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ai-collab-mcp

ai-collab-mcp

by qqqq8413-cyber
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JavaScript
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List configured AI providers

list_providers

Check which AI providers (Claude, OpenAI, Gemini) have API keys configured and are ready for use in collaborative workflows.

Instructions

Lists which AI providers (claude, openai, gemini) currently have API keys configured and are usable in the other tools.

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_providers"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It clearly communicates a read-only listing behavior and the exact filtering condition (configured API keys). It does not mention output format or absence of side effects, but for a simple zero-parameter list tool the behavioral disclosure is sufficient.

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 one concise sentence with no filler. The action, resource, and qualifying condition are all front-loaded, making it easy for an agent to grasp quickly.

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?

For a zero-parameter tool with no output schema, the description provides all necessary information: what is listed, which providers are included, and how the result relates to other tools. Nothing essential is missing.

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 input schema is empty, so the description cannot add parameter-level meaning. The baseline of 4 applies because there are no parameters to misunderstand or document.

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 a specific verb ('Lists') with a clear resource (configured AI providers) and names the providers (claude, openai, gemini). It also states the selection criterion (API keys configured) and is clearly distinct from sibling list tools like list_agents and list_models.

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 phrase 'usable in the other tools' implies this is a precondition check before running pipeline or orchestration tools, but there is no explicit statement of when to use this tool versus list_agents/list_models or when not to use it. The usage context is only implied.

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

Deploy Server

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