claude-find
claude-find
Rufen Sie Deep Memory aus Ihren Claude Code-Sitzungen ab – genau dann, wenn Sie es brauchen.

Semantische Suche über alle Ihre vergangenen Claude Code-Sitzungen. Findet Kontext anhand von Bedeutung und Schlüsselwörtern. Durchsucht die rohen Transkripte der Konversationen, nicht komprimierte Zusammenfassungen, sodass Claude das vollständige Bild erhält: Überlegungen, Einschränkungen, fehlgeschlagene Ansätze und Entscheidungen.
Einrichtung
brew install bun ollama
bunx claude-find setupsetup startet Ollama, lädt das Embedding-Modell herunter, setzt die Sitzungsaufbewahrung auf permanent und registriert den MCP-Server bei Claude Code. Sitzungen werden beim Start im Hintergrund indiziert. Suchen funktionieren sofort und liefern zunehmend vollständigere Ergebnisse, während die Indizierung fortgesetzt wird.
Installieren Sie Bun und Ollama und führen Sie dann bunx claude-find setup aus. Es erkennt Ihre Plattform und führt Sie durch alle fehlenden Schritte.
Related MCP server: am-memory
Verwendung
In jeder Claude Code-Sitzung:
/find that database migration we discussed last week
/find why we chose websockets over polling
/find the session where we kept getting timeout errors
/find refactoring the payment module across all projectsClaude durchsucht Ihre vergangenen Sitzungen semantisch, findet die relevanten Konversationen und synthetisiert den Kontext: was versucht wurde, was fehlgeschlagen ist, welche Einschränkungen Sie festgelegt haben und welche Entscheidungen getroffen wurden.
Funktionsweise
Indiziert alle Claude Code-Sitzungs-JSONL-Dateien aus
~/.claude/projects/Extrahiert Benutzer- und Assistentennachrichten, kompakte Zusammenfassungen sowie Dateipfade aus Tool-Aufrufen
Anreichert jeden Block mit Metadaten-Kontext (Projekt, Branch, Dateien, Datum) für eine bessere Abfrage
Erstellt Embeddings für Konversationsblöcke mittels qwen3-embedding über Ollama (GPU-beschleunigt)
Sucht mit hybrider semantischer + Schlüsselwortsuche (FTS5), zusammengeführt über Reciprocal Rank Fusion
Gibt rohe Konversationsblöcke zurück, damit Claude sie mit vollem Kontext synthetisieren kann
Führen Sie nach einem Upgrade bunx claude-find index aus, um den Index mit den neuesten Verbesserungen neu zu erstellen.
Was macht dies anders
Durchsucht rohe Transkripte. Nichts geht durch Komprimierung verloren.
Rückwirkend: Funktioniert sofort mit allen bestehenden Sitzungen. Keine Hooks erforderlich.
Permanenter Verlauf: Die Einrichtung deaktiviert die 30-tägige Sitzungsbereinigung von Claude Code, sodass Ihre Sitzungen für immer durchsuchbar sind.
Nicht blockierend: Indiziert im Hintergrund beim Start. Suchen funktionieren sofort, auch während der Indizierung.
Verwendet kompakte Zusammenfassungen: Claudes eigenes Sitzungsverständnis, im Ranking verstärkt.
Indiziert Tool-Aufruf-Metadaten: Suche nach bearbeiteten Dateien, aufgetretenen Fehlern.
Schnell: Ollama + GPU hält die Indizierung schnell und speicherbegrenzt.
Anforderungen
Lizenz
MIT
Available Tools
1 toolsearch_sessionsA
Search the full conversation history from past Claude Code sessions stored in ~/.claude/projects/. This tool has access to the complete raw transcripts of all previous sessions — including the actual back-and-forth discussion, reasoning, failed approaches, user constraints, and code decisions. Use this tool FIRST whenever the user mentions anything from a past session, asks 'what did we discuss', 'pull in context from', 'remember when we', 'how did we handle', or references any prior work. This tool searches semantically — the user doesn't need to remember exact words. Much more detailed than built-in memory.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What to search for — natural language description of the past session or topic | |
| max_sessions | No | Max sessions to return (default 3, max 5) | |
| max_chunks | No | Max conversation chunks per session (default 3, max 3) | |
| scope | No | 'current' searches only the current project (default), 'all' searches across all projects. Use 'all' when user says 'across all projects' or doesn't specify a project. | current |
| project_filter | No | Filter to a specific project by name (e.g. 'visk', 'myapp'). Use when user says 'in visk' or 'in the payments project'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the scope of data ('complete raw transcripts', 'discussion, reasoning, failed approaches') and the semantic search nature. It does not mention any destructive actions or potential privacy concerns, but for a read-only search tool, the disclosure is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with four sentences, starting with the core purpose and then usage guidance. Every sentence contributes meaning, though it could be slightly trimmed without loss.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately explains the tool's function and when to use it, but it does not describe the return value format or content. The schema hints at output via parameters like max_sessions and max_chunks, but without an output schema, the description should explicitly state what is returned (e.g., matching sessions with chunks).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions. The tool description adds context about the underlying data ('complete raw transcripts') that enriches understanding of what the 'query' parameter searches over, going beyond the schema's literal description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches 'full conversation history from past Claude Code sessions' and specifies the exact storage location. It distinguishes itself from built-in memory by claiming to be 'much more detailed', which is useful even though no siblings are listed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool first, listing example phrases like 'what did we discuss' and 'remember when we'. It also explains that searches are semantic, reducing the need for exact words, which is a clear usage recommendation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
search_sessions
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined.
The single tool name 'search_sessions' follows a consistent verb_noun pattern, though there are no other tools to compare against.
A single tool for searching is borderline thin; most servers of this scope would benefit from at least 2-3 tools (e.g., list_sessions, get_session). The count is at the low end of reasonable.
The tool covers the core search functionality well, but lacks complementary tools such as listing available sessions or retrieving full transcripts by ID, which would make the surface more complete.
Maintenance
Related MCP Connectors
Persistent memory for Claude Code and Cursor. Stop re-explaining your project every session.
Persistent cross-session memory shared by Codex, Claude Code, ChatGPT, and other AI agents.
Shared memory for coding agents. Stop re-explaining your codebase every session.
- SeturosOAuthcom.seturos
Shared work memory for Claude Code, Codex, Cursor and chat, scoped to each repository.
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables AI assistants to query and analyze past Claude Code sessions, providing structured insights like file changes, decisions, errors, and git history across projects.119 npm1MIT
- AlicenseNot gradedqualityDmaintenancePersistent memory for Claude Code — a self-evolving knowledge layer that survives across sessions, grows from every conversation, and surfaces relevant context automatically.14MIT
- AlicenseNot gradedqualityDmaintenanceProvides persistent memory for Claude Code, automatically extracting and surfacing relevant context from past sessions to avoid re-explaining issues and decisions.MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude Code to search and retrieve past chat history from Claude.ai exports and Claude Code sessions, allowing the AI to reference previous conversations and decisions.MIT