Skip to main content
Glama

Distil — Machine-Native Public Data Refinery

by com.distil-data

Get UK Company Filing

get_uk_company_filing
Read-onlyIdempotent

Retrieves official statutory corporate registration record from UK Companies House. Returns active status, incorporation date, SIC industry codes, registered office address, registered charges/mortgages count, statutory annual accounts filing health, and next confirmation statement due date in token-optimized Markdown-KV (< 1 KB). Call this during UK vendor due diligence, credit risk audits, and corporate KYB screening.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceFreshNoSpend Control: When true, bypasses Edge KV cache and forces a real-time statutory primary registry call (applies 2.5x Freshness Inconvenience Surcharge). Default: false.
maxAgeHoursNoSpend Control: Maximum acceptable data age in hours (e.g. 48 for 2 days tolerance). If cache satisfies this, delivers sub-5ms at standard 1.0x Economy rate. Set to 0 to force live primary fetch.
companyNumberYesThe 8-character UK Companies House company number (e.g. '00445790' for Tesco PLC, '00002065' for Lloyds Bank).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
isErrorNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond that: it is an 'official statutory' record, token-optimized Markdown-KV under 1 KB, and includes specific data-health signals like filing health and confirmation statement due date.

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, no filler. The first identifies the action and source; the second packs in return content, output format, and use cases efficiently. Every clause earns its place.

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?

Given the rich input schema, strong annotations, and output schema presence, the description covers the essentials: what it returns, why to call it, and the context. Nothing critical is missing for a UK company filing retrieval 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?

Schema description coverage is 100%, including spend-control semantics and an example company number. The tool description itself adds no parameter-level meaning beyond pointing to the data source, so the baseline of 3 applies.

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 states a specific verb ('Retrieves'), a clear resource ('official statutory corporate registration record from UK Companies House'), and lists concrete return fields. It is easily distinguished from sibling tools like get_fr_company_filing or get_uk_company_officers.

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?

It explicitly names target contexts: 'UK vendor due diligence, credit risk audits, and corporate KYB screening.' It does not mention exclusions or alternatives, but the use cases are clear enough to guide selection.

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

All Distil — Machine-Native Public Data Refinery toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation5/5

Each tool maps to a distinct data product: country-specific filings, tender records/search, risk scoring, sanctions screening, and entity graphs. The search/get split cleanly separates discovery from retrieval, and even similar country-specific tools are clearly qualified by region and entity type.

Naming Consistency5/5

All tools use a consistent snake_case verb_noun pattern: get_* for retrieval and search_* for discovery, with country or region qualifiers where applicable. Cross-cutting tools like get_corporate_risk_score and get_tender_intelligence fit the same pattern without feeling out of place.

Tool Count4/5

At 16 tools, the server is slightly above the ideal 3-15 range, but the multi-country, multi-domain scope justifies nearly every tool. The set is heavier than a tightly focused server, yet no tool feels redundant.

Completeness4/5

The set covers the core read-only workflow well: search to find entities or tenders, get to retrieve records, plus analytical tools for risk, sanctions, and tender intelligence. Minor gaps exist, such as no Spanish company registry access and no UK tender search, but these are workable and do not block primary due-diligence workflows.

Resources