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Penfield Memory

by io.github.penfieldlabs
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update_memory

Update an existing memory.

Modifies properties of a stored memory by its UUID.

Args:
    memory_id: UUID of memory to update
    content: New content (optional)
    importance: New importance score (optional, 0.0-1.0)
    tags: New tags (optional, replaces existing tags)
    ctx: MCP context (automatically provided)
    
Returns:
    Dict with success status and updated memory_id
    
Examples:
    >>> await update_memory("uuid-here", importance=0.9)
    {'success': True, 'memory_id': 'uuid-here'}
    
    >>> await update_memory("uuid-123", tags=["python", "errors", "important"])
    {'success': True, 'memory_id': 'uuid-123'}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
contentNo
memory_idYes
importanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false and openWorldHint=true, so the description correctly adds that it modifies an existing memory, requires a UUID, and replaces tags (not merges). This adds context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a one-liner purpose, then Args, Returns, and Examples. It is slightly verbose but every sentence adds value. The main purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers usage, parameters, return format, and examples. Given the moderate complexity (4 params, no enums) and presence of an output schema, it is nearly complete but could mention error cases or prerequisites.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description provides detailed explanations for all 4 parameters (memory_id, content, importance, tags) including types, optionality, and behavior (e.g., 'replaces existing tags'). This fully compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Update an existing memory' with specific properties (content, importance, tags), using a specific verb and resource. It distinguishes from siblings like 'store' (create) and 'recall' (read) by focusing on mutation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains required vs optional parameters but does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'store' for creation) or when not to use it. Examples show usage but lack context for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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