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MCP server — expose context graph tools to AI agents","maintainers":[{"name":"infona-ci","email":"oss@infona.ai"}],"readme":"# @infona-ai/mcp\n\nMCP (Model Context Protocol) server for [Infona](https://infona.ai). Gives AI agents tools to query, search, ingest, export, enrich, and manage your context graphs (knowledge graphs) in natural language.\n\n## Install / run\n\nPackage: **`@infona-ai/mcp`**. Primary binary: **`infona-mcp`**. Compatibility alias: **`onta-mcp`** (same entrypoint). There is no bare `mcp` bin.\n\nNo install needed — use `npx`:\n\n```bash\nnpx -y -p @infona-ai/mcp infona-mcp\n```\n\nOr install globally:\n\n```bash\nnpm install -g @infona-ai/mcp\ninfona-mcp\n```\n\n## Claude Desktop / Cursor / Claude Code\n\nLocal OSS (no API key):\n\n```json\n{\n  \"mcpServers\": {\n    \"infona\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"-p\", \"@infona-ai/mcp\", \"infona-mcp\"],\n      \"env\": {\n        \"INFONA_API_URL\": \"http://localhost:8000\",\n        \"INFONA_TENANT\": \"default\"\n      }\n    }\n  }\n}\n```\n\nHosted Infona also needs `INFONA_API_KEY` and your workspace id in `INFONA_TENANT`.\n\n## Tools exposed\n\nThe server registers **37** tools, plus **1 more** (`list_local_files`) when you opt in by configuring `INFONA_LOCAL_FILES_DIR` (see [Environment](#environment)):\n\n- `agent` — the single conversational front door to the Ask-AI agent. Send a natural-language message; the agent classifies intent and either answers a question, asks a clarifying question, or proposes a multi-step plan (enrich attributes, clean/normalize values, merge duplicates, inspect/extend the ontology). A plan is **not executed** until you confirm it by calling `agent` again with the returned `plan_id` as `confirm_plan_id`. Planning is free; any paid step a plan contains (e.g. web enrichment) is authorized server-side at execute time, so confirming honors your tenant's entitlements.\n- `list_knowledge_graphs` — list available KGs and their descriptions.\n- `ask` — ask a natural-language question against a context graph; returns the answer (and an explanation when available).\n- `search` — semantic + keyword (hybrid) search over free-text attributes of entities: find *which* entities mention/discuss a topic, with a matching snippet as the citation. Reads the **derived index** (not live triples). When the embedding service is unavailable or the semantic index is off, the tool still answers keyword-only and **says so** (`reduced recall`); use `grep` for an index-free literal scan of one graph. Use `ask` for aggregate or structured questions.\n- `grep` — literal substring search across every literal value in **one** context graph, by scanning its triples directly (no index). The exact-string debugging counterpart to `search`: it finds values `search` cannot see because they were never indexed. Plain substring matching, not regex; unranked, and can be slow on a large graph, so it is bounded to one KG and rate-limited.\n- `view_ontology` — show the ontology (types, attributes, relationships, and type descriptions) across your context graphs, plus the canonical skills prompt-block for those types. Tenant-wide and declaration-only; for one graph's actual data coverage use `inspect_graph_schema`.\n- `inspect_graph_schema`: inspect ONE context graph's schema *with population data*, i.e. per type, which attributes and relationships actually carry data there and on what share of its entities. Declared-but-empty types and attributes are listed and marked `EMPTY`, so a missing slot is never confused with a non-existent one. Use it before asking for specific attributes so you never guess between similar names.\n- `create_knowledge_graph` — create a new, empty KG (optionally with a description).\n- `delete_knowledge_graph` — delete a KG and all of its data (irreversible).\n- `ingest_csv` — ingest a CSV file by absolute path into a named KG; the schema is inferred automatically. Set `join_on` to merge each row onto the existing entity that carries the same key value instead of minting duplicates.\n- `ingest_text` — ingest free-form text (or JSON) into a named KG **without writing a file first**. Posts through the same canonical `POST /graphs/{tenant}/ingest` route as the CLI's `infona ingest --text` (LLM entity extraction → ontology resolve → insert). Use for notes, meeting summaries, or any unstructured knowledge; use `ingest_csv` for tabular files on disk.\n- `ingest_dlt` — extract a 3rd-party REST or SQL source via dlt and ingest the rows into a named KG. Posts the frozen `{source, map, kg}` body through the same SDK method (`ingestDlt`) the CLI and Explorer use. The Infona backend needs the optional extra (`pip install infona-client[dlt]`); if it is missing the tool returns that install hint. Auth is BYOK (`env:VAR`).\n- `export_kg` — export a context graph's instance data as **JSON** (default) or **CSV**. Same canonical `GET /graphs/{tenant}/kgs/{kg}/export` route as the CLI's `infona export` (`Client.exportKg`). Optional `type` and `limit` filters; large dumps are truncated in the tool response with a note (use filters or the CLI for a full file).\n- `er_rebuild` — second-pass entity resolution: collapse intra-batch duplicate fragments in an already-ingested KG. Same `Client.erRebuild` path as the CLI's `infona er rebuild` (`POST …/explore/kgs/{kg}/er-rebuild`, 300s timeout). Synchronous; use after messy ingest when duplicates remain.\n- `evolve_ontology` — resolve a fuzzy natural-language ontology-evolution ask (no exact names needed); auto-applies high-confidence changes and returns a summary plus any proposals to confirm.\n- `apply_ontology_change` — confirm and commit a single proposal returned by `evolve_ontology`.\n- `apply_ontology_changes` — confirm and commit several proposals from `evolve_ontology` in one call (one round-trip instead of N; idempotent, per-proposal outcomes).\n- `schedule` — set up a recurring standing alert / scheduled refresh (or `list` existing ones): watch values on a cadence and deliver a change payload to a webhook only when they change.\n- `list_jobs` — list background jobs (enrichment, dedupe, reconciliation, web-discovery) for the tenant; use it to check on async work the `agent` tool kicked off.\n- `get_job` — full record + live progress of a single background job by id (returns instantly with current status).\n- `wait_for_job` — block server-side until a background job settles (or a bounded timeout), then return its status + progress — so one call covers a whole wait window instead of polling `get_job` in a loop.\n- `list_local_files`: **opt-in, off by default.** List the `.csv` / `.json` / `.jsonl` files in a directory you have explicitly granted, so the agent can pass a real absolute path to `ingest_csv` instead of guessing one. Only registered when `INFONA_LOCAL_FILES_DIR` resolves to an existing directory; otherwise it does not appear at all.\n- `list_records` — one page of entity instances of a type (columns + rows). Same `Client.exploreRecords` path as the Explorer data table.\n- `get_entity` — one entity's properties and incident relationships (`Client.getEntity`).\n- `type_summary` — per-type inventory with the coverage (and samples, if any) the API already returns (`Client.exploreSummary`). Does not invent values.\n- `list_tenants` — workspaces this API key can access. The MCP process tenant is `INFONA_TENANT` (there is no mid-session switch).\n- `create_tenant` — create a workspace (writes the caller's tenant list). Does not change `INFONA_TENANT` for this process.\n- `recompute_stats` — schedule a type-stats recompute for a graph (writes the stats graph, not instance data).\n- `list_skills` / `get_skill` / `validate_skill` / `put_skill` / `delete_skill` — type-attached markdown skills. `put_skill` and `delete_skill` write the ontology overlay.\n- `skills_prompt_block` — the exact skill text an agent is handed (`GET …/skills/prompt-block`). Not re-rendered locally. `view_ontology` includes this block so inspect sees tenant skills.\n- `list_functions` / `register_function` / `invoke_function` / `delete_function` — type-attached HTTPS/Lambda endpoints. Register, invoke, and delete write the registry / graph.\n\n> Enrichment and cleaning/normalization are reached **through the `agent`\n> tool** — it plans them and, on confirm, runs them as background jobs, so any\n> paid step stays authorized server-side at execute time. Duplicate-fragment\n> collapse after ingest is the `er_rebuild` tool (same path as `infona er\n> rebuild`). Use `list_jobs` / `get_job` / `wait_for_job` to watch agent jobs\n> finish.\n\n## Environment\n\nLocal OSS — same JSON as the root README (no `INFONA_API_KEY`):\n\n```json\n{\n  \"mcpServers\": {\n    \"infona\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"-p\", \"@infona-ai/mcp\", \"infona-mcp\"],\n      \"env\": {\n        \"INFONA_API_URL\": \"http://localhost:8000\",\n        \"INFONA_TENANT\": \"default\"\n      }\n    }\n  }\n}\n```\n\n- `INFONA_API_KEY` — required for hosted (`https://api.infona.ai`). Optional for localhost / `127.0.0.1` (open-access OSS).\n- `INFONA_API_URL` — default `https://api.infona.ai`. Local OSS: `http://localhost:8000`.\n- `INFONA_TENANT` — default `demo-tenant` on hosted, `default` on localhost.\n- `INFONA_LOCAL_FILES_DIR`: **optional, unset by default.** An absolute path to one directory (or several, joined by your platform's path separator, max 4) that the agent may LIST. Setting it registers the `list_local_files` tool; leaving it unset means that tool does not exist.\n\nEnv vars are **`INFONA_*` only** — there is no `ONTA_*` (or other legacy-prefix) fallback in this package.\n\n### `INFONA_LOCAL_FILES_DIR` and what it grants\n\nThis MCP server runs as a local process with your own filesystem permissions, and\nanything it returns is sent to a remote model. So local file listing is off until\nyou name a directory, and it is scoped to exactly that directory:\n\n```json\n\"env\": {\n  \"INFONA_API_URL\": \"http://localhost:8000\",\n  \"INFONA_LOCAL_FILES_DIR\": \"/Users/you/infona-data\"\n}\n```\n\nWhat you are granting, precisely:\n\n- The agent can see **filenames, sizes and modification times** of `.csv`,\n  `.json` and `.jsonl` files inside that directory, up to 3 levels deep.\n- **No file contents are ever read** by this tool. Contents leave your machine\n  only when you ingest a specific file.\n- Nothing outside the directory is visible: directory symlinks are not followed,\n  every returned file must resolve back inside the root, and `..` is rejected.\n  The filesystem root (`/`) is refused as a value.\n- Dotfiles, dot-directories and `node_modules` are skipped.\n- One exception to the containment rule: a **hardlink** placed inside the\n  directory that points at a file outside it *is* listed (name, size and\n  modification time, never content). A hardlink cannot be distinguished from an\n  ordinary file, and creating one already requires write access to the directory.\n\nPoint it at a directory that holds the data you intend to ingest, not at your\nhome directory or `/`. Filenames inside the granted directory are themselves\nvisible to the model, so avoid granting a directory whose filenames are sensitive.\n\n## License\n\nApache-2.0. See [LICENSE](./LICENSE).\n","readmeFilename":"README.md"}