Alimenter votre agent
La commande propre à l'éditeur. Ce n'est pas un endpoint PlugMCP.
Host a copy with our keys and logs.
npx -y [email protected]Claude Code n'a pas de lien d'installation pour une commande locale. Copiez la commande ci-dessous et exécutez-la dans un terminal.
claude mcp add context-mcp-server -- npx -y context-mcp-server@1.3.0À exécuter dans un terminal.
{ "mcpServers": { "context-mcp-server": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
URL distante ou commande sur votre machine ? Ce qui distingue les deux installations
Exécuter sur PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LancerDe l'éditeur
Persistent memory and codebase knowledge graph for AI coding assistants — delivered as a single MCP server.
One shared context store across Claude Code, VS Code Copilot, Google Antigravity (2.0 / IDE / CLI), Codex CLI, Hermes Agent, Claude.ai, and ChatGPT. Save context from one AI, pick it up in another.
Persistent AI memory + codebase knowledge graph MCP server. Works across Claude Code, VS Code Copilot, Google Antigravity, Codex CLI, Hermes Agent, Claude.ai, and ChatGPT.
Every conversation with an AI assistant starts from zero. The AI re-reads files it already read yesterday, re-discovers architecture it already understood, re-derives decisions that were already made. You repeat context. You paste the same background.
This gets worse as projects grow — reading 20 files to answer "what calls this function?" burns thousands of tokens every time.
~/.context-mcp/projects/<name>/ per-project on your machine; all AI tools read and write itReal measured reduction on this project: 162× fewer tokens, 99.38% reduction per conversation.
npm install -g context-mcp-server
Requires Node.js ≥ 18. Installs context-mcp, context-mcp-http, and the ctx CLI.
ContextGraph requires uv (Python runner). Memory tools work without it.
# macOS / Linux
curl -Ls https://astral.sh/uv/install.sh | sh
# Windows
winget install astral-sh.uv
Run from your project root:
ctx install --initial
This installs Node.js + Python (ContextGraph) dependencies. Run once after installing the npm package.
Then write MCP config + AI instruction files:
ctx install --all
To install for a specific platform only:
ctx install --claude # Claude Code
ctx install --vscode # VS Code Copilot
ctx install --antigravity # Google Antigravity (2.0 / IDE / CLI)
ctx install --codex # Codex CLI
ctx install --hermes # Hermes Agent
For Codex project installs, ctx install --codex writes:
.codex/config.toml with [mcp_servers.context-mcp] MCP configuration.AGENTS.md with Context-MCP usage rules for Codex..codex/hooks/ pre/post shell hook scripts for project-local Codex sessions.For web clients (Claude.ai, ChatGPT), start the HTTP server:
ctx online # start in background, prints OAuth credentials + URL
ctx online --restart # force restart
ctx online --port 3200 # different port
This repo is also a self-hosted Claude Code plugin marketplace — an alternative to ctx install --claude that doesn't require cloning or npm-installing anything yourself:
claude plugin marketplace add vibhasdutta/context-mcp
claude plugin install context-mcp@context-mcp-marketplace
or from inside a session: /plugin marketplace add vibhasdutta/context-mcp then /plugin install context-mcp@context-mcp-marketplace. This installs the context-mcp skill, the Bash pre/post-tool-use hooks, and registers the MCP server (still launched via npx context-mcp-server@latest) — everything ctx install --claude writes into ~/.claude/, bundled as one installable unit. ctx install --initial is still required once to install the ContextGraph Python environment.
Both ctx and context are aliases for the same CLI.
ctx # interactive mode (UI)
# Context
ctx list [project] # list entries by tree: graph / context / summary / plans
ctx projects # all projects with graph status + recent entries
ctx search "query" # keyword → semantic fallback search
ctx add # add entry interactively
ctx summary [project] # summarize recent entries
# Delete
ctx delete <id-prefix> # delete one entry
ctx delete project <name> # delete all entries for a project
# Server
ctx online # start HTTP server (idempotent)
ctx online --restart # force stop + restart
ctx settings # view and edit config interactively
# Install
ctx install --initial # install / update Node.js + Python deps
ctx install --all # write config + rules for all platforms
File and git tools are sandboxed to your project root. Pass rootPath when calling context.resume:
{ "action": "resume", "project": "my-app", "rootPath": "/home/user/my-app" }
Any file or git operation outside that directory is rejected. Applies to all HTTP-connected clients.
context.resume — loads recent entries, active plans, and graph status; registers rootPath for sandboxingcontext.save — store context as note (or compaction for session summaries); categorize with free-form tagscontext.get / context.update / context.delete — full CRUD, single or batchsearch — keyword-first, semantic fallbackplan — auto-triggered when AI makes any plan; saves a markdown summary to a planDir you specifysummary.jsonAlso called CodeGraph. MCP tools use the
codegraph_*prefix — both names mean the same thing.
Step 1 — Build (once per project, runs locally, no API cost):
codegraph_build(path)
Parses codebase via tree-sitter AST (16 languages, regex fallback). Extracts functions, classes, imports, call edges, and inheritance. Every node carries a full enriched schema: signature, params, return_type, docstring, side_effect, exported, complexity, last_modified. PageRank scores all nodes by connectivity. Metadata saved to <project>/codegraph-cache/.
Step 2 — Query (instant, forever):
codegraph_arch(path, limit?) → module map: every file, its exports, its imports
codegraph_query(path, question?, node?) → structural question OR single-node lookup (or both)
codegraph_nodes(path, type, token_budget?) → all nodes of a type, sorted by PageRank
codegraph_filter(path, node_type?, exported?, → predicate filter: side_effect, return_type,
side_effect?, return_type?, called_by?, called_by, file_pattern — rank-sorted output
calls?, file_pattern?, token_budget?)
codegraph_report(path) → god nodes, clusters, surprising connections
codegraph_affected(path, node, depth?) → BFS blast radius — what breaks if you change X?
codegraph_query accepts question (natural language), node (exact/partial name), or both. codegraph_filter answers property questions ("which functions have side effects?", "all exported async handlers") without reading any files. Pass token_budget to any tool to get the highest-rank results within a token limit.
What's in each node (v1.2+):
Step 3 — Visualize (auto-generated on every build):
codegraph_html(path, formats?) → regenerate visualizations on demand
Every codegraph_build automatically writes to <project>/codegraph-cache/:
graph.html — interactive vis.js force graph (dark theme, search, community toggle)tree.html — D3 collapsible file hierarchycallflow.html — Mermaid architecture diagrams per communitygraph.graphml — Gephi / yEd exportobsidian/ — per-node .md vault with [[wikilinks]]Available to HTTP-connected clients (Claude.ai, ChatGPT). Local AI clients use their native IDE tools.
read_file, write_file, patch_file, create_dir, list_dir, delete_filegit_status, git_diff, git_log, git_add, git_commit, git_push, git_pull, git_branch, git_stash, git_reset, git_showEnable git tools with --access-git flag or access_git: true in config.
context-mcp [--data-dir <path>]
context-mcp-http [--port <number>] [--host <string>] [--access-git] [--data-dir <path>]
Default port: 3100. Default data dir: ~/.context-mcp.
~/.context-mcp/contextconfig.json — auto-created on first run:
Edit with ctx settings.
MIT
Persistent AI memory + codebase knowledge graph MCP server. Works across Claude Code, VS Code Copilot, Google Antigravity, Codex CLI, Hermes Agent, Claude.ai, and ChatGPT.
{ "mcpServers": { "context-mcp-server": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}Outils
Pas encore de liste d'outils. Les serveurs distants en obtiennent une après le premier contrôle d'état.
FAQ
Copiez sur cette page la configuration stdio pour Cursor, Claude Desktop, Claude Code ou VS Code. Elle utilise l'installation de l'éditeur, pas un proxy PlugMCP.
Aucune variable d'environnement requise n'est indiquée sur cette fiche.
Context s'exécute en local via stdio.
Dernière vérification : 2026-11-15. Persistent AI memory + codebase knowledge graph MCP server. Works across Claude Code, VS Code Copilot, Google Antigravity, Codex CLI, Hermes Agent, Claude.ai, and ChatGPT.