mirror of
https://github.com/basicmachines-co/basic-memory
synced 2026-06-21 13:47:35 +00:00
Paul Hernandez
12b51522bc
Signed-off-by: phernandez <paul@basicmachines.co> Co-authored-by: Claude <noreply@anthropic.com>
124 lines
4.5 KiB
Python
124 lines
4.5 KiB
Python
"""Build context tool for Basic Memory MCP server."""
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from typing import Optional
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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.mcp.project_session import get_active_project
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from basic_memory.schemas.base import TimeFrame
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from basic_memory.schemas.memory import (
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GraphContext,
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MemoryUrl,
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memory_url_path,
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)
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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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Memory URL Format:
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- Use paths like "folder/note" or "memory://folder/note"
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- Pattern matching: "folder/*" matches all notes in folder
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- Valid characters: letters, numbers, hyphens, underscores, forward slashes
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- Avoid: double slashes (//), angle brackets (<>), quotes, pipes (|)
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- Examples: "specs/search", "projects/basic-memory", "notes/*"
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Timeframes support natural language like:
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- "2 days ago", "last week", "today", "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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page: int = 1,
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page_size: int = 10,
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max_related: int = 10,
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project: Optional[str] = None,
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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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page: Page number of results to return (default: 1)
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page_size: Number of results to return per page (default: 10)
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max_related: Maximum number of related results to return (default: 10)
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project: Optional project name to build context from. If not provided, uses current active project.
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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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# Build context from specific project
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build_context("memory://specs/search", project="work-project")
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"""
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logger.info(f"Building context from {url}")
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# URL is already validated and normalized by MemoryUrl type annotation
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# Get the active project first to check project-specific sync status
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active_project = get_active_project(project)
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# Check migration status and wait briefly if needed
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from basic_memory.mcp.tools.utils import wait_for_migration_or_return_status
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migration_status = await wait_for_migration_or_return_status(
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timeout=5.0, project_name=active_project.name
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)
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if migration_status: # pragma: no cover
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# Return a proper GraphContext with status message
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from basic_memory.schemas.memory import MemoryMetadata
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from datetime import datetime
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return GraphContext(
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results=[],
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metadata=MemoryMetadata(
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depth=depth or 1,
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timeframe=timeframe,
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generated_at=datetime.now(),
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primary_count=0,
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related_count=0,
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uri=migration_status, # Include status in metadata
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),
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)
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project_url = active_project.project_url
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response = await call_get(
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client,
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f"{project_url}/memory/{memory_url_path(url)}",
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params={
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"depth": depth,
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"timeframe": timeframe,
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"page": page,
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"page_size": page_size,
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"max_related": max_related,
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},
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)
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return GraphContext.model_validate(response.json())
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