transcript-search
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@transcript-search find how we implemented authentication in past sessions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Semantic Transcript Search
A semantic search system for Claude Code transcript history, exposed as an MCP server so Claude Code can query its own past sessions.
Architecture
~/.claude/projects/**/*.jsonl → Indexer → Qdrant (localhost:6333)
↑
Claude Code → MCP Server → Query API ────┘
↓
OpenAI EmbeddingsRelated MCP server: conversation-history-mcp
Prerequisites
Python 3.12+
Qdrant running locally on port 6333
OpenAI API key
Starting Qdrant
Using Docker:
docker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrantOr install and run locally following Qdrant docs.
Installation
git clone https://github.com/ttrine/semantic-transcript-search.git
cd semantic-transcript-search
uv syncConfiguration
Create a config file at ~/.config/transcript-search/config.json:
{
"openai_api_key": "sk-...",
"qdrant_url": "http://localhost:6333",
"collection_name": "claude_transcripts",
"embedding_model": "text-embedding-3-small",
"embedding_dimensions": 1536
}Note: If using text-embedding-3-large, set embedding_dimensions to 3072.
Alternatively, set environment variables:
OPENAI_API_KEY- Your OpenAI API keyQDRANT_URL- Qdrant server URL (defaults tohttp://localhost:6333)
Usage
Continuous Indexing Service (Recommended)
Run the watcher service to automatically index new and modified transcripts:
uv run transcript-watcherOptions:
--foreground: Run in foreground with console logging--force-reindex: Re-index all files before watching--no-initial-index: Skip initial indexing, watch only--debounce FLOAT: Debounce delay in seconds (default: 2.0)--watch-path PATH: Custom path to watch
macOS launchd Service
To run the watcher as a background service that starts at login:
# Install the service
cp launchd/com.transcript-search.watcher.plist ~/Library/LaunchAgents/
# Edit the plist to set the correct path to transcript-watcher binary
# Then load the service
launchctl load ~/Library/LaunchAgents/com.transcript-search.watcher.plist
# Check status
launchctl list | grep transcript
# View logs
tail -f ~/.local/log/transcript-search/watcher.log
# Stop service
launchctl unload ~/Library/LaunchAgents/com.transcript-search.watcher.plistOne-Time Indexing
For manual/one-time indexing of all Claude Code transcripts:
uv run index-transcriptsOptions:
--force: Re-index all files (ignores cache)--stats: Show collection statistics--base-path PATH: Custom path to search for transcripts
CLI Search
uv run search-transcripts "how did we implement the TDD workflow?"Options:
-n, --limit: Number of results (default: 10)-p, --project: Filter by project path-t, --type: Filter by message type (user/assistant)-j, --json-output: Output as JSON-f, --full: Show full content instead of preview
MCP Server Integration
Add to your Claude Code MCP configuration (~/.claude/mcp.json):
{
"mcpServers": {
"transcript-search": {
"command": "uv",
"args": ["run", "--directory", "/path/to/semantic-transcript-search", "python", "-m", "transcript_search.mcp_server"]
}
}
}After restarting Claude Code, the search_transcripts tool will be available.
MCP Tools
search_transcripts
Search through past Claude Code session transcripts using semantic similarity.
Parameters:
query(required): The search querylimit: Maximum results (default: 10)project_filter: Filter to specific projectmessage_type: Filter by "user" or "assistant"
get_session_context
Retrieve full context from a specific session after finding a relevant match.
Parameters:
session_id(required): The session ID to retrievelimit: Maximum messages (default: 50)
Development
Install dev dependencies:
uv sync --extra devRun unit tests:
uv run pytest tests/ -m "not integration" -vRun integration tests (requires running Qdrant and valid OpenAI API key):
uv run pytest tests/ -m integration -vRun all tests:
uv run pytest tests/ -vLicense
MIT
Available Tools
2 toolsget_session_contextB
Retrieve full context from a specific Claude Code session. Use this after finding a relevant result to get more context from that session.
Inputs: session_id (required), limit.
search_transcriptsA
Search through past Claude Code session transcripts using semantic similarity. Use this to find past discussions, solutions, or context from previous coding sessions. Returns matched content with session metadata for navigation.
Inputs: query (required), limit, message_type, project_filter.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one searches across transcripts, the other retrieves full session context. No overlap or ambiguity in their roles.
Both tools follow a consistent verb_noun pattern using snake_case: search_transcripts and get_session_context. The naming is predictable and uniform.
With only 2 tools, the set is minimal, but it is appropriately scoped for a narrow transcript search-and-retrieve workflow. Slightly on the thin side, though reasonable for the stated purpose.
The workflow is complete: search for relevant transcripts and then retrieve full session context. No obvious missing operations for the domain.
Maintenance
Related MCP Connectors
Shared memory MCP server for Claude Code, Codex, Gemini CLI and Cursor. Cited, editable.
Search your AI chat history (ChatGPT, Claude, Codex) from any MCP client. Remote, private, read-only
An MCP server that gives your AI access to the source code and docs of all public github repos
Private persistent memory for Claude, ChatGPT & Gemini via MCP - semantic search, zero-code setup.
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