SoluCortex MCP

Official Model Context Protocol server for SoluCortex — living technical memory for AI agents.

Connect any MCP-compatible agent (Claude Code, Claude Desktop, Cursor, Codex, Cline, …) to your SoluCortex project so it can recall the decisions, conventions, risks and architecture that matter before it works, and remember what it learns when it's done.

Website: solucortex.ai · Setup guide: solucortex.ai/docs/mcp · Tools reference: solucortex.ai/docs/mcp-tools · PyPI: solucortex-mcp · MCP Registry: io.github.soluai-spa/solucortex-mcp

SoluCortex MCP demo: recall approved context at task start, remember learnings at close

Tools

Tool	What it does	When to use
`solucortex_recall`	Builds living context for a task (ranked by semantic similarity + importance)	At the start of a task, before touching code
`solucortex_search`	Ad-hoc semantic search over the project's memories	Specific questions mid-task
`solucortex_remember`	Records a memory (stored `approved` + traced as an authorized agent)	At close, or on a relevant technical decision
`solucortex_update_memory`	Corrects an existing memory — the edit returns it to pending for human re-approval in the panel	When a memory is right in spirit but wrong in detail
`solucortex_flag_memory`	Flags a memory as outdated/incorrect with a reason; a human resolves it in the panel	When a memory should be reviewed, archived or deleted
`solucortex_list_memories`	Lists memories without semantic search	Quick inspection / audit

Governance by design: agents propose, humans govern. Agents can never change a memory's status, rewrite its metadata, or delete it — edits go back to pending and deletions only happen from the SoluCortex panel.

Requirements

A SoluCortex account and a project API key (prefix scx_) — get it from your SoluCortex dashboard.
One of: uv (recommended), Python ≥ 3.10, or Docker.

Configuration

stdio mode (default, local)

The server is configured entirely through environment variables:

Variable	Required	Description
`SOLUCORTEX_API_KEY`	✅	Project API key (`scx_…`)
`SOLUCORTEX_PROJECT_ID`	optional	Default project UUID; if omitted, the backend infers it from the API key
`SOLUCORTEX_URL`	optional	API base URL. Default `https://solucortex.ai`

HTTP mode (remote, multi-tenant)

Run with MCP_TRANSPORT=http (or --http) to serve Streamable HTTP on $PORT (default 8080) — the mode behind https://mcp.solucortex.ai. Credentials travel with each request and the environment is ignored:

Header	Required	Description
`Authorization: Bearer scx_…`	✅	The caller's project API key (401 without it)
`X-Solucortex-Project`	optional	Default project UUID; if omitted, the backend infers it from the API key

GET /health (and /healthz locally; Cloud Run's frontend intercepts /healthz) responds without auth. The MCP endpoint is /mcp, runs stateless, and shares nothing between requests/tenants.

Never commit your API key. Keep it in your MCP client config's env block or a local .env (see .env.example).

Install

Remote (recommended — nothing to install)

The hosted server at https://mcp.solucortex.ai/mcp speaks Streamable HTTP; your key travels with each request:

Terminal

claude mcp add --transport http solucortex https://mcp.solucortex.ai/mcp \
  --header "Authorization: Bearer scx_xxx" \
  --header "X-Solucortex-Project: your-project-uuid"

Or in any client with remote MCP support:

config.json

{
  "mcpServers": {
    "solucortex": {
      "type": "http",
      "url": "https://mcp.solucortex.ai/mcp",
      "headers": {
        "Authorization": "Bearer scx_xxx",
        "X-Solucortex-Project": "your-project-uuid"
      }
    }
  }
}

Claude Code (local, stdio)

Terminal

claude mcp add solucortex \
  -e SOLUCORTEX_API_KEY=scx_xxx \
  -e SOLUCORTEX_PROJECT_ID=your-project-uuid \
  -- uvx solucortex-mcp

Claude Desktop / Cursor / Cline (JSON config)

Add to the client's MCP config (claude_desktop_config.json, Cursor mcp.json, etc.):

config.json

{
  "mcpServers": {
    "solucortex": {
      "command": "uvx",
      "args": ["solucortex-mcp"],
      "env": {
        "SOLUCORTEX_API_KEY": "scx_xxx",
        "SOLUCORTEX_PROJECT_ID": "your-project-uuid"
      }
    }
  }
}

From a local clone

bash

git clone https://github.com/soluai-spa/solucortex-mcp
cd solucortex-mcp
cp .env.example .env   # fill in your key
./run.sh               # loads .env, then runs via uv
# or, with SOLUCORTEX_* already exported: uv run solucortex-mcp

Docker

Prebuilt image on GHCR:

Terminal

docker run --rm -i \
  -e SOLUCORTEX_API_KEY=scx_xxx \
  ghcr.io/soluai-spa/solucortex-mcp:latest

Or build it yourself:

Terminal

docker build -t solucortex-mcp .
docker run --rm -i \
  -e SOLUCORTEX_API_KEY=scx_xxx \
  -e SOLUCORTEX_PROJECT_ID=your-project-uuid \
  solucortex-mcp

The server speaks MCP over stdio, so clients launch it as a subprocess (-i keeps stdin open).

Development

bash

uv sync
uv run solucortex-mcp            # run (stdio)
MCP_TRANSPORT=http uv run solucortex-mcp   # run (HTTP on :8080)
uv run pytest                    # test suite
npx @modelcontextprotocol/inspector uv run solucortex-mcp   # interactive test

Notes

Memory type vocabulary: the canonical set is architecture, decision, risk, convention, bug_history, tech_debt, sensitive_module, learning, external_integration. Some backends accept an older set (technical_decision, historical_bug, current_state, task_closure). The server passes type through and surfaces HTTP 422 so you can retry with the other set.
Never store real secrets in a memory. Record location, type, severity and action taken instead.

License

MIT — see LICENSE.