About Arquestra Audit
Arquestra Audit is a remote MCP server that guides assistants through checking data files before results are published. Measurement runs in the browser or locally; the server receives only counts, and results appear as red, amber, or green indicators with reasons.
Key features
- 01Column interpretation and audit setup
- 02In-chat browser measurement panel
- 03Local Python measurement script
- 04Per-figure status lights and reasons
- 05Worked audit examples
Use cases
- 01Checking data before publishing results
- 02Measuring files in a browser without uploading them
- 03Running checks locally with a script
MCP support
It is a remote MCP server at the listed Streamable HTTP endpoint. Its tools guide audit setup, measurement, and display of results.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://arquestra.dev/api/mcp/auditclaude mcp add --transport http pablo-ramossanchez-arquestra-audit https://arquestra.dev/api/mcp/audit{"mcpServers":{"pablo-ramossanchez-arquestra-audit":{"type":"http","url":"https://arquestra.dev/api/mcp/audit"}}}Open in Cursor{"servers":{"pablo-ramossanchez-arquestra-audit":{"type":"http","url":"https://arquestra.dev/api/mcp/audit"}}}Open in VS Code{"mcpServers":{"pablo-ramossanchez-arquestra-audit":{"type":"http","url":"https://arquestra.dev/api/mcp/audit"}}}{"mcpServers":{"pablo-ramossanchez-arquestra-audit":{"serverUrl":"https://arquestra.dev/api/mcp/audit"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEArquestra Audit · MCP server
Check your own data before you publish a result from it. Arquestra Audit is a remote MCP server by Arquestra that walks your AI assistant through an audit of a data file: it agrees with you how to read each column, measures the file where it is (in the chat's audit panel or with a small script on your computer), and shows one light per figure, red, amber or green, with the reasons.
Your data never reaches the server and nothing is stored. The measuring runs in your browser or on your computer; only counts come back.
- Endpoint:
https://arquestra.dev/api/mcp/audit(Streamable HTTP, no sign-in) - Page: arquestra.dev/assets/audit
Connect it
Claude (claude.ai, desktop, mobile): open Claude's connectors, search Arquestra Audit and press Connect. Not in the list yet? Add it directly.
Claude Code:
claude mcp add --transport http arquestra-audit https://arquestra.dev/api/mcp/audit
or install this repository as a plugin (Manage plugins → Install by GitHub URL → https://github.com/pablo-ramossanchez/arquestra-audit-mcp).
VS Code: install
Cursor: install
ChatGPT: settings → connectors (developer mode) → add https://arquestra.dev/api/mcp/audit.
Any other MCP client: add a remote server with the URL above.
Tools
| Tool | What it does |
|---|---|
start_audit | Proposes how to read your file (the role of each column, each result written as a claim) from its column names and the results you want to publish. |
measure_in_app | Opens the audit panel in the chat; the panel reads your file in your browser and measures it with the confirmed reading. |
prepare_script | Returns measure.py, a fixed Python script (standard library, no network) that counts what each check needs and prints counts, never rows. |
show_audit | Shows the lights: one per figure, with the reasons and a small chart. |
example_audits | How the audit works, with worked examples. |
About Arquestra
Arquestra builds open, checkable data assets on public procurement and AI governance: the Consulting Market Radar (public consulting tenders in Europe and who wins them), the AI Procurement Observatory, research papers, and this audit.
Earlier versions of this repository held a plugin for a scientific-computing product; that product is now listed as Arquestra Chem on arquestra.dev.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.