Enables semantic analysis of chat conversations through vector embeddings and knowledge graphs.
Chat Analysis MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. Enables semantic analysis of chat conversations through vector embeddings and knowledge graphs.
A Model Context Protocol (MCP) server that enables semantic analysis of chat conversations through vector embeddings and knowledge graphs. This server provides tools for analyzing chat data, performing semantic search, extracting concepts, and analyzing conversation patterns.
Once Chat Analysis is connected, these are the calls the assistant has available:
import_conversations — Import and analyze chat conversations python { "source_path": "/path/to/export.zip", "format": "openai_native" # or html, markdown, json }semantic_search — Search conversations by semantic similarity python { "query": "machine learning applications", "limit": 10, "min_score": 0.7 }analyze_metrics — Analyze conversation metrics python { "conversation_id": "conv-123", "metrics": [ "message_frequency", "response_times", "topic_diversity" ] }extract_concepts — Extract and analyze concepts python { "conversation_id": "conv-123", "min_relevance": 0.5, "max_concepts": 10 }You will need 4 environment variables: QDRANT_URL, NEO4J_URL, NEO4J_USER, NEO4J_PASSWORD. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
The server ships on PyPI as mcp-chat-analysis-server, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Plenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Chat Analysis's toolset — import_conversations, semantic_search, analyze_metrics and 1 more — is a fair guide to whether it matches your workflow. It is maintained by rebots-online; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| import_conversations | Import and analyze chat conversations python { "source_path": "/path/to/export.zip", "format": "openai_native" # or html, markdown, json } |
| semantic_search | Search conversations by semantic similarity python { "query": "machine learning applications", "limit": 10, "min_score": 0.7 } |
| analyze_metrics | Analyze conversation metrics python { "conversation_id": "conv-123", "metrics": [ "message_frequency", "response_times", "topic_diversity" ] } |
| extract_concepts | Extract and analyze concepts python { "conversation_id": "conv-123", "min_relevance": 0.5, "max_concepts": 10 } |
{
"mcpServers": {
"chat-analysis": {
"command": "uvx",
"args": ["mcp-chat-analysis-server"],
"env": {
"QDRANT_URL": "your-value",
"NEO4J_URL": "your-value",
"NEO4J_USER": "your-value",
"NEO4J_PASSWORD": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| NEO4J_URL | Endpoint or connection string the server talks to. | Yes |
| NEO4J_USER | Configuration value read at startup. | Optional |
| NEO4J_PASSWORD | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.