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AGTask MCP Server

AGTask MCP Server

by Charmly2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

agtask_set_default_model

Sets the default AI model for a specific task category, such as general conversation, so agents automatically use it for matching tasks.

Instructions

设置某类用途的默认模型

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": {
      "category": "<category>",
      "model_name": "<model_name>"
    },
    "name": "agtask_set_default_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
categoryYes用途分类，如「通用对话」
model_nameYes模型名

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing beyond the bare action. It does not say whether the setting persists globally, whether it requires admin rights, whether it affects in-flight tasks, or whether it is idempotent.

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?

A single short sentence with no waste and the action front-loaded. It is efficient, though arguably too terse for a mutation tool, which slightly limits its helpfulness.

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?

For a mutation tool with no annotations and no output schema, the definition is too thin: it omits the effect of the change, persistence semantics, and error conditions, leaving the agent under-informed before invoking it.

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?

Schema description coverage is 100%, so both parameters (category, model_name) are already documented in the schema. The description adds no format, syntax, or example detail beyond that baseline, so a 3 is appropriate.

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 states a clear verb+resource pair ('设置...默认模型' – set the default model) scoped to a category of use. It is specific enough to grasp the operation, but offers no differentiation from siblings such as get_model_config or call_model.

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 statement of when to use this instead of alternatives, no prerequisites, and no mention of related tools like get_model_config. The agent must infer the usage context entirely.

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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