MCP for economic development and local economic intelligence

Understand a local economy without weeks of research.

DataGardener MCP lets an economic-development team's AI agent aggregate UK company intelligence by region, county, local authority, constituency or postcode district: business population, sector mix, growth, incorporations, exporters and public-contract wins. Figures come with sources and a methodology note so they can be used in strategy documents.

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Trust proof for economic development buyers

Logos indicate organisations using the DataGardener platform, as shown on datagardener.com. They do not indicate use of DataGardener MCP.

The problem

Where the research goes today.

Official statistics lag the economy
Company filings, incorporations and contract awards show change months before aggregate statistics do.
Sector mapping is manual
Building a picture of a local cluster means exporting, deduplicating and classifying company lists by hand.
Inward-investment research is fragmented
Ownership, group structure and exporter status live in different places.

Example questions

Ask it the way you would ask an analyst.

“Show me high-growth digital businesses in Croydon founded during the last five years.”
“How many manufacturing SMEs in this constituency employ more than 20 people?”
“Which local companies are foreign-owned, and from where?”
“Which businesses here have won public contracts outside the region?”
“Compare incorporations and dissolutions across three neighbouring districts.”

Intelligence layers needed

The layers an agent draws on for economic development work.

Example MCP workflow

How the investigation runs.

1. Ask
An officer asks for a sector, growth or population view of a place.
2. Aggregate
The agent uses count and aggregation queries first so most answers consume no records.
3. Drill
Where detail is needed, the agent retrieves specific companies with their evidence.
4. Brief
The result is a briefing with methodology, period and sources.

Choose a question

Question

Show me high-growth digital businesses in Croydon founded during the last five years.

Stage 1 of 5

Understanding the request

The agent breaks the question into the conditions it must satisfy.

Geography: Croydon
Sector: digital
Age: under 5 years
Growth

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
Local and Regional Economy
People and Directors

Read this investigation as text

Question: Show me high-growth digital businesses in Croydon founded during the last five years.

Intent: Geography: Croydon; Sector: digital; Age: under 5 years; Growth.

Tools selected: execute_query, execute_accountsdata_query.

Modules queried: Company index (Digital and software SIC codes, Croydon postcodes, incorporated since 2021: 1,058 companies); Accounts data index (Employee growth or turnover growth above threshold between filings: 97 match); Company index (Aggregate by ward and SIC group: Sector map).

Evidence connected: Incorporation dates; Employee counts across filings; Ward-level aggregation.

Answer: 97 digital businesses founded in Croydon since 2021 show measurable growth in employees or turnover. Software development and IT consultancy account for 61 of them, concentrated in three wards.

Provenance: Source Companies House; filed accounts; date Filings to 2025; module Company, Financial, Economy; usage 97 company records.

Suggested next questions: Which have raised investment? / Which are exporting? / Compare with Sutton and Bromley

Every answer arrives with its provenance

SourceNamed register or filingDateDate of the underlying documentModuleEconomy, Company, FinancialCRNRegistration numberUsageReported per call

Time-saving logic

Report-building time

Economic-development teams produce recurring briefings for members, LEPs, combined authorities and investors. An agent that aggregates and cites can take most of the data gathering off that cycle.

Estimated outcome

Monthly research hours today: 162
Hours potentially recovered per month: 97
Annual capacity recovered: 1,169 h; about 0.7 full-time equivalents
Estimated productivity value per year: £52,609

This is an estimate computed only from the assumptions you enter, using 4.33 weeks per month and 1,650 working hours per full-time equivalent. It does not include DataGardener MCP costs and is not a guarantee of savings. Validate the acceleration share with a pilot before relying on it.

Relevant proof

Evidence and illustrative workflows.

All case studies

Illustrative MCP workflow

Local economy briefing for an economic-development team

Illustrative workflow · Economic development

An economic-development officer asks for the business population of a constituency by sector, growth and public-contract wins over five years. The agent aggregates rather than pulls records, returns a briefing with methodology, and suggests which SMEs to invite to a market-engagement event.

instead of weeks of research: Minutes
included with every figure: Methodology

Economy
Company
Financial
Procurement

Illustrative workflow. Not a customer claim.

Economic development: frequently asked questions

Which geographies can the agent aggregate by?

Region, county, local authority, parliamentary constituency and postcode district are supported through DataGardener's company index fields and aggregation queries.

Is the data current?

DataGardener states its platform is updated daily. Each record carries the date of the underlying filing or notice.

Can we publish figures from the agent?

Figures can be used in reports with the methodology and source notes the agent returns. Check your DataGardener agreement for publication terms.

Your agents already know how to think.Give them something worth knowing.

Built on DataGardener intelligence already used by leading UK organisations.

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