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VoxellInc

@voxell/forge-mcp

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by VoxellInc
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TypeScript
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Server Quality Checklist

75%
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  • Latest release: v0.1.5

  • Disambiguation5/5

    The two tools have entirely distinct purposes: embedding text and listing models. There is no overlap or ambiguity.

    Naming Consistency4/5

    While 'embed' is a single verb and 'list_models' follows verb_noun pattern, the naming is still clear and predictable. Minor inconsistency but not confusing.

    Tool Count3/5

    With only 2 tools, the server feels minimal. However, for a focused embedding API, this may be sufficient. It is borderline but not extreme.

    Completeness5/5

    The tool set covers the core operations for an embedding service: generating embeddings and listing available models. No obvious gaps for the stated purpose.

  • Average 4.3/5 across 2 of 2 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
    • Last stable release on
    • 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.

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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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses embedding behavior, model options, dimension truncation (Matryoshka, re-normalized), and input_type prefix. Could mention rate limits or error handling, but adequate for typical use.

    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?

    Description is a single paragraph, detailed but not overly long. Front-loaded with main action. Could be improved with bullet points for readability, but still concise.

    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?

    Given presence of output schema (context indicates true), description adequately covers purpose, parameters, and usage. Might miss batch limits, but overall complete for the tool's complexity.

    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?

    Schema coverage is 100%, but description adds extra meaning: model quality/cost ranking, Matryoshka truncation explanation, and retrieval prefix for input_type. Adds significant value beyond schema.

    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 'Generate vector embeddings for one or more texts with Forge', specifies use cases (semantic search, RAG, clustering, similarity), and distinguishes from sibling '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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides clear guidance on when to use (various NLP tasks) and specific instructions for input_type ('query' vs 'document') and model selection with quality/cost trade-offs. Missing explicit when-not-to-use.

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

  • Behavior3/5

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

    No annotations are provided, so the description must disclose behavior. It states the tool lists models and dimensions, which is read-only. However, it does not cover authentication, caching, or side effects. For a simple list tool, this is adequate but not richly transparent.

    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?

    Two sentences, no wasted words. The first sentence states the action, the second provides usage guidance. Ideal conciseness.

    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?

    Given the tool is simple (no parameters, has output schema), the description is sufficient: it explains what is listed and why to call it. The output schema handles return value details.

    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 input schema has no parameters, and schema coverage is 100% vacuously. Per guidelines, 0 parameters yields a baseline of 4; no additional parameter information is needed.

    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 explicitly states the tool lists available Forge embedding models and their dimensions, with a clear verb and resource. It also distinguishes from the sibling 'embed' by advising to call this before embedding.

    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 gives a clear usage context: 'Call this to pick a model before embedding.' This implicitly directs away from the sibling 'embed' tool. However, it does not explicitly state when not to use it or alternative scenarios.

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

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