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solucortex-mcp

solucortex-mcp

Remote
github.com/soluai-spaMemory & Knowledge0Updated 3w ago
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About solucortex-mcp

solucortex-mcp connects AI agents to SoluCortex project memory, helping them recall decisions, conventions, risks, and architecture and record new learnings. Agents can propose and correct memories, while humans retain approval and deletion control.

Key features

  1. 01Recall task-relevant project context
  2. 02Search memories semantically
  3. 03Record and update memories
  4. 04Flag outdated or incorrect entries
  5. 05List memories for inspection

Use cases

  1. 01Load project context before coding
  2. 02Find a remembered decision during a task
  3. 03Record a technical finding at task close
  4. 04Flag a memory for human review

MCP support

The server exposes six memory tools over stdio or Streamable HTTP. It connects to a SoluCortex project using a project API key; memory status and deletion remain under human control.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.solucortex.ai/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://mcp.solucortex.ai/mcpAuth required
Packages
pypisolucortex-mcp1.1.2

Tools

6
ParameterTypeRequiredDescription
querystringYesDescribe the current task/module in natural language, e.g. 'implement API key rotation in the secrets module'. Used to semantically retrieve the most relevant memories.
project_idanyNoProject UUID. If omitted, the session default applies, else the backend infers it from the API key.

Details

From the project README

SoluCortex MCP

PyPI CI Python License: MIT

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

ToolWhat it doesWhen to use
solucortex_recallBuilds living context for a task (ranked by semantic similarity + importance)At the start of a task, before touching code
solucortex_searchAd-hoc semantic search over the project's memoriesSpecific questions mid-task
solucortex_rememberRecords a memory (stored approved + traced as an authorized agent)At close, or on a relevant technical decision
solucortex_update_memoryCorrects an existing memory — the edit returns it to pending for human re-approval in the panelWhen a memory is right in spirit but wrong in detail
solucortex_flag_memoryFlags a memory as outdated/incorrect with a reason; a human resolves it in the panelWhen a memory should be reviewed, archived or deleted
solucortex_list_memoriesLists memories without semantic searchQuick 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:

VariableRequiredDescription
SOLUCORTEX_API_KEY✅Project API key (scx_…)
SOLUCORTEX_PROJECT_IDoptionalDefault project UUID; if omitted, the backend infers it from the API key
SOLUCORTEX_URLoptionalAPI 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:

HeaderRequiredDescription
Authorization: Bearer scx_…✅The caller's project API key (401 without it)
X-Solucortex-ProjectoptionalDefault 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:

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:

{
  "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)
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.):

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

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

Or build it yourself:

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).

Instructions for your agent (AGENTS.md)

Once connected, give your agent the methodology too: copy templates/AGENTS.md into the root of your repo (it works as AGENTS.md or CLAUDE.md). The MCP handshake already carries these instructions — the file lets you version them alongside your code, extend them with your own conventions, and optionally enable the commented Claude Code hook recipe for guaranteed recall.

Development

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.

Comments

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