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coding-agents-mcp

coding-agents-mcp

by funkyfunc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

agy_list_models

List all AI models available to the Antigravity CLI runtime to help users identify and select the appropriate model for their tasks.

Instructions

List all AI models available to the Antigravity CLI runtime (e.g., Gemini 3.8 Flash, Gemini 3.1 Pro, Claude Sonnet 4.6, Claude Opus 4.6).

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": "agy_list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior2/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It only states 'List all AI models' with no mention of side effects, permissions, output format, or potential variability (e.g., whether results are sorted, include deprecated models). For a read operation the risk is low, but the description gives no additional behavioral context beyond the literal action.

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 a single sentence that front-loads the action and scope. It includes examples to make the purpose concrete without extra verbosity. Every word earns its place.

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 parameterless listing tool with no output schema, the description provides a clear purpose and examples of expected output. It could explicitly state the return format (e.g., an array of model names), but the examples strongly imply this. Given the tool's simplicity, this is adequate.

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, and the schema is an empty object (100% coverage). The description doesn't need to explain any parameters, so the baseline of 4 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 clearly states the action ('List'), the resource ('all AI models'), and the scope ('available to the Antigravity CLI runtime'). It also provides concrete examples of models, which removes ambiguity. This is distinct from all sibling tools, none of which focus on model listing.

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 description does not explicitly state when to use this tool versus alternatives. However, the siblings are all different operations (delegation, tasks, diffs, sessions, etc.), so there is no obvious overlap. Still, no when-to-use or when-not-to-use guidance is provided.

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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