← fairdata.ai

INDIVIDUAL MCP TOOL

get_linked_papers

Return the publications linked to a dataset — papers that cite, supplement, or document it.

fairdata.ainone authenticationAvailability not checked

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 endpoint

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

  • get_record — Retrieve the FAIRdata.ai enriched record for a research dataset DOI.
  • get_record_format — Get a FAIRdata.ai enriched record serialised in a specific machine-actionable format.
  • assess_dataset — Run a full FAIR and AI-readiness assessment for a research dataset by DOI.
  • list_records — Browse the FAIRdata.ai record registry.
  • get_ai_readiness — Get a detailed AI-readiness assessment for a dataset against the GDS/DSIT 4-pillar framework ('Making Government Datasets Ready for AI', January 2026).
  • get_files — Get the direct, downloadable files for a dataset — real contentUrl + size + checksum per file, resolved from the source repository.
  • get_data_quality — Get a schema-level data-quality grade (0-100) for a dataset's tabular files: completeness (non-null), structure (non-constant columns), and size.
  • find_training_data — Find AI-ready datasets for model training/evaluation by theme.