Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Local memory and context retrieval for AI agents through CueMap.
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Pick the AI app you use, then copy the settings.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"cuemap": {
"command": "npx",
"args": [
"-y",
"cuemap-mcp"
]
}
}
}Run this in your terminal
claude mcp add cuemap -- npx -y cuemap-mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"cuemap": {
"command": "npx",
"args": [
"-y",
"cuemap-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"cuemap": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"cuemap-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
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Runs on your computer and uses local settings instead
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.7.5) reached the registry 21 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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CueMap MCP Server v0.7.5
A premium MCP bridge for explainable, repository-aware agent memory.
The Model Context Protocol (MCP) server for CueMap, allowing AI coding assistants (like Claude Desktop, Cursor, Windsurf, and Antigravity) to instantly recall codebase context using the CueMap engine.
For agent-facing operating guidance—including repository consent, supported content types, recall modes, accuracy-oriented knob selection, and troubleshooting—see SKILL.md.
The CueMap MCP Server is designed to work completely out-of-the-box. When started, it automatically manages a high-performance Rust instance of the CueMap Server in the background. The v0.7.3 engine bundles qint8 MiniLM-L3 by default and q4 MiniLM-L3 for the edge profile; no model download occurs at runtime.
You do not need to install or run the CueMap CLI manually. The correct pre-compiled binary for your operating system is automatically downloaded via optional NPM dependencies. Embedded startup supports Linux x64/ARM64, macOS x64/ARM64, and Windows x64.
# Global installation makes the `cuemap-mcp` command available
npm install -g cuemap-mcp
*(Note: Ensure your package manager is configured to download optionalDependencies so the local Rust binary is included).*
CueMap also ships a separate cuemap-agent-plugin package for Agent Plugins-compatible clients. It bundles the portable plugin.json manifest, stdio mcp.json configuration, and a repository-memory skill. The plugin launches this MCP server at the matching release version and must be installed or downloaded separately from cuemap-mcp.
By default, the embedded engine runs on port 8735. You can customize the server behavior by passing the following environment variables in your MCP configuration:
CUEMAP_PORT: Override the port the embedded engine binds to (default: 8735).CUEMAP_CONFIG_PATH: Absolute path to a custom server_config.toml to configure advanced Engine tuning, background jobs, and RAG search parameters.CUEMAP_URL: If you prefer to bypass the embedded engine and connect to a remotely hosted or separately running CueMap server, specify its URL here (e.g. http://localhost:8735).CUEMAP_PROJECT: Override the default project. Without this setting, CueMap derives a stable repository-scoped project from the current Git remote or working directory.CUEMAP_LOG_PATH: Override the embedded engine log path. It defaults to ~/.cuemap/server.log, which is the file read by cuemap logs.The MCP server calls EmbeddedCueMap.start() as soon as Codex launches the plugin process, before any CueMap tool is invoked. It attaches to a compatible engine already listening at CUEMAP_URL or the configured port; otherwise it starts and owns a local engine automatically. cuemap_init does not start the server—it previews or applies a confirmed repository scope after the engine is ready. An engine owned by the MCP process is stopped gracefully when that process exits.
To use this MCP server with your AI assistant, add it to your assistant's MCP configuration file.
{
"mcpServers": { "cuemap": { "command": "npx", "args": [ "-y", "cuemap-mcp" ], "env": { "CUEMAP_PORT": "8735" } } } }
path (string): Absolute path to the repository root.projectName (string, optional): Project ID override. Defaults to the stable repository-scoped ID.includedPaths (string[], optional): Repository-relative files or folders to preview. Empty means all supported files allowed by ignore rules.ignoredPatterns (string[], optional): Additional gitignore-style exclusions.ignoredExtensions (string[], optional): Additional excluded extensions.idle status with 0/0 writes is not considered complete.path (string): Absolute path to the repository root.projectName (string, optional): Project ID override. Defaults to the stable repository-scoped ID.includedPaths (string[], optional): User-approved repository-relative files or folders. Empty means all supported files allowed by ignore rules.ignoredPatterns (string[], optional): Additional gitignore-style exclusions.ignoredExtensions (string[], optional): Additional excluded extensions.confirmed (boolean): Must be true only after explicit user confirmation of the previewed scope.cuemap_init until verified_complete is true.project (string, optional): Project ID override. Defaults to the stable repository-scoped ID.active: Whether the current phase is non-terminal or writes/intent annotations remain pending.observed_activity: Whether this MCP process has observed an active phase or positive write/intent counts for the project.pending_writes: Remaining writes based on the latest totals.pending_intents: Remaining memory intent annotations based on the latest totals.verified_complete: True only after activity has been observed, the project reaches done or idle, no work failed or remains pending, and intent_ready is not false..cuemap package.global: true.memory_id and owning project. Requires an engine supporting GET /memories/:id?decoded=true; older engines return an upgrade error through this tool.cues.confirmed: true after explicit user confirmation.depth and same_domain_only controls.embeddings.These tools are for explicit ingestion requests. Repository initialization continues to use the preview, confirmation, and watcher workflow above.
confirmed: true.confirmed: true.content (string): Memory content to store.project (string, optional): Project ID that will own the memory. Defaults to the current repository-scoped project.cues (string[], optional): Explicit cues/tags to associate with the memory.metadata (object, optional): JSON metadata to store with the memory.source_key (string, optional): Stable source key for deterministic upsert/deduplication.event_time (number, optional): Original event timestamp as Unix seconds. Defaults to ingestion time.embedding (number[], optional): Precomputed memory embedding.disable_temporal_chunking (boolean, optional): Disable temporal chunking for this memory.async_ingest (boolean, optional): Process ingestion in the background and return immediately.target: "query" or "memory" and returns relative intent scores, recall/memory eligibility, confidence weight, and model/taxonomy versions. Scores are ranking signals, not calibrated probabilities.query (string): The natural language query to search for.limit (number, optional): Maximum results to return (default: 10).response_mode (full | preview, optional): Default full. Use preview for broad discovery; each hit returns a leading excerpt instead of full content, retaining IDs and metadata.preview_chars (integer, optional): Preview length cap, 100–2000 UTF-16 code units (default: 200). Ignored in full mode. Metadata and diagnostics are not capped.projects (string[], optional): List of project IDs to scope the search to. Multiple enables cross-project queries.cues (string[], optional): Specific cue tags to filter the search.query_time (string, optional): Timestamp or natural-language time anchor for v0.7 temporal query intent.depth (number, optional): Depth of multi-hop recall expander (default: 1).expansion_depth (number or "conversation_round", optional): Neighbor context expansion (default: "conversation_round"). Values above 1 include nearby parent chunks or source-ordered context, using a radius of expansion_depth - 1 when linkage exists. "conversation_round" prepends the message each result replies to (for an assistant reply, tOnly the beginning is shown; read the rest in the repo