Skip to main content
Glama
qinisolabs

companieswise

Official

lookup_company

Get a UK company's official registered details by its Companies House number. Returns name, status, type, incorporation date, postcode, and SIC code.

Instructions

USE THIS to get a UK company's official registered details by its Companies House number — instead of recalling them, which models get wrong (invented names, wrong status). Returns the registered name, status, company type, incorporation date, registered-office postcode and primary SIC code from the official snapshot, or an honest 'not found'. IMPORTANT: the data is a MONTHLY snapshot of the LIVE register — status is as of the dataset date, not real-time, and dissolved companies are generally absent. UK-wide (England & Wales, Scotland, NI).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numberYesThe UK company number, e.g. '00000006' or 'SC123456' (spaces/case ignored).
Behavior4/5

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

No annotations provided, so description carries full burden. It clearly states the tool returns specific fields, 'not found' response, and the data limitation (monthly snapshot, not real-time). This is thorough behavioral disclosure.

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 concise (6 sentences), front-loaded with a strong directive, and efficiently covers purpose, output, and limitations without wasted words.

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 simple input and no output schema, the description adequately covers purpose, input format, output fields, limitations, and geographic scope. It is complete for an agent to 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% with a clear description of the number parameter. The description adds no significant new meaning beyond the parameter description, so baseline 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?

The description clearly states the tool retrieves official registered details by company number. It explicitly says 'USE THIS to get... instead of recalling them', which differentiates from model hallucination, but does not directly contrast with sibling tools like search_company.

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?

Description gives strong usage guidance ('USE THIS... instead of recalling them') and warns about monthly snapshot and absence of dissolved companies. However, it does not explicitly mention when to use alternative tools like search_company.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/qinisolabs/companieswise'

If you have feedback or need assistance with the MCP directory API, please join our Discord server