
Cowork History
io.github.egoughnourPublished by GitHub account egoughnourv4.0.3Updated Oct 27, 2026
Hybrid search for Cowork history: FTS, Spotlight, and semantic vectors
Overview
Searches and browses your local Claude Code and Cowork conversation history using full-text, Spotlight and optional semantic vector search.
- What you can do
- Search conversations with hybrid, full-text, Spotlight or vector modes, with project filters and phrase matching
- List recent conversations, optionally filtered by project, and list all projects with history
- Read the full content of a specific conversation by session ID
- Show index statistics and which search backends are active
- Rebuild the index and optionally generate embeddings
- Check, install and configure Ollama for vector search
- When to use it
- Useful when you want an assistant to recall what you discussed in earlier Claude Code or Cowork sessions, find a past debugging thread, or review conversations by project without opening them by hand.
- Requirements
- Runs locally as a stdio process, installed via uvx, pip or the Claude Desktop bundle; macOS-oriented (Spotlight via mdfind, Homebrew install option). Needs an existing ~/.claude/ directory with conversation history. Vector search additionally needs a running Ollama server, configured by OLLAMA_URL and EMBEDDING_MODEL.
Installation
In SourceWeft
- Open Cowork History in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Cowork History MCP
An MCP (Model Context Protocol) server for searching and browsing your Claude conversation history stored in ~/.claude/. Works with both Claude Code and Cowork conversations.
Features
- Hybrid Search - Combines multiple search methods for best results:
- SQLite FTS5 - Fast full-text search with BM25 ranking
- macOS Spotlight - Leverages system content indexing via
mdfind - Vector Embeddings - Semantic similarity search (optional, requires Ollama)
- Smart Path Reconstruction - Recovers actual filesystem paths via probing (not heuristic guessing)
- Persistent Index - SQLite database with incremental updates for fast queries
- Ollama Setup Tools - Automated installation and configuration for embeddings
Installation
Option 1: Claude Desktop (One-Click Install)
Download cowork-history.mcpb from the latest release and double-click to install.
Option 2: Via uvx (Recommended for CLI)
Option 3: Via pip
Option 4: Manual Configuration
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
Quick Start
Once installed, Claude can search your conversation history:
Available Tools
Search & Browse
Ollama Setup (for Vector Search)
Search Modes
The cowork_history_search tool supports multiple search modes:
Search Examples
Enabling Vector Search
Vector search provides semantic similarity matching (finding related concepts even without exact keywords). It requires Ollama with an embedding model.
Quick Setup
Ask Claude to set it up for you:
Or manually:
Then generate embeddings:
How It Works
Indexing
The server maintains a SQLite database at ~/.claude/.history-index/conversations.db with:
- FTS5 virtual table for fast full-text search
- Conversation metadata (session ID, project, timestamps, topic)
- Full content for comprehensive search
- Path cache for reconstructed paths
- Embeddings table for vector search (optional)
The index updates automatically when you search (if >5 minutes old) or you can force a rebuild with cowork_history_reindex.
Environment Variables
Troubleshooting
No conversations found
- Make sure
~/.claude/directory exists - Check that you have conversation history (use Claude Code or Cowork first)
- Verify the MCP server is properly configured
Vector search not available
- Check Ollama is installed:
ollama --version - Check Ollama is running:
curl http://localhost:11434/api/tags - Check model is available:
ollama list - Pull embedding model:
ollama pull nomic-embed-text
Search not finding expected results
- Try natural language queries (semantic search is more flexible)
- Use
mode: "fts"for exact phrase matching - Check
cowork_history_statsto see which search backends are active
Development
Running locally
Testing with MCP Inspector
License
MIT License - see LICENSE file for details.
Source: README.md at commit 270013b
Tools
0Version history
1- v4.0.3LatestSep 16, 2026
Score
50% of the applicable checks met
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