Skip to main content
Glama

@meetdewey/mcp

MCP server for Dewey — search and research your document collections from Claude, Cursor, and any MCP-compatible agent. See the full API reference for details.

Installation

Add to your claude_desktop_config.json (or equivalent MCP client config):

{
  "mcpServers": {
    "dewey": {
      "command": "/bin/sh",
      "args": ["-lc", "npx -y @meetdewey/mcp"],
      "env": {
        "DEWEY_API_KEY": "dwy_live_...",
        "DEWEY_COLLECTION_ID": "..."
      }
    }
  }
}

Note for Windows users: replace "command": "/bin/sh" and "args": ["-lc", "npx -y @meetdewey/mcp"] with "command": "npx" and "args": ["-y", "@meetdewey/mcp"] — the login-shell wrapper is only needed on macOS/Linux to pick up Homebrew and nvm PATH entries that Claude Desktop doesn't inherit.

Related MCP server: MinerU Document Explorer

Environment variables

Variable

Required

Description

DEWEY_API_KEY

Yes

Your Dewey project API key

DEWEY_COLLECTION_ID

No

Default collection ID. When set, tools that accept collection_id will fall back to this value.

DEWEY_API_URL

No

Override the API base URL (default: https://api.meetdewey.com/v1)

Available tools

Search & research

Tool

Description

dewey_list_collections

List all collections in the project, including description and research instructions when set

dewey_search

Hybrid semantic + keyword search over chunk content

dewey_scan_sections

Lightweight search over section titles and summaries

dewey_research

Agentic research query with configurable depth — returns a grounded, cited answer

dewey_get_section

Fetch the full Markdown content of a section by ID

Document management

Tool

Description

dewey_list_documents

List documents in a collection with their processing status

dewey_get_document_sections

List all sections in a document (table of contents with heading levels and IDs)

dewey_get_document_markdown

Fetch the full converted Markdown content of a document

dewey_retry_document

Retry a failed document — clears error state and re-queues processing

dewey_delete_document

Permanently delete a document and all its derived data

Claims & contradictions

Tool

Description

dewey_list_claims

List extracted factual claims from a collection or specific document, filterable by importance (1–5)

dewey_list_contradictions

List detected contradictions — clusters of conflicting claims with explanations and suggested resolutions

dewey_detect_contradictions

Trigger an async contradiction detection run across all claims in a collection

dewey_get_contradiction_run

Get the status and stats of the latest contradiction detection run (use to poll after dewey_detect_contradictions)

dewey_resolve_contradiction

Apply a resolution instruction to a contradiction or dismiss it

Deduplication

Tool

Description

dewey_detect_duplicates

Trigger an async deduplication run — identifies near-duplicate documents by measuring shared content across chunks and marks one per cluster as canonical

dewey_get_duplicate_run

Get the status and stats of the latest deduplication run (use to poll after dewey_detect_duplicates)

dewey_list_duplicate_groups

List near-duplicate groups with canonical + members and coverage percentages

dewey_promote_duplicate_canonical

Promote a different member to canonical; old canonical becomes near_duplicate

dewey_disband_duplicate_group

Disband a group; former members rejoin retrieval as distinct documents

Non-canonical documents are excluded from retrieval and contradiction detection. Requires enableDeduplication: true on the collection (set via dewey_update_collection or the Dewey dashboard).

Collection settings

Tool

Description

dewey_get_collection_stats

Get document count, storage, section/chunk/claim counts, and processing status breakdown

dewey_update_collection

Update collection name, description, research instructions, visibility, and feature flags

dewey_recompute_summaries

Re-run AI section summarization across all documents (e.g. after changing the LLM model)

dewey_recompute_captions

Re-run AI captioning for all images and tables across all documents

dewey_delete_collection

Permanently delete a collection and all its data

Research instructions

Collections can have natural-language instructions that are automatically injected into the research system prompt — for example, noting units, preferred sources, or how to handle missing information. They can be set in the Dewey dashboard, via the API, or directly through dewey_update_collection. When dewey_resolve_contradiction is used to apply a resolution, the suggested instruction is also appended here automatically.

Tool usage pattern

A typical read-only research workflow:

  1. dewey_list_collections — discover available collections and understand their purpose

  2. dewey_scan_sections — quickly explore document structure to identify relevant sections

  3. dewey_get_section — load full content of a specific section

  4. dewey_search — retrieve the most relevant chunks for a focused question

  5. dewey_research — run a full agentic research query for questions that need multi-step reasoning

For deeper analysis and curation:

  1. dewey_get_collection_stats — assess how much content has been processed

  2. dewey_list_claims — surface the most important facts across documents

  3. dewey_list_contradictions — identify where documents disagree

  4. dewey_resolve_contradiction — apply or dismiss each contradiction, updating collection instructions automatically

License

MIT

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    B
    maintenance
    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
    4
    -
  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.
    16 npm
    633
    MIT
  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to intelligently search and reference documentation using hybrid semantic + keyword search via MCP protocol.
    -
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to query and manage a document knowledge base via MCP, with RAG-powered search and grounded answers with citations.
    MIT