Developers · Model Context Protocol

Ask MESSAI from your AI assistant

MESSAI is a Model Context Protocol (MCP) server. Connect it once and your assistant can search 19,000+ papers on microbial electrochemical systems, read literature ranges and priors, check where papers disagree and predict reactor performance, citing the papers behind every number.

Server URL

https://www.messai.io/api/mcp

Use the www address. The bare messai.io domain redirects, and some clients do not follow a redirect on a POST request.

Connect in a minute

Claude (claude.ai and the desktop app)

Settings → Connectors → Add custom connector. Name it MESSAI and paste the server URL. Custom connectors are available on Claude plans that support them.

Claude Desktop (config file)

Add this to claude_desktop_config.json, then quit and reopen Claude. It uses the mcp-remote bridge (Node.js required).

{
  "mcpServers": {
    "messai": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://www.messai.io/api/mcp"
      ]
    }
  }
}

Cursor

Use the one-click install, or add this to ~/.cursor/mcp.json:

Add to Cursor

{
  "mcpServers": {
    "messai": {
      "url": "https://www.messai.io/api/mcp"
    }
  }
}

VS Code

Add this to .vscode/mcp.json:

{
  "servers": {
    "messai": {
      "type": "http",
      "url": "https://www.messai.io/api/mcp"
    }
  }
}

Other MCP clients

Add a remote (Streamable HTTP) server with the URL above. Clients that only speak stdio can wrap it with: npx -y mcp-remote <URL>.

Try asking

What is the typical coulombic efficiency of microbial electrolysis cells? Give n and the range.
Benchmark my MFC: carbon-cloth anode, 25 cm², 28 mL, pH 7, 30 °C. Where does it sit in the literature?
Where do papers disagree on internal resistance, and why?
I measured coulombic efficiencies of 0.42, 0.47 and 0.51 in my MEC. How does that change the literature estimate?
Which open research gaps around nitrogen recovery are under-studied?

What it can do

Papers: search the corpus; open a paper by id or DOI with its extracted values and source snippets: search_papers · get_paper
Parameters: find a parameter, its literature distribution per SI unit, the source papers, the Bayesian prior, contradictions, and what it is linked to: list_parameters · get_parameter_distribution · get_parameter_sources · get_prior · get_contradictions · get_parameter_relationships
Modelling: predict reactor performance with calibrated intervals, update a prior with your own measurements, export priors as a dataset: predict_performance · update_with_my_data · export_priors
Discovery: the closest published studies to your conditions; ranked open research gaps: find_similar_studies · find_research_gaps
Reference: electroactive microbes; electrode and membrane materials: lookup_microbes · lookup_materials

Ready-made workflows

Clients that support MCP prompts offer three workflows: benchmark my reactor, review a parameter, and find a research direction.