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declare_preference

Store user-declared preferences or facts directly into AI memory with full confidence, ensuring consistent recall across interactions.

Instructions

Let the user proactively tell the AI about themselves. User declarations are classified by the engine but always stored with confidence=1.0 and source_layer='declaration'. Active declaration + passive classification = complete memory coverage.

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": {
      "message": "<message>"
    },
    "name": "declare_preference"
  }
}

Input Schema

Name	Required	Description	Default
`message`	Yes	What the user wants to declare (e.g., 'I prefer dark mode', 'We use PostgreSQL', 'My timezone is UTC+8')

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Jun 20, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and discloses key behaviors: declarations are stored with confidence=1.0 and source_layer='declaration', and they are classified by the engine. This provides transparency about how declarations are processed. It does not detail side effects like overwriting, but the core behavior is clear.

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

Conciseness5/5

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

The description is three sentences: the first captures the core purpose, the second details technical storage specifics, and the third ties to the broader memory system. No superfluous words or redundancy. It is front-loaded and efficient.

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

Completeness3/5

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

The tool is simple (1 required parameter, no output schema, no annotations). The description explains what it does and how it works internally. However, it does not mention what the tool returns (e.g., confirmation or status) or any prerequisites. For a simple tool, it is mostly complete but could include return behavior.

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

Parameters3/5

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

The schema already provides 100% coverage with a clear description and example for the 'message' parameter. The description adds context about classification and storage but does not add new semantic meaning beyond what the schema offers. Baseline 3 is appropriate as the schema does the heavy lifting.

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 clearly states the tool's purpose: letting users proactively tell the AI about themselves. It specifies the action and resource, and the mention of classification and storage distinguishes it from siblings like add_rule or forget_memory. However, it could explicitly differentiate from classify_message to enhance clarity.

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 provides implicit usage context by contrasting active declaration with passive classification. It suggests using this tool when the user wants to explicitly state something. However, it lacks explicit guidance on when not to use it or mention alternatives, such as classify_and_remember for mixed cases.

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