infino-ai/mcp-server

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/infino-ai-mcp-server

About

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

6 read · 4 write · 2 delete · 3 not yet classified

Secrets

2 (API keys, tokens or passwords)

Install scripts

Not scanned yet

Runs as

Node.js package

Tools (15)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• infino_add_documentsAdd documents to an Infino table

  Append documents (rows, as JSON objects keyed by column name) to a table; one call is one commit. If the table has a vector index and a document omits the vector, the server embeds its text column (a local model, no API key). Send tens of rows per call; for a whole corpus use the infino CLI or an SDK.

• infino_countCount keyword matches

  Use when you only need HOW MANY rows match a keyword query, not the rows themselves — a fast tally over a text column, without fetching or ranking. Cheaper than infino_keyword_search when a number is all you need (e.g. 'how many docs mention X'). For the matching rows use infino_keyword_search or infino_token_match.

• infino_create_databaseCreate the database this server is connected to

  Provision the database named in the connection. On Infino Cloud this registers the database; on a local path or bucket the catalog root is the database, so this is a no-op success. Idempotent: an existing database is reported as created: false. Call it when a hosted connection answers 404.

• infino_create_tableCreate an Infino table

  Create a table from a {column: type} descriptor. Full-text (BM25) indexes go on the columns named in 'fts' (default: every large_utf8 column; the index requires that type). With vector: true the server adds an 'embedding' column sized to its embedder and a cosine vector index on it, so semantic and hybrid search work and rows added without a vector are embedded from their text. Every column is required in every row you add, so declare only columns you will always fill. Give every table a stable key column of type utf8 so rows can be replaced or removed later by predicate (e.g. key = 'doc-1'); …

• infino_delete_documentsDelete documents from an Infino table

  Delete the rows matching a SQL predicate, e.g. "status = 'spam'". Returns how many rows matched and were removed. Check the predicate first with infino_count or infino_sql. Requires durable storage (not memory://).

• infino_describe_tableDescribe an Infino table

  Return a table's column names and types — call before searching so you know which column to target and what fields each result row carries.

• infino_drop_tableDrop an Infino table

  Drop a table from the catalog and, by default, delete its storage objects too. Pass purge: false to only unregister the table and leave the bytes in place. Irreversible.

• infino_exact_matchExact match (unranked exact filter)

  Use to fetch rows whose column exactly equals a value — a tag, status, or id string. Unranked exact-equality filter over an indexed column. For ranked text relevance use infino_keyword_search; for multi-column analytical filtering use infino_sql.

• infino_hybrid_searchHybrid (keyword + semantic) search

  Use when a query carries both specific terms and an intent — you want exact-term precision without giving up paraphrase recall. Fuses BM25 over a text column with vector similarity over the embedding column in a single ranking pass, so rows matching the literal terms AND the meaning rank highest; the score is the fused rank (higher is better) plus the columns you project ('columns'; the full text column by default). Embeds the query with a local model (no API key). Sits between infino_keyword_search (literal only) and infino_semantic_search (meaning only).

• infino_keyword_searchKeyword (BM25) search

  Use when the query is literal terms — identifiers, error codes, product names, exact phrases — and you want results ranked by relevance. BM25 full-text search over a text column: ranks rows by how well the query's tokens (and their stems) match, each with a relevance score (higher is better) plus the columns you project ('columns'; the full text column by default). Matches exact tokens, not synonyms or paraphrases. Prefer this over SQL LIKE for known literal terms. For meaning-based search use infino_semantic_search; for both at once use infino_hybrid_search.

• infino_list_tablesList Infino tables

  List the tables in the connected catalog. Call this first to discover what is available to search or query.

• infino_semantic_searchSemantic (vector) search

  Use when searching for a concept by meaning and the exact wording is unknown — this retrieves paraphrases and synonyms, not just literal matches. Embeds the query with a local model (no API key) and ranks a table's embedding column by vector similarity. Each hit carries a score that is a DISTANCE (lower is closer) plus the columns you project ('columns'; the full text column by default). Optional 'filter' restricts the ranking to rows whose keyword column matches a predicate first (a pushdown pre-filter, e.g. semantic search only within rows tagged 'billing'). For exact terms use infino_keywor…

• infino_sqlSQL over Infino

  Use for structural or analytical questions — counts, GROUP BY, joins, aggregates, filtering by column value — returning result rows. The engine's search functions are callable as table-valued relations, so a single query can rank AND aggregate: bm25_search('table','text_col','terms', k) — also bm25_search_prefix / token_match / exact_match — need no embedding. vector_search('table','vec_col', {{q}}, k) and hybrid_search('table','text_col','terms','vec_col', {{q}}, k) need a query vector: put a {{name}} placeholder where the vector goes and pass embed:{"name":"query text"} — the server embeds t…

• infino_token_matchToken match (unranked keyword filter)

  Use when you need the SET of rows containing a keyword, not a ranked order — a fast unranked keyword filter. Returns rows whose text column contains the token(s), matching indexed tokens and their stems. For ranked results use infino_keyword_search; for analytical filtering across columns use infino_sql.

• infino_update_documentsUpdate documents in an Infino table

  Replace the rows matching a SQL predicate with new documents, 1:1; the number of matched rows must equal the number of replacement documents. As with add, a row that omits its vector has it embedded from the text column (local model, no API key). Requires durable storage (not memory://).

Signals

Stars

0

Forks

0

Open issues

0

Weekly downloads

58

Last commit

Oct 9, 2026 (20 days ago)

Last release

Oct 1, 2026 (28 days ago)

Last package publish

Oct 1, 2026 (28 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.infino-ai/mcp-server

Version

0.16.0

Auth

No auth

Packages

npm @infino-ai/mcp-server

License

Apache-2.0

Repository

github.com/infino-ai/infino-mcp

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql