haiku.rag/docs/mcp.md

# Model Context Protocol (MCP)

The MCP server exposes `haiku.rag` as MCP tools for compatible MCP clients like Claude Desktop.

## Starting MCP Server

The MCP server supports Streamable HTTP and stdio transports:

```bash
# Default streamable HTTP transport on 127.0.0.1:8001
haiku-rag mcp

# Custom port
haiku-rag mcp --port 9000

# Bind to all interfaces (e.g. inside a container)
haiku-rag mcp --host 0.0.0.0 --port 8001

# stdio transport (for Claude Desktop)
haiku-rag mcp --stdio
```

`--host` defaults to `127.0.0.1` (loopback only). Bind to `0.0.0.0` only
when you want the MCP server reachable from outside the local machine —
e.g. inside a Docker container with port mapping, or on a trusted LAN.

The server opens the database read-only. Ingestion goes through the CLI
(`haiku-rag add`, `add-src`, `delete`) or [`haiku-ingester`](ingester.md).

## Collections

With several databases in `lancedb.databases`, the server covers all of
them, as `haiku-rag search` does. Results, documents and citations name
theirs in `source`. `sources` on the search and question tools restricts a
call to a subset; `source` on `get_document` names the database holding the
document. A name the server does not cover is an error.
`haiku-rag --db-name NAME mcp` serves one. See
[Multiple Databases](configuration/storage.md#multiple-databases).

## Claude Desktop Integration

Add to your Claude Desktop configuration (`claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "haiku-rag": {
      "command": "haiku-rag",
      "args": ["mcp", "--stdio"]
    }
  }
}
```

With a custom database path:

```json
{
  "mcpServers": {
    "haiku-rag": {
      "command": "haiku-rag",
      "args": ["mcp", "--stdio", "--db", "/path/to/database.lancedb"]
    }
  }
}
```

After restarting Claude Desktop, you can ask Claude to search your documents or answer questions using your knowledge base.

## Tools

Every tool is read-only and says so in its annotations. Each parameter carries
a description in the tool schema, so the listing below names them without
repeating it.

| Tool | Registered | Parameters |
|---|---|---|
| `search_documents` | always | `query`, `limit`, `include_images`, `filter`, `sources` |
| `search_documents_by_image` | multimodal embedder only | `image_base64`, `limit`, `include_images`, `filter`, `sources` |
| `get_document` | always | `document_id`, `source` |
| `get_document_outline` | always | `document_id`, `source` |
| `get_document_section` | always | `document_id`, `section_id`, `source` |
| `list_documents` | always | `limit`, `offset`, `filter` |
| `ask_question` | always | `question`, `images_base64`, `sources` |
| `analyze` | always | `question`, `filter`, `images_base64`, `sources` |

`search_documents` runs hybrid search, vector and full-text. Its text content
is the rendering the in-process agents read: results best first, each with its
rank, `Document ID`, `Collection` when the server covers several, the document
title, section headings and the passage. Pictures in the results follow as
image blocks, one per distinct picture, each preceded by a line naming its
result; `include_images: false` leaves them out. The structured content is the
`SearchResult` list without picture bytes. Scores are not comparable across
queries or search types, so rank is the signal. `search_documents_by_image`
embeds the query image and searches by vector similarity alone.

`get_document` returns a document whole, in reading order. For a long one,
`get_document_outline` returns the heading tree with page numbers and
`get_document_section` the text of one section, subsections included; a
node's `id` in the outline is the `section_id`. A document without headings
has an empty outline. `list_documents` returns titles, URIs and metadata,
which is how a client learns what a filter can match.

`ask_question` runs the RAG agent on the server and returns an answer
followed by its citations. `analyze` writes and runs Python in a sandbox
over the documents, for counting, aggregation and computation across
documents. Both cost a model call.

### Filters

`filter` is a SQL WHERE clause over the document columns `id`, `uri`, `title`,
`metadata`, `created_at`, `updated_at`. `metadata` is a JSON string, so match
its keys with LIKE:

```sql
metadata LIKE '%"author": "Smith"%'
uri LIKE '%.pdf'
title = 'Q3 report'
```

### Errors

A failure is an MCP error, never an empty result. Expected failures carry a
message: a document or section id that matches nothing, a collection the
server does not cover, a filter the query engine rejects (with its message),
invalid base64,
and an `ask_question` or `analyze` failure naming only the exception type.
Anything else reaches the client as `Error calling tool 'name'` and its
traceback goes to the server log.

### Instructions

The server publishes `instructions` describing the knowledge base: what it
holds, when to reach for it, the collection names when it covers several, and
`prompts.domain_preamble` when set. Claude Code shows them to the model. Claude
Desktop does not, so every tool description stands on its own.

## Continuous ingestion

For continuous document ingestion (filesystem watch, S3 polling, HTTP
sources, a job queue with retries), run [`haiku-ingester`](ingester.md)
as a separate process against the same LanceDB.