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akiojin

Model Hub MCP

by akiojin
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model-hub-mcp

日本語版 README

An MCP (Model Context Protocol) server that fetches AI model information from OpenAI, Anthropic, and Google.

Features

  • Multi-provider Support: Supports three providers - OpenAI, Anthropic, and Google AI

  • List Models: Retrieve a list of available models from each provider

  • Get Model Details: Fetch detailed information about specific models

  • Unified Retrieval: Batch fetch model information from all configured providers

Related MCP server: Outsource MCP

Quick Start (npx)

# Run with environment variables
OPENAI_API_KEY=your_key npx @akiojin/model-hub-mcp

Note: The package will be downloaded from npm on first run.

Installation

Global Installation

npm install -g @akiojin/model-hub-mcp

Local Installation

npm install @akiojin/model-hub-mcp

Configuration

  1. Copy .env.example to .env:

cp .env.example .env

  1. Set API keys for each provider in the .env file:

OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GOOGLE_API_KEY=your_google_api_key_here

Note: You can leave API keys empty for providers you don't plan to use.

Build

Compile TypeScript code:

npm run build

Usage

This MCP server is not meant to be run directly. It should be configured in your MCP client configuration.

See the "MCP Client Configuration Examples" section below for setup instructions.

Available Tools

list_models

Retrieve a list of available models from a specific provider.

Parameters:

  • provider: "openai" | "anthropic" | "google"

get_model

Fetch detailed information about a specific model.

Parameters:

  • provider: "openai" | "anthropic" | "google"

  • model_id: Model ID (e.g., "gpt-4", "claude-3-opus", "gemini-pro")

list_all_models

Batch fetch model information from all configured providers.

MCP Client Configuration Examples

Claude Code

You can easily add this MCP server to Claude Code using the following command:

claude mcp add model-hub -s user \
  -e GOOGLE_API_KEY=$GEMINI_API_KEY \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  -- npx -y @akiojin/model-hub-mcp

This command assumes you have environment variables set in your shell:

  • $GEMINI_API_KEY - Your Google AI API key

  • $OPENAI_API_KEY - Your OpenAI API key

  • $ANTHROPIC_API_KEY - Your Anthropic API key

Using npx

{
  "mcpServers": {
    "model-hub": {
      "command": "npx",
      "args": ["@akiojin/model-hub-mcp"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key",
        "ANTHROPIC_API_KEY": "your_anthropic_api_key",
        "GOOGLE_API_KEY": "your_google_api_key"
      }
    }
  }
}

License

MIT

Available Tools

3 tools
get_modelC

Get details of a specific model from a provider

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesThe AI provider
model_idYesThe model ID to fetch details for

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'gets details' but doesn't describe what details are returned, error handling, authentication needs, rate limits, or other behavioral traits. This leaves significant gaps for an agent to understand how the tool behaves beyond basic functionality.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what details are returned, potential errors, or other contextual factors needed for effective use. For a tool with 2 parameters and no structured output information, more completeness is required to guide an agent adequately.

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?

Schema description coverage is 100%, with clear descriptions for both parameters, so the baseline is 3. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or providing examples, but it doesn't need to compensate for low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get details') and resource ('specific model from a provider'), making the purpose understandable. It doesn't explicitly distinguish from sibling tools like 'list_all_models' or 'list_models', which appear to be listing operations rather than fetching details for a specific model, so it misses full sibling differentiation.

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 provides no guidance on when to use this tool versus alternatives like the sibling tools. It implies usage for fetching details of a specific model but doesn't specify prerequisites, exclusions, or contextual factors that would help an agent choose appropriately among available tools.

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

list_all_modelsB

List all available models from all configured providers

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool lists models but doesn't describe return format (e.g., structure, pagination), performance characteristics, or potential side effects. This is inadequate for a tool that presumably returns data, as agents need to understand what to expect from the output.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every element contributing to understanding the tool's scope and action.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., list format, fields included), which is critical for an agent to use the tool effectively. The description alone leaves significant gaps in understanding the tool's behavior and output.

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 schema description coverage is 100% (though trivial since there are no parameters). The description adds no parameter information, which is appropriate here. A baseline of 4 is given for zero-parameter tools, as there's nothing to document beyond what's already clear from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('all available models from all configured providers'), providing specific scope information. However, it doesn't explicitly differentiate from sibling tools 'get_model' (likely retrieves a single model) and 'list_models' (potentially has different filtering or scope), which prevents a perfect score.

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 provides no guidance on when to use this tool versus the sibling tools 'get_model' or 'list_models'. There's no mention of alternatives, prerequisites, or context for choosing this specific listing function over others, leaving usage decisions ambiguous.

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

list_modelsC

List all available models from a specific provider

ParametersJSON Schema
NameRequiredDescriptionDefault
providerYesThe AI provider to list models from

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the action ('list') but doesn't disclose behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like. For a tool with zero annotation coverage, this is insufficient.

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, clear sentence with zero waste. It's front-loaded and efficiently conveys the core purpose without unnecessary details, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'list' entails (e.g., format, pagination, or data returned), and with sibling tools present, it fails to provide context for differentiation. This leaves significant gaps for an agent to use the tool effectively.

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?

Schema description coverage is 100%, with the single parameter 'provider' fully documented in the schema (including enum values). The description adds no additional meaning beyond implying the provider is 'specific', which is already covered. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'list' and the resource 'all available models from a specific provider', which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_all_models' or 'get_model', which likely have different scopes or functions.

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 provides no guidance on when to use this tool versus alternatives like 'list_all_models' or 'get_model'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name and schema alone.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updates
    • First observedget_model
    • First observedlist_all_models
    • First observedlist_models
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TDQS

B3/5.0
Disambiguation2/5

The tools 'list_all_models' and 'list_models' have overlapping purposes that could cause confusion, as both involve listing models with only a subtle distinction (all providers vs. specific provider). The 'get_model' tool is distinct, but the ambiguity between the two listing tools is significant enough to risk misselection by an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_model, list_all_models, list_models), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.

Tool Count3/5

With only 3 tools, the server feels thin for a 'Model Hub' domain, which might involve more operations like uploading, updating, or deleting models. While the count is not extreme, it borders on being under-scoped for the apparent purpose.

Completeness2/5

The tool surface is significantly incomplete for a model hub domain. There are no tools for creating, updating, or deleting models, and no operations for managing providers or configurations. This leaves obvious gaps that could cause agent failures in common workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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