Quickstart

Get access

Graphomics is provisioned per organization. Request access and you'll receive:

your API base URL and a MCP endpoint,
an API key (sent to you securely), and
your organization's initial roles and members.

There is no self-serve signup — access is granted and scoped to your organization. Keep your API key secret; treat it like a password.

Everything you connect to is issued by us when your organization is provisioned — there is no shared public endpoint. The examples in these docs use $GRAPHOMICS_API_URL (your REST base URL, ending in /api/v1), $GRAPHOMICS_MCP_URL (your MCP endpoint), and $GRAPHOMICS_API_KEY; substitute the values you were given.

Connect over MCP

Every core Graphomics capability is exposed as Model Context Protocol tools over streamable HTTP. Point an MCP-capable client — Claude Code, an agent framework, or your own MCP client — at your Graphomics MCP endpoint and pass your credential.

Example MCP client configuration (Claude Code / Claude Desktop style):

json

{
  "mcpServers": {
    "graphomics": {
      "type": "http",
      "url": "${GRAPHOMICS_MCP_URL}",
      "headers": {
        "Authorization": "Bearer ${GRAPHOMICS_TOKEN}"
      }
    }
  }
}

Once connected, your agent can call tools directly — for example, ground a question against the graph, then run a pipeline on the result. See the MCP tool reference for the full catalog.

Query the knowledge graph over REST

Prefer plain HTTP? The knowledge graph is a REST API. Authenticate with the X-API-Key header:

bash

curl -s $GRAPHOMICS_API_URL/diseases/search/parkinson \
  -H "X-API-Key: $GRAPHOMICS_API_KEY"

Find the microbes associated with a disease:

bash

curl -s "$GRAPHOMICS_API_URL/diseases/{disease_id}/taxa?limit=25" \
  -H "X-API-Key: $GRAPHOMICS_API_KEY"

See the API reference for authentication, conventions, and the full endpoint catalog.

A typical grounded workflow

Ground — search the graph for the entities relevant to your question (disease, taxa, metabolites).
Execute — assemble and run a Workbench pipeline on your data (e.g. diversity + differential abundance).
Contribute — write findings back into the graph as Assertions, linked to the run that produced them.
Trace — query the lineage of any entity to see the experiments, analyses, and decisions behind it.