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
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
"""Quill Agent — Writing and content generation.
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Capabilities:
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- Documentation writing
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- Content creation
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- Text editing
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- Summarization
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"""
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from typing import Any
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from agents.base import BaseAgent
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QUILL_SYSTEM_PROMPT = """You are Quill, a writing and content generation specialist.
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Your role is to create, edit, and improve written content.
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## Capabilities
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- Documentation writing
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- Content creation
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- Text editing and refinement
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- Summarization
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- Style adaptation
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## Guidelines
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1. **Write clearly** — Plain language, logical structure
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2. **Know your audience** — Adapt tone and complexity
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3. **Be concise** — Cut unnecessary words
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4. **Use formatting** — Headers, lists, emphasis for readability
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## Tool Usage
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- Use write_file to save documents
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- Use read_file to review existing content
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- Use memory_search to check style preferences
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## Response Format
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Provide written content with:
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- Clear structure (headers, sections)
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- Appropriate tone for the context
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- Proper formatting (markdown)
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- Brief explanation of choices made
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You work for Timmy, the sovereign AI orchestrator. Create polished, professional content.
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"""
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class QuillAgent(BaseAgent):
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"""Writing and content specialist."""
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def __init__(self, agent_id: str = "quill") -> None:
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super().__init__(
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agent_id=agent_id,
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name="Quill",
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role="writing",
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system_prompt=QUILL_SYSTEM_PROMPT,
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tools=["write_file", "read_file", "memory_search"],
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)
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async def execute_task(self, task_id: str, description: str, context: dict) -> Any:
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"""Execute a writing task."""
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prompt = f"Create the requested written content:\n\nTask: {description}\n\nWrite professionally with clear structure."
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result = await self.run(prompt)
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return {
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"task_id": task_id,
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"agent": self.agent_id,
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"result": result,
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"status": "completed",
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}
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async def write_documentation(self, topic: str, format: str = "markdown") -> str:
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"""Write documentation for a topic."""
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prompt = f"Write comprehensive documentation for: {topic}\n\nFormat: {format}\nInclude: Overview, Usage, Examples, Notes"
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return await self.run(prompt)
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