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Cortex

by gzoonet

Indexes project files into a local knowledge graph and exposes natural-language queries through a CLI, dashboard, and four MCP tools.

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Install Cortex

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "gzoonet-cortex": {
      "command": "npx",
      "args": [
        "-y",
        "@gzoo/cortex"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y @gzoo/cortex

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

gzoonet/cortex MCP server connects an agent to a local knowledge graph built from watched project files, including Markdown, TypeScript, JavaScript, Python, JSON, and YAML. It extracts entities and relationships with configurable cloud or local LLM providers, stores data locally, and exposes getstatus, listprojects, findentity, and querycortex. Reach for it when you need to search decisions, dependencies, patterns, or constraints across multiple codebases while retaining local control over project data.

Use cases

  • Search architecture decisions across multiple repositories
  • Track dependencies and components from project files
  • Detect contradictory decisions between projects
  • Query indexed codebase context with source citations

Key features

  • Local knowledge graph for project files
  • LLM-based entity and relationship extraction
  • Natural-language graph queries with citations
  • Optional keyword and vector search
  • Cloud, hybrid, and local LLM routing
  • CLI and web dashboard

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

17/17 checks healthy over the last 77d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 8d ago via OSV.dev · @gzoo/cortex (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars24Stargazers on the source repository.
npm downloads90Package downloads in the last 30 days.
Last commit27d agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How Cortex works

What gzoonet/cortex MCP server does

gzoonet/cortex MCP server builds a local knowledge graph from developer project files. It watches configured directories, processes supported file types, and extracts entities such as architecture decisions, components, dependencies, constraints, patterns, and action items. It also infers relationships across entities and projects, identifies contradictions and duplicates, and supports natural-language queries with source citations.

The project includes a CLI and a web dashboard. The dashboard provides a graph visualization, live ingestion feed, and query explorer. The CLI can register projects, ingest existing files, query the graph, search for entities, and list contradictions. The MCP integration exposes four tools: getstatus, listprojects, findentity, and querycortex.

How it works

For each file change, the processing pipeline parses content into chunks, uses an LLM to extract entities, relates new entities to existing ones, checks for conflicts and duplicates, and stores the results. SQLite holds graph data, while LanceDB stores vectors. Queries combine graph information with natural-language synthesis. Optional semantic search blends keyword matching with embedding similarity; embeddings are configured separately from the chat model and are disabled by default.

All stored data remains under ~/.cortex/. LLM requests can leave the machine when a cloud provider is configured, except for restricted projects, which are kept local. Routing modes include cloud-first, hybrid, local-first, and local-only. Hybrid and local modes require Ollama; cloud modes can use Anthropic, Google Gemini, DeepSeek, Groq, OpenRouter, or another OpenAI-compatible endpoint.

Setup and configuration

Install the CLI globally with npm install -g @gzoo/cortex, then run cortex init to configure an LLM provider, credentials, routing mode, watched directories, and a monthly budget. cortex doctor checks configuration, providers, and the database. Global settings are written to ~/.cortex/cortex.config.json, while API keys are stored in ~/.cortex/.env.

Register directories with cortex projects add, backfill existing files with cortex ingest, and start ongoing processing with cortex serve or cortex watch. These modes should not run at the same time because they compete for file changes. cortex serve also starts the dashboard and API, normally at http://localhost:3710.

Configuration can come from built-in defaults, the global file, an optional project-level cortex.config.json, and CORTEX_* environment variables. Exclusion rules and restricted paths are managed through CLI commands.

Tools and capabilities

The gzoonet/cortex MCP server provides:

  • getstatus for checking the system state.
  • listprojects for listing registered projects.
  • findentity for locating entities in the graph.
  • querycortex for asking natural-language questions against the indexed knowledge.

The broader application also supports contradiction reports, entity expansion, source citations, semantic search, project watching, and dashboard-based graph exploration.

Limitations and notes

Node.js 20 or newer is required. Cloud routing needs an API key for the selected provider, while local-only operation requires Ollama. Semantic search needs a separately configured embeddings provider and is not enabled by default. Initial existing content requires an explicit ingest because the watcher does not re-ingest all files when it starts.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Cortex

Install the package globally with `npm install -g @gzoo/cortex`, then run `cortex init` to configure it.

It provides `getstatus`, `listprojects`, `findentity`, and `querycortex`.

Cloud routing requires a provider API key. Local-only routing uses Ollama instead and does not require a cloud API key.

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