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code retrieval MCP Server — natural language → relevant code snippets, with incremental sync and file watching","maintainers":[{"name":"huawang","email":"2499354024@qq.com"}],"readme":"# semantic-retrieval-mcp\n\nSemantic code retrieval MCP Server — natural language queries → relevant code snippets.\n\n## What it does\n\nIn any MCP-compatible AI client (Claude Code, Cline, Windsurf, etc.), ask:\n\n> \"Find the authentication handler in this project\"\n\nThe AI calls this MCP Server, which performs semantic search across your codebase and returns precisely matched code snippets.\n\n**Core capability**: Natural language → relevant code. Cross-language, real-time index, far better than grep.\n\n## Install\n\n```bash\nnpm install -g semantic-retrieval-mcp\n```\n\nOr use directly with `npx`:\n\n```bash\nnpx -y semantic-retrieval-mcp\n```\n\n## Configuration\n\n### With npx (recommended)\n\n```json\n{\n  \"mcpServers\": {\n    \"semantic-retrieval\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"semantic-retrieval-mcp\"],\n      \"env\": {\n        \"SR_TOKEN\": \"your-access-token\",\n        \"SR_TENANT\": \"https://your-tenant-url/\"\n      }\n    }\n  }\n}\n```\n\n### With global install\n\n```json\n{\n  \"mcpServers\": {\n    \"semantic-retrieval\": {\n      \"command\": \"semantic-retrieval-mcp\",\n      \"env\": {\n        \"SR_TOKEN\": \"your-access-token\",\n        \"SR_TENANT\": \"https://your-tenant-url/\"\n      }\n    }\n  }\n}\n```\n\n### Auto-detect from editor\n\nIf you have a compatible editor with an active session, the server can read credentials automatically:\n\n```json\n{\n  \"mcpServers\": {\n    \"semantic-retrieval\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"semantic-retrieval-mcp\"],\n      \"env\": {\n        \"SR_EDITOR\": \"cursor\"\n      }\n    }\n  }\n}\n```\n\n### Environment Variables\n\n| Variable | Description |\n|---|---|\n| `SR_TOKEN` | Access token (recommended) |\n| `SR_TENANT` | Tenant URL (recommended) |\n| `SR_EDITOR` | Auto-detect from editor: `vscode` / `cursor` / `windsurf` / `trae` / `kiro` |\n| `SR_WATCH` | File watching enabled by default; set to `0` to disable |\n\n## Usage Scenarios\n\n### Scenario 1: AI auto-invokes (most common)\n\nAfter configuration, just chat with your AI normally. When it needs to understand code, it calls the tool automatically:\n\n```\nYou: \"Refactor the login logic from session-based to JWT\"\n\nAI internally: → calls codebase-retrieval(\"authentication and login logic\")\n              → receives relevant code snippets\n              → provides solution based on actual code\n```\n\n**You don't need to do anything.** On first query for a directory, the server automatically:\n1. Scans all source files\n2. Computes fingerprints (SHA256)\n3. Uploads new files to the indexing backend\n4. Creates a checkpoint\n5. Performs semantic retrieval\n\nSubsequent queries reuse the cached checkpoint — no re-scanning or re-uploading.\n\n### Scenario 2: Different project directory\n\nEach `directory_path` is managed independently. New directories get indexed automatically.\n\n### Scenario 3: Force re-sync after major changes\n\n```\nYou: \"I just merged a big branch, re-index the project\"\nAI: → calls sync-workspace(directory_path)\n```\n\n### Scenario 4: Real-time file watching\n\nFile watching is enabled by default. When active, chokidar monitors file changes:\n\n- File edits/creates/deletes → automatic fingerprint update\n- 2-second debounce for batch processing (or 50 changes triggers immediately)\n- Auto upload + checkpoint update\n- **Next query always uses the latest index**\n\n### Scenario 5: Token rotation\n\nWhen the token changes, cached checkpoints auto-invalidate. Next query re-syncs with the new token automatically.\n\n## Tools\n\n### codebase-retrieval\n\n| Parameter | Type | Description |\n|---|---|---|\n| `information_request` | string | Natural language query (e.g. \"Find the database connection code\") |\n| `directory_path` | string | Absolute path to the project directory to search |\n\n### sync-workspace\n\n| Parameter | Type | Description |\n|---|---|---|\n| `directory_path` | string | Absolute path to the project directory to re-sync |\n\n## How it works\n\n```\nYour AI Client\n    │ MCP (stdio)\n    ▼\nMCP Server (index.js)\n    │\n    ├─ First query for a dir ──→ BlobManager\n    │   ├─ Scan files → SHA256(path+content) = blobName\n    │   ├─ POST find-missing     → Which files does the server not have?\n    │   ├─ POST batch-upload     → Upload missing files\n    │   ├─ POST checkpoint-blobs → Create snapshot, get checkpointId\n    │   └─ Persist to ~/.semantic-retrieval-mcp/<hash>.json\n    │\n    ├─ Subsequent queries ──→ Use cached checkpointId\n    │\n    └─ POST agents/codebase-retrieval\n        → Cloud semantic search\n        → Return matching code snippets to AI\n\nFile watching (SR_WATCH=1):\n    chokidar → file change → debounce → find-missing → upload → checkpoint\n```\n\n## Cache\n\nEach workspace directory's state is cached in `~/.semantic-retrieval-mcp/<hash>.json`.\n\n- Survives MCP Server restarts\n- Same tenant, different token → checkpoint reused (content-based SHA256)\n- Different tenant → checkpoint auto-invalidated\n- Delete `~/.semantic-retrieval-mcp/` to clear all caches\n\n## License\n\nMIT\n","readmeFilename":"README.md"}