Conversation System

Overview

The AI Conversation Recording & Utilization Integration System v2.0 is an MCP server designed to transform raw dialogue with Claude into a structured, searchable knowledge base. By hooking directly into the MCP client on Claude Desktop, it automatically captures every utterance, compresses and indexes the content in Redis, and exposes a REST API for fine‑grained retrieval. This eliminates manual logging, reduces storage footprints by 30–40 %, and guarantees that every conversation is preserved in full fidelity.

Why It Matters

Developers who rely on AI assistants often struggle with information loss—the temptation to truncate long technical explanations or the inability to locate a past discussion about a specific tool. The Conversation System solves these pain points by providing adaptive detail levels: the newest five exchanges are stored verbatim, the next fifteen receive a medium‑depth summary that retains key technical terms, and older records are distilled to bullet‑point highlights. This tiered approach ensures that the most relevant context is always available without bloating storage or slowing response times.

Core Capabilities

• Smart Compression – Uses zlib to shrink conversation blobs while preserving all original data, enabling long‑term archival without sacrificing retrievability.
• Multi‑Layer Summaries – Generates three summary layers (short, medium, key points) via natural language processing, allowing users to request the appropriate level of detail with a single prompt.
• Automatic Technical Term Extraction – Detects programming languages, frameworks, and tools (e.g., Docker, Terraform, PostgreSQL) and indexes them in a dedicated Redis hash for instant semantic search.
• RESTful API – Exposes endpoints for compression analysis, adaptive context retrieval, and technical search, making it straightforward to integrate with existing tooling or build custom dashboards.

Real‑World Use Cases

• DevOps Knowledge Management – Capture every troubleshooting session, automatically tag it with relevant infrastructure keywords, and retrieve concise summaries when revisiting similar issues.
• Software Onboarding – New team members can query past conversations to quickly learn the project’s architecture, while senior developers keep a searchable archive of design decisions.
• Compliance Auditing – The system’s immutable, compressed logs provide a reliable audit trail for regulatory reviews without manual intervention.
• AI‑Driven Analytics – By feeding the indexed conversations into downstream analytics pipelines, teams can surface trends (e.g., recurring performance bottlenecks) and feed insights back into the AI assistant.

Integration Flow

1. Trigger – A user types “会話を記録して” in Claude Desktop.
2. Capture – The MCP client forwards the conversation to the server, which immediately compresses and stores it.
3. Processing – The server runs summarization, term extraction, and adaptive indexing in parallel.
4. Retrieval – Subsequent prompts such as “Dockerについて検索して” or “会話履歴を見せて” hit the REST API, which returns the appropriate summary layer or a list of relevant technical entries.

This seamless loop turns every interaction with Claude into a structured asset, dramatically boosting developer productivity and ensuring that knowledge never gets lost.