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Pavo 官网 MCP

Pavo 官网 MCP

by congliu1851-ui
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

查询实时模型

pavo_list_models
Read-onlyIdempotent

Retrieves available canvas image, video, or audio models and their constraints for a given scene code from a Pavo account, so you can select compatible generation options.

Instructions

读取账号实时canvas_image、canvas_video或canvas_audio模型列表与约束；文字模型由tool-specs返回。

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": {
      "scene_code": "<scene_code>"
    },
    "name": "pavo_list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
scene_codeYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so safety behavior needs no restating. The description adds that the result is account-scoped and includes 'constraints' (约束) alongside the model list, but does not say what those constraints cover or whether results are cached/live beyond the word '实时'.

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?

A single front-loaded sentence with the resource and scope first, followed by the sibling routing clause. No filler or redundant restatement of the tool name.

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?

With one required parameter, rich annotations and an output schema, the description covers purpose, account scoping and the text-model alternative sufficiently. The remaining gap is the acceptable values for scene_code, which is left implicit.

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 coverage is 0% and scene_code has no description or enum, so the description carries the burden. It names three plausible values (canvas_image/canvas_video/canvas_audio) but never states that scene_code takes exactly those strings, leaving the mapping implicit. Partial compensation for an otherwise undocumented parameter.

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?

States a specific verb (读取) plus resource (模型列表与约束) and narrows scope to the account's real-time canvas_image/canvas_video/canvas_audio models. It also explicitly carves out text models, so an agent can distinguish it from pavo_get_tool_specs without inspecting a schema.

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

Usage Guidelines4/5

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

Explicitly routes text-model lookups to tool-specs ('文字模型由tool-specs返回'), which is a genuine when-to-use-this-vs-alternative rule. It does not state any exclusion conditions or prerequisites (e.g. auth requirements) beyond that.

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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