Skip to main content
Glama
akiojin

Model Hub MCP

by akiojin
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • 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.

  • Average 3/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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.

  • 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.

  • 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.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

model-hub-mcp MCP server

Copy to your README.md:

Score Badge

model-hub-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/akiojin/model-hub-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server