INDIVIDUAL MCP TOOL

get_ruleset

The qualification ruleset in force: every criterion, its threshold, the evidence type behind it, what an unknown value means, and the limits of the verdict.

fairdata.ainone authenticationAvailability not checked

LIVE ENDPOINT

https://fairdata.ai/api/mcp

No auth detected

Connect to this endpoint to inspect the live schema for get_ruleset and invoke it with your own arguments.

Indexed input schema

{}

Risk classification

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

Parent server

fairdata.ai

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.fairdata-ai]
url = "https://fairdata.ai/api/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "fairdata-ai": {
      "type": "http",
      "url": "https://fairdata.ai/api/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: fairdata-ai
Remote MCP URL: https://fairdata.ai/api/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "fairdata-ai": {
      "url": "https://fairdata.ai/api/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "fairdata-ai": {
      "type": "http",
      "url": "https://fairdata.ai/api/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "fairdata-ai",
  "transport": "streamable-http",
  "url": "https://fairdata.ai/api/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

Related tools

search_collections — Find themed collections of research datasets.
get_collection — One collection in full: what it is, what defines membership, its aggregates, its licence position, its freshness, and where to fetch it in each available format.
compare_collections — Compare two to five collections on the dimensions that decide whether one is usable for a task: size, licence clarity and mix, FAIR and curation medians, file accessibility, assessment freshness, and modality coverage.
list_collection_datasets — The member datasets of a collection, with licence, scores, file counts and links.
get_collection_manifest — The versioned manifest for a collection — the artefact to pin a workflow to.
explain_collection_qualification — The full criteria matrix for a collection: each criterion, its threshold, the observed value, pass/fail/unknown, and the evidence with its evaluator and version.
search_datasets_in_corpus — Keyword search over the datasets that clear the Machine-Ready gate.
get_dataset — One dataset from the qualifying corpus: metadata, licence, scores, files, the collections it belongs to, and its qualification result.