Mnemex

Instructions

Implementation Reference

• src/cortexgraph/tools/read_graph.py:13-146 (handler)

  The complete implementation of the 'read_graph' MCP tool, including the @mcp.tool() decorator for registration, function definition with input parameters (serving as schema), and the full handler logic for reading the knowledge graph with status filtering, optional scoring, pagination, and statistics.
  @mcp.tool() def read_graph( status: str = "active", include_scores: bool = True, limit: int | None = None, page: int | None = None, page_size: int | None = None, ) -> dict[str, Any]: """ Read the entire knowledge graph of memories and relations. Returns the complete graph structure including all memories (with decay scores), all relations between memories, and statistics about the graph. **Pagination:** Results are paginated to help you navigate large knowledge graphs. Use `page` and `page_size` to retrieve specific portions of the graph. If searching for specific memories or patterns, increment `page` to see more results. Args: status: Filter memories by status - "active", "promoted", "archived", or "all". include_scores: Include decay scores and age in results. limit: Maximum number of memories to return (1-10,000). page: Page number to retrieve (1-indexed, default: 1). page_size: Number of memories per page (default: 10, max: 100). Returns: Dictionary with paginated graph including: - memories: List of memories for current page - relations: All relations (not paginated, for graph structure) - stats: Graph statistics - pagination: Metadata (page, page_size, total_count, total_pages, has_more) Examples: # Get first page of active memories read_graph(status="active", page=1, page_size=10) # Get next page read_graph(status="active", page=2, page_size=10) # Larger page for overview read_graph(status="active", page=1, page_size=50) Raises: ValueError: If status is invalid or limit is out of range. """ # Input validation valid_statuses = {"active", "promoted", "archived", "all"} if status not in valid_statuses: raise ValueError(f"status must be one of {valid_statuses}, got: {status}") if limit is not None: limit = validate_positive_int(limit, "limit", min_value=1, max_value=10000) # Only validate pagination if explicitly requested pagination_requested = page is not None or page_size is not None status_filter = None if status == "all" else MemoryStatus(status) graph = db.get_knowledge_graph(status=status_filter) if limit and limit > 0: graph.memories = graph.memories[:limit] graph.stats["limited_to"] = limit now = int(time.time()) memories_data = [] for memory in graph.memories: mem_data = { "id": memory.id, "content": memory.content, "entities": memory.entities, "tags": memory.meta.tags, "created_at": memory.created_at, "last_used": memory.last_used, "use_count": memory.use_count, "strength": memory.strength, "status": memory.status.value, } if include_scores: score = calculate_score( use_count=memory.use_count, last_used=memory.last_used, strength=memory.strength, now=now, ) mem_data["score"] = round(score, 4) mem_data["age_days"] = round((now - memory.created_at) / 86400, 1) memories_data.append(mem_data) relations_data = [ { "id": rel.id, "from": rel.from_memory_id, "to": rel.to_memory_id, "type": rel.relation_type, "strength": round(rel.strength, 4), "created_at": rel.created_at, } for rel in graph.relations ] # Apply pagination only if requested if pagination_requested: # Validate and get non-None values valid_page, valid_page_size = validate_pagination_params(page, page_size) paginated_memories = paginate_list( memories_data, page=valid_page, page_size=valid_page_size ) return { "success": True, "memories": paginated_memories.items, "relations": relations_data, "stats": { "total_memories": graph.stats["total_memories"], "total_relations": graph.stats["total_relations"], "avg_score": round(graph.stats["avg_score"], 4), "avg_use_count": round(graph.stats["avg_use_count"], 2), "status_filter": graph.stats["status_filter"], }, "pagination": paginated_memories.to_dict(), } else: # No pagination - return all memories return { "success": True, "memories": memories_data, "relations": relations_data, "stats": { "total_memories": graph.stats["total_memories"], "total_relations": graph.stats["total_relations"], "avg_score": round(graph.stats["avg_score"], 4), "avg_use_count": round(graph.stats["avg_use_count"], 2), "status_filter": graph.stats["status_filter"], }, }