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set_preference

Stores a named preference and value so USERDATA can remember how Michael wants tasks, settings, and research workflows handled.

Instructions

Remembers how Michael wants things done.

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": {
      "preference_key": "<preference_key>",
      "preference_value": "<preference_value>"
    },
    "name": "set_preference"
  }
}

Input Schema

NameRequiredDescriptionDefault
preference_keyYes
preference_valueYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.7/5.0
Behavior1/5

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

With no annotations, the description carries the full behavioral burden, and it discloses almost nothing: not whether preferences persist across sessions, whether writing an existing key overwrites it, whether scope is global or per-project, or whether confirmation is returned. 'Remembers' is the only behavioral clue and it is metaphorical rather than operational.

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

Conciseness2/5

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

The single sentence is short, but brevity here is under-specification rather than economy; it is front-loaded with an anthropomorphic framing instead of the operation. Nothing in the sentence earns its place as usable guidance.

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?

An output schema exists, so return values need not be explained, but for a two-parameter mutation with zero annotation coverage and zero schema descriptions, the description leaves essentially every operational question open. It is not complete enough for an agent to call the tool confidently.

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

Parameters1/5

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

Schema description coverage is 0%: preference_key has no type guidance and no examples, and preference_value has no type at all. The description does not compensate by explaining key naming conventions, accepted value formats, or whether values are free-form strings.

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

Purpose2/5

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

"Remembers how Michael wants things done" gestures at the concept of a preference but never states that the tool stores a key/value pair, nor does it distinguish itself from siblings like save_skill, add_note, or define_concept, which all persist information about the user. An agent must infer the operation from the name alone.

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?

There is no when-to-use guidance, no exclusion criteria, and no mention of the many sibling tools that also record durable information (save_skill, add_note, define_concept). The only hint is the implicit notion of 'preferences' versus other record types.

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