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Chat Analysis

This MCP server for chat analysis, developed by Robin L. M. Cheung, integrates vector embeddings and knowledge graphs to provide advanced chat data processing capabilities. Built with Python, it leverages Neo4j for graph storage, Qdrant for vector search, and sentence transformers for embedding generation. The implementation stands out by combining semantic similarity search with graph-based relationship analysis, enabling more nuanced understanding of chat conversations. By exposing these capabilities through standardized MCP endpoints, it allows AI systems to perform complex chat analysis tasks such as topic modeling, sentiment analysis, and user behavior tracking. This server is particularly useful for applications in customer support analytics, social media monitoring, or building intelligent chatbots that can learn from and adapt to conversation patterns.

Cognium trust score

92%

Tier

Verified

Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.

Scan details: Circle-IR · 2026-09-19 · Appeal

View full trust & usage report →

Metadata

Version: 1.0.0
Skill type: atomic
Execution layer: mcp-remote
Category: social-media
Source: PulseMCP
Repository: github.com/rebots-online/mcp-chat-analysis-server
Author type: human
Last scanned: 2026-09-19
Updated: 2026-09-19

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MCP

Resolve Chat Analysis from your agent

Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.

One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.

claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp

Swap --scope user for --scope project to commit it to .mcp.json.

Your client Same endpoint everywhere; only the config key differs. The full list is in the descriptor.

Claude Desktop · Settings → Connectors → Add custom connector

https://api.skillsregistry.net/mcp

Paste the URL as the connector endpoint. No authentication.

ChatGPT · Settings → Connectors → Create (developer mode) → MCP server URL

https://api.skillsregistry.net/mcp

Authentication: none. Works in chat and deep research — the server exposes the connector search and fetch tools.

Codex CLI · ~/.codex/config.toml

[mcp_servers.skillsregistry]
url = "https://api.skillsregistry.net/mcp"