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TypeScript
Remote

Get company financials

get_financials

Retrieve UK company financials from iXBRL filings, including revenue, profits, assets, equity, and employee counts, with data quality indicators.

Instructions

Get structured financial data for a UK company, parsed from its iXBRL accounts filed at Companies House. Returns revenue, cost of sales, gross profit, operating profit, net profit, fixed assets, current assets, total equity, net assets, creditors, and average employees for the current and prior reporting year. Also includes accounts type (full/abbreviated/micro/dormant) and a data_quality block indicating which fields were extracted and which were absent from the filing. Cached for 7 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_numberYesCompanies House company number, e.g. '00445790' for Tesco PLC. Numeric-only numbers should be zero-padded to 8 digits.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv2.0.5
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • changedInput schema / properties / company_number / description
      Previous value: -"Companies House company number, e.g. '00445790' for Tesco PLC"New value: +"Companies House company number, e.g. '00445790' for Tesco PLC. Numeric-only numbers should be zero-padded to 8 digits."
  2. First observedv1.0.2

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does well: it reveals the iXBRL source, the 7-day cache, the accounts-type classification, and the data_quality block that indicates missing fields. It does not cover failure modes such as missing filings, but the data_quality block partially addresses that concern.

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 front-loaded with the core purpose and then layers specific return fields, metadata, and cache behavior. Every sentence adds distinct value and the length is appropriate given there is no output schema to document the return shape.

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 only one parameter, no output schema, and no annotations, the description is largely complete for invocation: it specifies input, return contents, data_quality reporting, and caching. It could be more explicit about currency/units and the exact nested shape of data_quality, but those are secondary to correct 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 schema already provides 100% coverage for the single parameter, company_number, including pattern, example, and zero-padding guidance. The description adds little beyond confirming the tool applies to UK companies, so the baseline score of 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 names a specific verb and resource ('Get structured financial data for a UK company') and then enumerates the exact fields returned, the source filing type, and the extra metadata blocks. This clearly distinguishes it from the sibling tools like get_company, get_directors, or get_psc.

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 makes it clear what data the tool returns, so an agent needing financial statements can infer it is the right choice. However, it never contrasts this tool with its siblings or states when not to use it, leaving the alternative selection to inference rather than explicit guidance.

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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