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Omnidim

@omnidim-ai/mcp-server

by Omnidim

listLLMProviders

Read-only

Retrieve all available Large Language Model providers to compare options and select the right model for your use case.

Instructions

Retrieve all available Large Language Model providers. (Tags: Providers)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds no significant behavioral details beyond restating the scope. The phrase 'all available' is consistent with openWorldHint=false but doesn't provide new information. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core sentence is concise and front-loaded, but the 'Tags: Providers' suffix is unnecessary fluff. Overall, the description is efficient but not perfectly waste-free.

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 simple list tool with no parameters and no output schema, the description is sufficient. It clearly states what the tool returns (all available LLM providers) and the annotations cover safety and world-openness. No additional context is needed.

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 the baseline is 4. The description doesn't need to add parameter details since there are none, and the schema is trivially covered.

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 verb 'retrieve' and the resource 'all available Large Language Model providers,' making the tool's purpose unambiguous. It also differentiates from sibling tools like listSTTProviders and listTTSProviders by explicitly focusing on LLM providers.

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 implies usage when LLM providers are needed, and the name reinforces this. However, it doesn't explicitly compare against alternatives like listAllProviders or mention when not to use it. This is clear context without explicit exclusions.

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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