How it works
Question → agent → DataGardener MCP → evidence → answer.
How an AI agent uses DataGardener MCP: it understands the question, selects intelligence tools, queries entitled modules, connects evidence on company registration number and returns an answer with source and date.
- 1
The host sends the question and the tool list to the model
When a person asks a question in an MCP host such as Claude, the host includes the DataGardener MCP tool definitions in the model's context. The model now knows which tools exist, what arguments they take and what they return.
- 2
The model plans
It decomposes the question into conditions (geography, sector, financial signal, contract history) and maps each to the lightest tool that can evidence it. Describe, count and knowledge-base tools are used first so scope is known before records are pulled.
- 3
The host calls DataGardener MCP
Each tool call travels to the DataGardener MCP server over the authorised OAuth connection. Identity is bound to the connection, so the server applies that customer's entitlement and default filters, for example excluding dissolved companies.
- 4
DataGardener returns structured results
Records come back with their fields, the company registration number, and where applicable the source, source date and usage consumed. Errors come back as readable messages the model can act on.
- 5
The model connects the evidence
Results from different modules are joined on registration number. A company that fails a condition drops out with the reason recorded. The model may call more tools if a gap appears.
- 6
The answer, with its working
The person receives a direct answer, the supporting records or aggregates, the provenance chips and suggested next questions. Nothing is asserted that the evidence does not support.
What the person sees at the end
A short answer, the records, and this:
Watch the stages
The same five stages, animated.
Choose a question
Illustrative output. Real tool names.
Question
Find engineering SMEs in the West Midlands that are growing, have won public-sector work and may need additional working capital.
Stage 1 of 5
Understanding the request
The agent breaks the question into the conditions it must satisfy.
- Geography: West Midlands
- Sector: engineering
- Size: SME
- Growth signal
- Public contracts
- Working-capital signal
Each condition maps to one or more DataGardener intelligence layers. Nothing is guessed: if a condition cannot be evidenced, the agent says so.
- Company Intelligence
- Financial Intelligence
- Procurement
- Lending
- Risk
Read this investigation as text
Question: Find engineering SMEs in the West Midlands that are growing, have won public-sector work and may need additional working capital.
Intent: Geography: West Midlands; Sector: engineering; Size: SME; Growth signal; Public contracts; Working-capital signal.
Tools selected: search_companies, execute_ukgaap_query, search_contracts, execute_query.
Modules queried: Company index (Engineering SIC codes, West Midlands, live, SME size bands: 1,284 candidates); UK GAAP financials (Turnover up over two periods, cash down or trade debtors rising: 216 match); Contract Finder index (Awarded supplier in last 24 months, any buyer: 41 match); Company index (No new charge in 36 months, no insolvency event: 27 shortlisted).
Evidence connected: Turnover growth and debtor build-up from two consecutive filings; Contract award notice with buyer and value; Charge history showing age of last secured facility; Company status, incorporation date and SIC description.
Answer: 27 engineering SMEs in the West Midlands match all four conditions. The strongest signal set combines two years of turnover growth with rising trade debtors, a public-sector award in the last 24 months, and a last registered charge older than three years.
Provenance: Source Companies House filings; Contracts Finder; date Filed accounts to 2025; awards to 2026; module Company, Financial, Procurement, Lending; usage 27 company records.
Suggested next questions: Which of these have a director or PSC change in the last 12 months? / Which lenders hold the existing charges? / Who is the finance contact at the top ten?
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Built on DataGardener intelligence already used by leading UK organisations.