mirror of
https://github.com/basicmachines-co/basic-memory
synced 2026-06-21 13:47:35 +00:00
093dab5f03
## Summary - Add comprehensive documentation to all MCP prompt modules - Enhance search prompt with detailed contextual output formatting - Implement consistent logging and docstring patterns across prompt utilities - Fix type checking in prompt modules ## Prompts Added/Enhanced - `search.py`: New formatted output with relevance scores, excerpts, and next steps - `recent_activity.py`: Enhanced with better metadata handling and documentation - `continue_conversation.py`: Improved context management ## Resources Added/Enhanced - `ai_assistant_guide`: Resource with description to give to LLM to understand how to use the tools ## Technical improvements - Added detailed docstrings to all prompt modules explaining their purpose and usage - Enhanced the search prompt with rich contextual output that helps LLMs understand results - Created a consistent pattern for formatting output across prompts - Improved error handling in metadata extraction - Standardized import organization and naming conventions - Fixed various type checking issues across the codebase This PR is part of our ongoing effort to improve the MCP's interaction quality with LLMs, making the system more helpful and intuitive for AI assistants to navigate knowledge bases. 🤖 Generated with [Claude Code](https://claude.ai/code) --------- Co-authored-by: phernandez <phernandez@basicmachines.co>
202 lines
7.2 KiB
Python
202 lines
7.2 KiB
Python
"""Note management tools for Basic Memory MCP server.
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These tools provide a natural interface for working with markdown notes
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while leveraging the underlying knowledge graph structure.
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"""
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from typing import Optional, List
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from loguru import logger
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import logfire
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from basic_memory.mcp.server import mcp
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from basic_memory.mcp.async_client import client
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from basic_memory.schemas import EntityResponse, DeleteEntitiesResponse
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from basic_memory.schemas.base import Entity
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from basic_memory.mcp.tools.utils import call_get, call_put, call_delete
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from basic_memory.schemas.memory import memory_url_path
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@mcp.tool(
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description="Create or update a markdown note. Returns a markdown formatted summary of the semantic content.",
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)
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async def write_note(
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title: str,
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content: str,
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folder: str,
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tags: Optional[List[str]] = None,
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) -> str:
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"""Write a markdown note to the knowledge base.
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The content can include semantic observations and relations using markdown syntax.
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Relations can be specified either explicitly or through inline wiki-style links:
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Observations format:
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`- [category] Observation text #tag1 #tag2 (optional context)`
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Examples:
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`- [design] Files are the source of truth #architecture (All state comes from files)`
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`- [tech] Using SQLite for storage #implementation`
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`- [note] Need to add error handling #todo`
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Relations format:
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- Explicit: `- relation_type [[Entity]] (optional context)`
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- Inline: Any `[[Entity]]` reference creates a relation
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Examples:
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`- depends_on [[Content Parser]] (Need for semantic extraction)`
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`- implements [[Search Spec]] (Initial implementation)`
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`- This feature extends [[Base Design]] and uses [[Core Utils]]`
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Args:
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title: The title of the note
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content: Markdown content for the note, can include observations and relations
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folder: the folder where the file should be saved
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tags: Optional list of tags to categorize the note
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Returns:
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A markdown formatted summary of the semantic content, including:
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- Creation/update status
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- File path and checksum
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- Observation counts by category
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- Relation counts (resolved/unresolved)
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- Tags if present
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"""
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with logfire.span("Writing note", title=title, folder=folder): # pyright: ignore [reportGeneralTypeIssues]
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logger.info(f"Writing note folder:'{folder}' title: '{title}'")
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# Create the entity request
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metadata = {"tags": [f"#{tag}" for tag in tags]} if tags else None
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entity = Entity(
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title=title,
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folder=folder,
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entity_type="note",
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content_type="text/markdown",
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content=content,
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entity_metadata=metadata,
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)
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# Create or update via knowledge API
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logger.info(f"Creating {entity.permalink}")
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url = f"/knowledge/entities/{entity.permalink}"
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response = await call_put(client, url, json=entity.model_dump())
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result = EntityResponse.model_validate(response.json())
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# Format semantic summary based on status code
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action = "Created" if response.status_code == 201 else "Updated"
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summary = [
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f"# {action} {result.file_path} ({result.checksum[:8] if result.checksum else 'unknown'})",
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f"permalink: {result.permalink}",
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]
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if result.observations:
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categories = {}
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for obs in result.observations:
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categories[obs.category] = categories.get(obs.category, 0) + 1
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summary.append("\n## Observations")
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for category, count in sorted(categories.items()):
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summary.append(f"- {category}: {count}")
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if result.relations:
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unresolved = sum(1 for r in result.relations if not r.to_id)
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resolved = len(result.relations) - unresolved
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summary.append("\n## Relations")
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summary.append(f"- Resolved: {resolved}")
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if unresolved:
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summary.append(f"- Unresolved: {unresolved}")
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summary.append("\nUnresolved relations will be retried on next sync.")
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if tags:
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summary.append(f"\n## Tags\n- {', '.join(tags)}")
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return "\n".join(summary)
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@mcp.tool(description="Read note content by title, permalink, relation, or pattern")
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async def read_note(identifier: str, page: int = 1, page_size: int = 10) -> str:
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"""Get note content in unified diff format.
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The content is returned in a unified diff inspired format:
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```
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--- memory://docs/example 2025-01-31T19:32:49 7d9f1c8b
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<document content>
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```
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Multiple documents (from relations or pattern matches) are separated by
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additional headers.
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Args:
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identifier: Can be one of:
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- Note title ("Project Planning")
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- Note permalink ("docs/example")
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- Relation path ("docs/example/depends-on/other-doc")
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- Pattern match ("docs/*-architecture")
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page: the page number of results to return (default 1)
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page_size: the number of results to return per page (default 10)
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Returns:
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Document content in unified diff format. For single documents, returns
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just that document's content. For relations or pattern matches, returns
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multiple documents separated by unified diff headers.
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Examples:
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# Single document
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content = await read_note("Project Planning")
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# Read by permalink
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content = await read_note("docs/architecture/file-first")
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# Follow relation
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content = await read_note("docs/architecture/depends-on/docs/content-parser")
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# Pattern matching
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content = await read_note("docs/*-architecture") # All architecture docs
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content = await read_note("docs/*/implements/*") # Find implementations
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Output format:
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```
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--- memory://docs/example 2025-01-31T19:32:49 7d9f1c8b
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<first document content>
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--- memory://docs/other 2025-01-30T15:45:22 a1b2c3d4
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<second document content>
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```
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The headers include:
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- Full memory:// URI for the document
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- Last modified timestamp
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- Content checksum
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"""
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with logfire.span("Reading note", identifier=identifier): # pyright: ignore [reportGeneralTypeIssues]
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logger.info(f"Reading note {identifier}")
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url = memory_url_path(identifier)
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response = await call_get(
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client, f"/resource/{url}", params={"page": page, "page_size": page_size}
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)
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return response.text
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@mcp.tool(description="Delete a note by title or permalink")
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async def delete_note(identifier: str) -> bool:
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"""Delete a note from the knowledge base.
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Args:
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identifier: Note title or permalink
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Returns:
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True if note was deleted, False otherwise
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Examples:
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# Delete by title
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delete_note("Meeting Notes: Project Planning")
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# Delete by permalink
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delete_note("notes/project-planning")
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"""
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with logfire.span("Deleting note", identifier=identifier): # pyright: ignore [reportGeneralTypeIssues]
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response = await call_delete(client, f"/knowledge/entities/{identifier}")
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result = DeleteEntitiesResponse.model_validate(response.json())
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return result.deleted
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