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Glama

rpa_app_params

Retrieves input parameter definitions for a specified RPA application, including names, data types, and required status, to help validate or construct app calls.

Instructions

查某个影刀应用的入参定义(参数名、类型、是否必填)。

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": {
      "app_id": "<app_id>"
    },
    "name": "rpa_app_params"
  }
}

Input Schema

NameRequiredDescriptionDefault
app_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.7.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It implies a read-only lookup ('查') and discloses what the response contains (parameter name, type, required), which is genuinely useful. It says nothing about behavior on an invalid or unknown app_id, whether the lookup requires auth or permissions, or whether it is side-effect free, and for an unannotated tool those are real gaps.

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 short sentence that front-loads the action and follows with the returned fields. It contains no filler and no redundancy.

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?

For a one-parameter read tool with no output schema, the description covers what the tool does and roughly what it returns (names, types, required flags). The remaining gaps (how to obtain app_id, error behavior) are minor, but they still leave the definition slightly incomplete.

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 0%, and the description only implicitly ties app_id to '某个影刀应用' (a certain Yingdao app). It does add context explaining that the identifier selects an app, but it does not state the format of app_id, nor that rpa_list_apps supplies a valid value, so the low coverage is only partly compensated.

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 gives a specific verb and resource in Chinese: '查某个影刀应用的入参定义' (query the input-parameter definition of a Yingdao app), and it even summarizes the returned fields (name, type, required). That clearly separates it from rpa_list_apps, rpa_start_job, and rpa_query_job. It does not, however, explicitly differentiate from a sibling it might be confused with, so it is a 4 and not a 5.

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 usage is only implied: you would call this before starting a job, in order to learn what parameters an app expects. The description never says when to use it, when not to, or that rpa_list_apps is the way to discover a valid app_id. The agent must infer the workflow.

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