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Reehal","email":"jag@jagreehal.com","url":"https://jagreehal.com"},"license":"Apache-2.0","homepage":"https://github.com/jagreehal/autotel/tree/main/packages/autotel-mcp#readme","keywords":["opentelemetry","otel","mcp","model-context-protocol","ai-agents","observability","traces","metrics","logs","otlp","collector"],"repository":{"type":"git","url":"git+https://github.com/jagreehal/autotel.git","directory":"packages/autotel-mcp"},"description":"MCP server for AI agents to investigate OpenTelemetry traces, metrics, and logs","maintainers":[{"name":"jagreehal","email":"jag.reehal@gmail.com"}],"readme":"# autotel-mcp\n\nAn MCP server that gives AI agents the ability to investigate OpenTelemetry traces, metrics, and logs. Ships with a built-in OTLP collector so any instrumented app can send data directly — no Jaeger, Grafana, or vendor setup required.\n\n### Key Features\n\n- **Backend-agnostic.** Built-in OTLP collector on port 4318 accepts data from any OTel-instrumented app.\n- **All three signals.** Traces, metrics, and logs — with cross-signal correlation.\n- **Agent-optimized.** 33 tools designed for progressive investigation: discover → diagnose → correlate → root cause.\n- **Zero infrastructure.** In-memory by default, persistent with `--persist`.\n\n### Requirements\n\n- Node.js 20 or newer\n- An MCP client: Claude Code, Claude Desktop, VS Code, Cursor, Windsurf, Goose, or any other MCP client\n\n## Getting started\n\nInstall the autotel-mcp server with your client.\n\n**Standard config** works in most tools:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"command\": \"npx\",\n      \"args\": [\"autotel-mcp\"]\n    }\n  }\n}\n```\n\n<details>\n<summary>Claude Code</summary>\n\nUse the Claude Code CLI to add the server:\n\n```bash\nclaude mcp add autotel npx autotel-mcp\n```\n\n</details>\n\n<details>\n<summary>Claude Desktop</summary>\n\nFollow the MCP install [guide](https://modelcontextprotocol.io/quickstart/user). Add to your config:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"command\": \"npx\",\n      \"args\": [\"autotel-mcp\"]\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary>VS Code</summary>\n\nFollow the MCP install [guide](https://code.visualstudio.com/docs/copilot/chat/mcp-servers#_add-an-mcp-server), use the standard config above. Or use the CLI:\n\n```bash\ncode --add-mcp '{\"name\":\"autotel\",\"command\":\"npx\",\"args\":[\"autotel-mcp\"]}'\n```\n\n</details>\n\n<details>\n<summary>Cursor</summary>\n\nGo to `Cursor Settings` -> `MCP` -> `Add new MCP Server`. Use `command` type with the command `npx autotel-mcp`.\n\n</details>\n\n<details>\n<summary>Windsurf</summary>\n\nFollow Windsurf MCP [documentation](https://docs.windsurf.com/windsurf/cascade/mcp). Use the standard config above.\n\n</details>\n\n<details>\n<summary>Cline</summary>\n\nAdd to your `cline_mcp_settings.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"type\": \"stdio\",\n      \"command\": \"npx\",\n      \"args\": [\"autotel-mcp\"],\n      \"disabled\": false\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary>Codex</summary>\n\n```bash\ncodex mcp add autotel npx \"autotel-mcp\"\n```\n\nOr add to `~/.codex/config.toml`:\n\n```toml\n[mcp_servers.autotel]\ncommand = \"npx\"\nargs = [\"autotel-mcp\"]\n```\n\n</details>\n\n<details>\n<summary>Copilot CLI</summary>\n\n```bash\n/mcp add\n```\n\nOr add to `~/.copilot/mcp-config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"type\": \"local\",\n      \"command\": \"npx\",\n      \"tools\": [\"*\"],\n      \"args\": [\"autotel-mcp\"]\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary>Gemini CLI</summary>\n\nFollow the MCP install [guide](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md#configure-the-mcp-server-in-settingsjson), use the standard config above.\n\n</details>\n\n<details>\n<summary>Goose</summary>\n\nGo to `Advanced settings` -> `Extensions` -> `Add custom extension`. Use type `STDIO`, set command to `npx autotel-mcp`.\n\n</details>\n\n<details>\n<summary>Amp</summary>\n\n```bash\namp mcp add autotel -- npx autotel-mcp\n```\n\nOr add to VS Code settings:\n\n```json\n\"amp.mcpServers\": {\n  \"autotel\": {\n    \"command\": \"npx\",\n    \"args\": [\"autotel-mcp\"]\n  }\n}\n```\n\n</details>\n\n<details>\n<summary>Warp</summary>\n\nGo to `Settings` -> `AI` -> `Manage MCP Servers` -> `+ Add`. Use the standard config above.\n\n</details>\n\n### With Jaeger backend\n\nTo query an existing Jaeger instance instead of the built-in collector:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"command\": \"npx\",\n      \"args\": [\"autotel-mcp\"],\n      \"env\": {\n        \"AUTOTEL_BACKEND\": \"jaeger\",\n        \"JAEGER_BASE_URL\": \"http://localhost:16686\"\n      }\n    }\n  }\n}\n```\n\n### With persistent storage\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"command\": \"npx\",\n      \"args\": [\"autotel-mcp\", \"--persist\", \"./autotel.db\"]\n    }\n  }\n}\n```\n\n## How it works\n\n```\nYour App ──OTLP──> autotel-mcp (port 4318) ──libsql──> in-memory store\n                        │\nAI Agent ──MCP──────────┘\n                   (stdio or HTTP)\n```\n\n1. Your instrumented app sends traces/metrics/logs via OTLP to `http://localhost:4318`\n2. autotel-mcp stores the data in libsql (in-memory by default)\n3. Your AI agent connects via MCP and investigates using 33 tools\n\n## Backends\n\n### Collector (default)\n\nBuilt-in OTLP collector with libsql storage. Accepts all three signals on port 4318. No external dependencies.\n\n```bash\n# In-memory (default) — data lost on restart\nnpx autotel-mcp\n\n# Persistent storage — survives restarts\nnpx autotel-mcp --persist ./autotel.db\n```\n\nPoint your app's OTLP exporter at the collector:\n\n```bash\nOTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 node your-app.js\n```\n\n### Jaeger\n\nQuery an existing Jaeger instance. Traces only (metrics and logs unsupported by Jaeger API).\n\n```bash\nAUTOTEL_BACKEND=jaeger JAEGER_BASE_URL=http://localhost:16686 npx autotel-mcp\n\n## Offline cache and snapshots\n\nCollector schema and semantic-convention tools can run in CI or air-gapped environments using local cache/snapshots.\n\n- `AUTOTEL_OFFLINE_MODE=true`\n  Disable network fetches and use local cache + bundled snapshots only.\n- `AUTOTEL_UPSTREAM_CACHE_DIR=/path/to/cache`\n  Override cache directory (default: `.autotel-cache` in current working directory).\n\nBundled snapshots ship under `fixtures/upstream/` and include a baseline collector version/component catalog plus semantic-convention namespace data.\n```\n\n## Configuration\n\n### Environment Variables\n\n| Variable                 | Default                                       | Description                           |\n| ------------------------ | --------------------------------------------- | ------------------------------------- |\n| `AUTOTEL_BACKEND`        | `collector`                                   | Backend: `collector`, `jaeger`        |\n| `AUTOTEL_TRANSPORT`      | `stdio`                                       | MCP transport: `stdio`, `http`        |\n| `AUTOTEL_PORT`           | `3000`                                        | MCP HTTP port                         |\n| `AUTOTEL_HOST`           | `127.0.0.1`                                   | MCP HTTP bind address                 |\n| `AUTOTEL_COLLECTOR_PORT` | `4318`                                        | OTLP receiver port                    |\n| `AUTOTEL_PERSIST`        | —                                             | libsql file path (omit for in-memory) |\n| `AUTOTEL_RETENTION_MS`   | `3600000` (1h mem) / `86400000` (24h persist) | Data retention                        |\n| `AUTOTEL_MAX_TRACES`     | `10000`                                       | Max traces before eviction            |\n| `JAEGER_BASE_URL`        | `http://localhost:16686`                      | Jaeger API URL                        |\n\n### HTTP mode\n\nRun as a standalone HTTP server (for remote clients or environments without stdio):\n\n```bash\nnpx autotel-mcp --transport http --port 3000\n```\n\nThen configure your MCP client with:\n\n```json\n{\n  \"mcpServers\": {\n    \"autotel\": {\n      \"url\": \"http://localhost:3000/mcp\"\n    }\n  }\n}\n```\n\n## Tools\n\n33 tools organized by investigation workflow.\n\n<details>\n<summary><b>Discovery (5)</b></summary>\n\n- **list_services** — Services with span counts and error rates\n- **list_operations** — Operations for a service, ranked by traffic\n- **backend_health** — Backend reachability and ingestion status\n- **backend_capabilities** — Signal support and query features\n- **list_capabilities** — Full server manifest\n\n</details>\n\n<details>\n<summary><b>Trace Investigation (4)</b></summary>\n\n- **search_traces** — Find traces by service, operation, status, duration, tags, time window\n- **search_spans** — Span-level search across traces\n- **get_trace** — Full trace detail by ID\n- **summarize_trace** — Compact summary: span tree, errors, critical path, duration breakdown\n\n</details>\n\n<details>\n<summary><b>Diagnosis (4)</b></summary>\n\n- **find_anomalies** — Scan for statistical outliers: latency spikes, error rate jumps\n- **find_root_cause** — Walk a trace span tree to identify the bottleneck span\n- **find_errors** — Aggregate error spans grouped by service and operation\n- **check_slos** — Report SLO violations given p99 latency and error rate targets\n\n</details>\n\n<details>\n<summary><b>Topology (2)</b></summary>\n\n- **service_map** — Dependency graph with call counts, error rates, latency percentiles\n- **list_services** / **list_operations** — Service and operation discovery\n\n</details>\n\n<details>\n<summary><b>LLM Analytics (6)</b></summary>\n\n- **get_llm_usage** — Token usage by model and service\n- **list_llm_models** — Models in use with request counts\n- **get_llm_model_stats** — Latency/token/error percentiles per model\n- **get_llm_expensive_traces** — Top traces by token count\n- **get_llm_slow_traces** — Slowest LLM traces\n- **list_llm_tools** — Tool/function call usage by name\n\n</details>\n\n<details>\n<summary><b>Signals (3)</b></summary>\n\n- **list_metrics** — Available metric series\n- **get_metric_series** — Time-series data for a metric\n- **search_logs** — Log search by severity, service, trace ID, text\n\n</details>\n\n<details>\n<summary><b>Cross-Signal Correlation (2)</b></summary>\n\n- **correlate** — Given a trace ID: return trace + metrics from involved services + correlated logs\n- **explain_slowdown** — Combines anomaly detection with cross-signal correlation\n\n</details>\n\n<details>\n<summary><b>Collector Config (3)</b></summary>\n\n- **validate_collector_config** — Validate OTLP receiver config fragment\n- **explain_collector_config** — Explain config shape and defaults\n- **suggest_collector_config** — Generate minimal config\n\n</details>\n\n<details>\n<summary><b>Instrumentation Quality (2)</b></summary>\n\n- **score_span_instrumentation** — Quality score 0-100 with A-F grade\n- **explain_instrumentation_score** — Scoring rubric details\n\n</details>\n\n## Resources\n\nMCP resources give agents context without burning tool calls:\n\n| URI                              | Content                                           |\n| -------------------------------- | ------------------------------------------------- |\n| `otel://capabilities`            | Server manifest: transports, tool groups, signals |\n| `otel://tool-catalog`            | All tools with descriptions and workflow hints    |\n| `otel://backend/capabilities`    | Active backend's signal support                   |\n| `otel://collector/config`        | OTLP receiver config guidance                     |\n| `otel://instrumentation/scoring` | Scoring rubric explanation                        |\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}