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moa-mcp

moa-mcp

by cvanes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_models

List supported model providers and their models, including model IDs and input modalities, to determine which models can execute prompts, such as text-only limitations.

Instructions

List supported model providers and the models available under each.

Returns a mapping of provider name to a list of models, each with a
human-friendly name, the provider-specific model id to pass to
execute_prompt, and the input modalities the model accepts (text-only
models reject image attachments).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It transparently describes the read-only nature ('List') and details the returned data, including a caveat that text-only models reject image attachments. It omits error or pagination behavior, but for a simple zero-parameter list operation, this is quite transparent.

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 two sentences, front-loaded with the primary action, and every word contributes. It avoids redundancy with the empty schema and efficiently conveys the return structure and a behavioral caveat.

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

Completeness5/5

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

As a zero-parameter, read-only list tool with no output schema, the description fully covers what the tool does, what it returns, and how the output connects to the one sibling tool (execute_prompt). There is no missing critical information for an agent to decide when to use it.

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

Parameters4/5

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

The tool has zero parameters, so there is no parameter schema to describe. The description adds value by explaining what the returned fields mean (model id for execute_prompt, input modalities), which is the relevant semantic context. Baseline 4 for zero parameters is appropriate.

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?

The description opens with a specific verb 'List' and a clear resource: 'supported model providers and the models available under each.' It also explains the return structure, making the tool's purpose distinct from the sibling execute_prompt, which is implicitly for running prompts.

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?

The description indicates the primary use by stating that the provider-specific model id is 'to pass to execute_prompt', which tells the agent how the output should be used. It does not explicitly say 'use this before execute_prompt' or exclude other tools, but given only one sibling, this clear linkage earns a 4.

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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