys the purpose and result fields, the second the API key prerequisite. Every word serves a purpose, with no redundancy or fluff.

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?

Given the lack of output schema and annotations, the description covers the essential purpose, data fields, and authentication requirement. It doesn't explain edge cases or response structure, but for a straightforward search tool with 5 well-documented parameters, it is reasonably 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%, and the schema descriptions are already detailed. The description adds no new parameter-specific meaning beyond confirming what fields are returned, so it doesn't go beyond the baseline of 3.

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 'Search or look up UK companies on the official Companies House register' and enumerates specific data returned (status, type, incorporation date, registered office, SIC codes, optionally officers). This specific verb+resource combination distinguishes it from sibling tools like search_crunchbase or lookup_lei.

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 provides clear context that this tool is for UK company data from the official register, and notes the API key requirement. While it doesn't explicitly name alternatives or exclusion criteria, the strong UK-specific framing implicitly signals when this tool should be selected over broader company search tools.

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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TDQS

A3.5/5.0

Disambiguation4/5

Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.

Tool Count2/5

With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.

Completeness4/5

The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.

Resources

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