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Preference Memory MCP

Preference Memory MCP

by efficjump
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_observe

Extract preference candidates from observations without storing raw data, using host sampling to preserve privacy.

Instructions

Use host sampling to extract pending candidates without storing the raw observation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "observation": "<observation>"
    },
    "name": "memory_observe"
  }
}

Input Schema

NameRequiredDescriptionDefault
scopeNo
strengthNosoft
observationYes

Output Schema

NameRequiredDescriptionDefault
statusYes
messageYes
candidatesNo
created_countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

Annotations are all false, providing minimal behavioral cues. The description adds that it does not store raw observation, but does not disclose side effects, state changes, or whether the extraction modifies internal data. Key behavioral traits are missing.

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 a single sentence, very concise and front-loaded. However, it is too brief to cover necessary guidance, which slightly reduces structure effectiveness.

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

Completeness2/5

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

Given the tool has 3 parameters including a nested Scope object and an enum, and has sibling tools, the description is inadequate. It does not explain how parameters affect output, what 'host sampling' means, or what the return value contains, despite having an output schema.

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

Parameters2/5

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

Schema description coverage is 0%, meaning no descriptions on top-level properties. The description does not explain the 'observation', 'scope', or 'strength' parameters. The Scope definition in schema has a description, but parameters overall are underspecified.

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

Purpose4/5

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

The description uses the verb 'extract' and clarifies the tool extracts pending candidates without storing the raw observation. It distinguishes from sibling tools like memory_remember (store) and memory_recall (retrieve). However, 'pending candidates' is not defined, so purpose is not fully precise.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs alternatives. Implies usage for host sampling, but no when-not conditions or comparisons to siblings like memory_resolve or memory_review.

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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