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"""MCP Server for Project Memory - The Brain of the system."""
import asyncio
import json
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
from app.database import init_db
from app.tools.projects import create_project, list_projects, join_project
from app.tools.tasks import create_task, list_tasks, list_activity
from app.tools.memory import complete_task, memory_search
from app.smart_query import smart_query
from app.vectorstore import init_vectorstore
# Initialize the MCP server
server = Server("project-memory")
# Tool definitions
TOOLS = [
# Project Tools
Tool(
name="create_project",
description="Create a new project and add the creator as owner",
inputSchema={
"type": "object",
"properties": {
"name": {"type": "string", "description": "Project name"},
"description": {"type": "string", "description": "Project description"},
"userId": {"type": "string", "description": "ID of the user creating the project"}
},
"required": ["name", "description", "userId"]
}
),
Tool(
name="list_projects",
description="List all projects for a user",
inputSchema={
"type": "object",
"properties": {
"userId": {"type": "string", "description": "ID of the user"}
},
"required": ["userId"]
}
),
Tool(
name="join_project",
description="Add a user to an existing project",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "ID of the project to join"},
"userId": {"type": "string", "description": "ID of the user joining"}
},
"required": ["projectId", "userId"]
}
),
# Task Tools
Tool(
name="create_task",
description="Create a new task in a project",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "ID of the project"},
"title": {"type": "string", "description": "Task title"},
"description": {"type": "string", "description": "Task description"},
"assignedTo": {"type": "string", "description": "User ID to assign the task to"}
},
"required": ["projectId", "title"]
}
),
Tool(
name="list_tasks",
description="List all tasks for a project, optionally filtered by status",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "ID of the project"},
"status": {
"type": "string",
"enum": ["todo", "in_progress", "done"],
"description": "Optional status filter"
}
},
"required": ["projectId"]
}
),
Tool(
name="list_activity",
description="Get recent activity (log entries) for a project",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "ID of the project"},
"limit": {"type": "number", "default": 20, "description": "Maximum number of entries"}
},
"required": ["projectId"]
}
),
# Memory Tools (placeholders for Dev B to implement)
Tool(
name="complete_task",
description="Mark a task as complete with documentation. Generates AI documentation and stores embeddings.",
inputSchema={
"type": "object",
"properties": {
"taskId": {"type": "string", "description": "ID of the task to complete"},
"projectId": {"type": "string", "description": "ID of the project"},
"userId": {"type": "string", "description": "ID of the user completing the task"},
"whatIDid": {"type": "string", "description": "Description of what was done"},
"codeSnippet": {"type": "string", "description": "Optional code snippet"}
},
"required": ["taskId", "projectId", "userId", "whatIDid"]
}
),
Tool(
name="memory_search",
description="Semantic search across project memory. Returns relevant log entries and AI-synthesized answer.",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "ID of the project to search"},
"query": {"type": "string", "description": "Search query"},
"filters": {
"type": "object",
"properties": {
"userId": {"type": "string"},
"dateFrom": {"type": "string"},
"dateTo": {"type": "string"},
"tags": {"type": "array", "items": {"type": "string"}}
},
"description": "Optional filters"
}
},
"required": ["projectId", "query"]
}
),
# Smart Query Tool - LLM-first natural language queries
Tool(
name="smart_query",
description="Natural language query with context awareness. Understands 'yesterday', 'I', user names, task references. Use for questions like 'What did I do yesterday?' or 'How does auth work?'",
inputSchema={
"type": "object",
"properties": {
"projectId": {"type": "string", "description": "Project ID to query"},
"query": {"type": "string", "description": "Natural language query"},
"currentUserId": {"type": "string", "description": "ID of user making the query"},
"currentDatetime": {"type": "string", "description": "Current datetime ISO format (optional)"}
},
"required": ["projectId", "query", "currentUserId"]
}
)
]
@server.list_tools()
async def handle_list_tools() -> list[Tool]:
"""Return all available tools."""
return TOOLS
@server.call_tool()
async def handle_call_tool(name: str, arguments: dict) -> list[TextContent]:
"""Handle tool calls by dispatching to the appropriate function."""
try:
match name:
# Project Tools
case "create_project":
result = create_project(
name=arguments["name"],
description=arguments["description"],
user_id=arguments["userId"]
)
case "list_projects":
result = list_projects(user_id=arguments["userId"])
case "join_project":
result = join_project(
project_id=arguments["projectId"],
user_id=arguments["userId"]
)
# Task Tools
case "create_task":
result = create_task(
project_id=arguments["projectId"],
title=arguments["title"],
description=arguments.get("description"),
assigned_to=arguments.get("assignedTo")
)
case "list_tasks":
result = list_tasks(
project_id=arguments["projectId"],
status=arguments.get("status")
)
case "list_activity":
result = list_activity(
project_id=arguments["projectId"],
limit=arguments.get("limit", 20)
)
# Memory Tools (Dev B - implemented)
case "complete_task":
result = await complete_task(
task_id=arguments["taskId"],
project_id=arguments["projectId"],
user_id=arguments["userId"],
what_i_did=arguments["whatIDid"],
code_snippet=arguments.get("codeSnippet")
)
case "memory_search":
result = await memory_search(
project_id=arguments["projectId"],
query=arguments["query"],
filters=arguments.get("filters")
)
# Smart Query Tool
case "smart_query":
result = await smart_query(
project_id=arguments["projectId"],
query=arguments["query"],
current_user_id=arguments["currentUserId"],
current_datetime=arguments.get("currentDatetime")
)
case _:
result = {"error": f"Unknown tool: {name}"}
return [TextContent(type="text", text=json.dumps(result, default=str))]
except Exception as e:
error_result = {"error": str(e)}
return [TextContent(type="text", text=json.dumps(error_result))]
async def main():
"""Run the MCP server."""
# Initialize database tables
init_db()
# Initialize vector store for embeddings
init_vectorstore()
print("Project Memory MCP Server starting...", flush=True)
async with stdio_server() as (read_stream, write_stream):
await server.run(
read_stream,
write_stream,
server.create_initialization_options()
)
if __name__ == "__main__":
asyncio.run(main())
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