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all-agents-mcp

all-agents-mcp

by Dokkabei97
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

list_models

List available AI models for agents, optionally filtering by specific agent (Claude, Codex, Gemini, Copilot).

Instructions

List available models for agents. Optionally filter by specific agent.

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
agentNoSpecific agent to list models for (all agents if omitted)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.2

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits, but it only restates the listing action without adding context about return format, side-effect freedom, permissions, or rate limits. The word 'list' implies read-only, but the description does not explicitly confirm safety or describe behavior 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 two short sentences, completely front-loaded with the main purpose and an optional qualifier. Every word earns its place; there is no redundancy or filler. It is an exemplary model of conciseness.

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?

The tool has a single, well-documented optional parameter, and the description covers the core operation. While there is no output schema, the phrase 'List available models' sufficiently communicates the return concept for a simple list tool, though it omits details like sorting or scope permissions.

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?

The schema already provides a full description for the agent parameter (including that omission means all agents). The description's phrasing 'Optionally filter by specific agent' mirrors the schema and adds little new semantic value. With 100% schema coverage, a baseline of 3 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 uses the specific verb 'List' and clearly identifies the resource ('available models for agents'). It distinguishes from sibling tool list_agents by focusing on models rather than agents, and the optional filter adds precision about the operation's scope.

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?

The description offers no guidance on when to choose this tool over alternatives such as list_agents or ask_agent. It simply states the action and optional filter, but does not explain common use cases, prerequisites, or exclusions. For a tool with many siblings, this is a clear gap.

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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