mirror of
https://github.com/basicmachines-co/basic-memory
synced 2026-06-21 13:47:35 +00:00
phernandez
134 lines
4.7 KiB
Python
134 lines
4.7 KiB
Python
"""Discussion context tools for Basic Memory MCP server."""
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from typing import Optional, List
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from loguru import logger
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from basic_memory.mcp.async_client import client
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from basic_memory.mcp.server import mcp
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from basic_memory.mcp.tools.utils import call_get
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from basic_memory.schemas.memory import GraphContext, MemoryUrl
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from basic_memory.schemas.search import SearchItemType
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from basic_memory.schemas.base import TimeFrame
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@mcp.tool(
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description="""Build context from a memory:// URI to continue conversations naturally.
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Use this to follow up on previous discussions or explore related topics.
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Timeframes use natural language support - examples:
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- "2 days ago"
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- "last week"
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- "today"
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- "3 months ago"
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Or standard formats like "7d", "24h"
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""",
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)
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async def build_context(
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url: MemoryUrl,
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depth: Optional[int] = 1,
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timeframe: Optional[TimeFrame] = "7d",
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max_results: int = 10,
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) -> GraphContext:
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"""Get context needed to continue a discussion.
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This tool enables natural continuation of discussions by loading relevant context
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from memory:// URIs. It uses pattern matching to find relevant content and builds
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a rich context graph of related information.
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Args:
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url: memory:// URI pointing to discussion content (e.g. memory://specs/search)
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depth: How many relation hops to traverse (1-3 recommended for performance)
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timeframe: How far back to look. Supports natural language like "2 days ago", "last week"
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max_results: Maximum number of results to return (default: 10)
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Returns:
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GraphContext containing:
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- primary_results: Content matching the memory:// URI
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- related_results: Connected content via relations
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- metadata: Context building details
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Examples:
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# Continue a specific discussion
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build_context("memory://specs/search")
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# Get deeper context about a component
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build_context("memory://components/memory-service", depth=2)
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# Look at recent changes to a specification
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build_context("memory://specs/document-format", timeframe="today")
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# Research the history of a feature
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build_context("memory://features/knowledge-graph", timeframe="3 months ago")
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"""
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logger.info(f"Building context from {url}")
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# Map directly to the memory endpoint
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memory_url = MemoryUrl.validate(url)
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response = await call_get(
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client,
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f"/memory/{memory_url.relative_path()}",
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params={"depth": depth, "timeframe": timeframe, "max_results": max_results},
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)
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return GraphContext.model_validate(response.json())
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@mcp.tool(
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description="""Get recent activity from across the knowledge base.
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Timeframe supports natural language formats like:
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- "2 days ago"
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- "last week"
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- "3 weeks"
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- "2 months"
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- "yesterday"
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- "today"
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Or standard formats like "7d", "24h"
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""",
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)
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async def recent_activity(
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types: List[SearchItemType] = None,
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depth: Optional[int] = 1,
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timeframe: Optional[TimeFrame] = "7d",
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max_results: int = 10,
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) -> GraphContext:
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"""Get recent activity across the knowledge base.
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Args:
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types: Filter by entity types (["entity", "relation", "observation"]). If None, returns all types.
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depth: How many relation hops to traverse when building context (1-3 recommended)
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timeframe: How far back to look. Supports natural language like "2 days ago", "last week"
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max_results: Maximum number of results to return (default: 10)
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Returns:
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GraphContext containing:
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- primary_results: Latest activities matching the filters
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- related_results: Connected content via relations
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- metadata: Query details and statistics
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Examples:
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# Get all activity from last week
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recent_activity(timeframe="last week")
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# Get only entity changes from yesterday
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recent_activity(types=["entity"], timeframe="yesterday")
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# Track recent specification changes
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recent_activity(types=["entity"], depth=2, timeframe="3 days ago")
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# Follow recent relation changes
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recent_activity(types=["relation"], timeframe="today")
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Notes:
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- Higher depth values (>3) may impact performance with large result sets
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- For focused queries, consider using build_context with a specific URI
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- Max timeframe is 1 year in the past
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"""
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logger.info(
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f"Getting recent activity from {types}, depth={depth}, timeframe={timeframe}, max_results={max_results}"
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)
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response = await client.get(
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"/memory/recent",
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params={"depth": depth, "timeframe": timeframe, "max_results": max_results, "types": types},
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)
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return GraphContext.model_validate(response.json())
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