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basicmachines-co-basic-memory/src/basic_memory/mcp/tools/notes.py
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Paul Hernandez 093dab5f03 feat: Add enhanced prompts and resources (#15)
## 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>
2025-02-27 20:48:56 -06:00

202 lines
7.2 KiB
Python

"""Note management tools for Basic Memory MCP server.
These tools provide a natural interface for working with markdown notes
while leveraging the underlying knowledge graph structure.
"""
from typing import Optional, List
from loguru import logger
import logfire
from basic_memory.mcp.server import mcp
from basic_memory.mcp.async_client import client
from basic_memory.schemas import EntityResponse, DeleteEntitiesResponse
from basic_memory.schemas.base import Entity
from basic_memory.mcp.tools.utils import call_get, call_put, call_delete
from basic_memory.schemas.memory import memory_url_path
@mcp.tool(
description="Create or update a markdown note. Returns a markdown formatted summary of the semantic content.",
)
async def write_note(
title: str,
content: str,
folder: str,
tags: Optional[List[str]] = None,
) -> str:
"""Write a markdown note to the knowledge base.
The content can include semantic observations and relations using markdown syntax.
Relations can be specified either explicitly or through inline wiki-style links:
Observations format:
`- [category] Observation text #tag1 #tag2 (optional context)`
Examples:
`- [design] Files are the source of truth #architecture (All state comes from files)`
`- [tech] Using SQLite for storage #implementation`
`- [note] Need to add error handling #todo`
Relations format:
- Explicit: `- relation_type [[Entity]] (optional context)`
- Inline: Any `[[Entity]]` reference creates a relation
Examples:
`- depends_on [[Content Parser]] (Need for semantic extraction)`
`- implements [[Search Spec]] (Initial implementation)`
`- This feature extends [[Base Design]] and uses [[Core Utils]]`
Args:
title: The title of the note
content: Markdown content for the note, can include observations and relations
folder: the folder where the file should be saved
tags: Optional list of tags to categorize the note
Returns:
A markdown formatted summary of the semantic content, including:
- Creation/update status
- File path and checksum
- Observation counts by category
- Relation counts (resolved/unresolved)
- Tags if present
"""
with logfire.span("Writing note", title=title, folder=folder): # pyright: ignore [reportGeneralTypeIssues]
logger.info(f"Writing note folder:'{folder}' title: '{title}'")
# Create the entity request
metadata = {"tags": [f"#{tag}" for tag in tags]} if tags else None
entity = Entity(
title=title,
folder=folder,
entity_type="note",
content_type="text/markdown",
content=content,
entity_metadata=metadata,
)
# Create or update via knowledge API
logger.info(f"Creating {entity.permalink}")
url = f"/knowledge/entities/{entity.permalink}"
response = await call_put(client, url, json=entity.model_dump())
result = EntityResponse.model_validate(response.json())
# Format semantic summary based on status code
action = "Created" if response.status_code == 201 else "Updated"
summary = [
f"# {action} {result.file_path} ({result.checksum[:8] if result.checksum else 'unknown'})",
f"permalink: {result.permalink}",
]
if result.observations:
categories = {}
for obs in result.observations:
categories[obs.category] = categories.get(obs.category, 0) + 1
summary.append("\n## Observations")
for category, count in sorted(categories.items()):
summary.append(f"- {category}: {count}")
if result.relations:
unresolved = sum(1 for r in result.relations if not r.to_id)
resolved = len(result.relations) - unresolved
summary.append("\n## Relations")
summary.append(f"- Resolved: {resolved}")
if unresolved:
summary.append(f"- Unresolved: {unresolved}")
summary.append("\nUnresolved relations will be retried on next sync.")
if tags:
summary.append(f"\n## Tags\n- {', '.join(tags)}")
return "\n".join(summary)
@mcp.tool(description="Read note content by title, permalink, relation, or pattern")
async def read_note(identifier: str, page: int = 1, page_size: int = 10) -> str:
"""Get note content in unified diff format.
The content is returned in a unified diff inspired format:
```
--- memory://docs/example 2025-01-31T19:32:49 7d9f1c8b
<document content>
```
Multiple documents (from relations or pattern matches) are separated by
additional headers.
Args:
identifier: Can be one of:
- Note title ("Project Planning")
- Note permalink ("docs/example")
- Relation path ("docs/example/depends-on/other-doc")
- Pattern match ("docs/*-architecture")
page: the page number of results to return (default 1)
page_size: the number of results to return per page (default 10)
Returns:
Document content in unified diff format. For single documents, returns
just that document's content. For relations or pattern matches, returns
multiple documents separated by unified diff headers.
Examples:
# Single document
content = await read_note("Project Planning")
# Read by permalink
content = await read_note("docs/architecture/file-first")
# Follow relation
content = await read_note("docs/architecture/depends-on/docs/content-parser")
# Pattern matching
content = await read_note("docs/*-architecture") # All architecture docs
content = await read_note("docs/*/implements/*") # Find implementations
Output format:
```
--- memory://docs/example 2025-01-31T19:32:49 7d9f1c8b
<first document content>
--- memory://docs/other 2025-01-30T15:45:22 a1b2c3d4
<second document content>
```
The headers include:
- Full memory:// URI for the document
- Last modified timestamp
- Content checksum
"""
with logfire.span("Reading note", identifier=identifier): # pyright: ignore [reportGeneralTypeIssues]
logger.info(f"Reading note {identifier}")
url = memory_url_path(identifier)
response = await call_get(
client, f"/resource/{url}", params={"page": page, "page_size": page_size}
)
return response.text
@mcp.tool(description="Delete a note by title or permalink")
async def delete_note(identifier: str) -> bool:
"""Delete a note from the knowledge base.
Args:
identifier: Note title or permalink
Returns:
True if note was deleted, False otherwise
Examples:
# Delete by title
delete_note("Meeting Notes: Project Planning")
# Delete by permalink
delete_note("notes/project-planning")
"""
with logfire.span("Deleting note", identifier=identifier): # pyright: ignore [reportGeneralTypeIssues]
response = await call_delete(client, f"/knowledge/entities/{identifier}")
result = DeleteEntitiesResponse.model_validate(response.json())
return result.deleted