forked from Rockachopa/Timmy-time-dashboard
a719c7538d
## 1. MCP (Model Context Protocol) Implementation ### Registry (src/mcp/registry.py) - Tool registration with JSON schemas - Dynamic tool discovery - Health tracking per tool - Metrics collection (latency, error rates) - @register_tool decorator for easy registration ### Server (src/mcp/server.py) - MCPServer class implementing MCP protocol - MCPHTTPServer for FastAPI integration - Standard endpoints: list_tools, call_tool, get_schema ### Schemas (src/mcp/schemas/base.py) - create_tool_schema() helper - Common parameter types - Standard return types ### Bootstrap (src/mcp/bootstrap.py) - Automatic tool module loading - Status reporting ## 2. MCP-Compliant Tools (src/tools/) | Tool | Purpose | Category | |------|---------|----------| | web_search | DuckDuckGo search | research | | read_file | File reading | files | | write_file | File writing (confirmation) | files | | list_directory | Directory listing | files | | python | Python code execution | code | | memory_search | Vector memory search | memory | All tools have proper schemas, error handling, and MCP registration. ## 3. Event Bus (src/events/bus.py) - Async publish/subscribe pattern - Pattern matching with wildcards (agent.task.*) - Event history tracking - Concurrent handler execution - Module-level singleton for system-wide use ## 4. Sub-Agents (src/agents/) All agents inherit from BaseAgent with: - Agno Agent integration - MCP tool registry access - Event bus connectivity - Structured logging ### Agent Roster | Agent | Role | Tools | Purpose | |-------|------|-------|---------| | Seer | Research | web_search, read_file, memory_search | Information gathering | | Forge | Code | python, write_file, read_file | Code generation | | Quill | Writing | write_file, read_file, memory_search | Content creation | | Echo | Memory | memory_search, read_file, write_file | Context retrieval | | Helm | Routing | memory_search | Task routing decisions | | Timmy | Orchestrator | All tools | Coordination & user interface | ### Timmy Orchestrator - Analyzes user requests - Routes to appropriate sub-agent - Handles direct queries - Manages swarm coordination - create_timmy_swarm() factory function ## 5. Integration All components wired together: - Tools auto-register on import - Agents connect to event bus - MCP server provides HTTP API - Ready for dashboard integration ## Tests - All 973 existing tests pass - New components tested manually - Import verification successful Next steps: Cascade Router, Self-Upgrade Loop, Dashboard integration
71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
"""Memory search tool.
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MCP-compliant tool for searching Timmy's memory.
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"""
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import logging
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from typing import Any
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from mcp.registry import register_tool
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from mcp.schemas.base import create_tool_schema, PARAM_STRING, PARAM_INTEGER, RETURN_STRING
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logger = logging.getLogger(__name__)
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MEMORY_SEARCH_SCHEMA = create_tool_schema(
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name="memory_search",
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description="Search Timmy's memory for past conversations, facts, and context. Use when user asks about previous discussions or when you need to recall something from memory.",
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parameters={
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"query": {
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**PARAM_STRING,
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"description": "What to search for in memory",
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},
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"top_k": {
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**PARAM_INTEGER,
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"description": "Number of results to return (1-10)",
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"default": 5,
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"minimum": 1,
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"maximum": 10,
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},
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},
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required=["query"],
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returns=RETURN_STRING,
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)
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def memory_search(query: str, top_k: int = 5) -> str:
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"""Search Timmy's memory.
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Args:
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query: Search query
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top_k: Number of results
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Returns:
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Relevant memories from past conversations
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"""
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try:
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from timmy.semantic_memory import memory_search as semantic_search
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results = semantic_search(query, top_k=top_k)
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if not results:
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return "No relevant memories found."
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formatted = ["Relevant memories from past conversations:", ""]
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for i, (content, score) in enumerate(results, 1):
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relevance = "🔥" if score > 0.8 else "⭐" if score > 0.5 else "📄"
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formatted.append(f"{relevance} [{i}] (score: {score:.2f})")
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formatted.append(f" {content[:300]}...")
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formatted.append("")
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return "\n".join(formatted)
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except Exception as exc:
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logger.error("Memory search failed: %s", exc)
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return f"Memory search error: {exc}"
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# Register with MCP
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register_tool(name="memory_search", schema=MEMORY_SEARCH_SCHEMA, category="memory")(memory_search)
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