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RN0000

Blendkit MCP Server

by RN0000
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches for assets, the other retrieves download URLs for a specific asset. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using snake_case: get_asset_download_url and search_blendkit_assets.

    Tool Count3/5

    With only two tools, the surface feels limited for a 3D asset library. While search and download form a minimal workflow, additional tools for categories or asset details would improve scope.

    Completeness4/5

    The tools cover the essential search-to-download flow. Minor gaps exist, such as missing asset detail retrieval or category listing, but the core functionality is present.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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

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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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses the need for an authorization header when downloading paid/private assets, which is valuable. However, it does not clarify side effects, whether the tool is read-only, or behavior for free assets. It partially addresses transparency.

    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 description is four sentences with a clear structure: purpose, args, returns, note. It is front-loaded with the main action. Some redundancy exists (e.g., 'download URLs' repeated), but it remains concise enough.

    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?

    For a simple one-parameter tool without an output schema, the description covers the parameter, return type, and a usage note. It lacks error handling or examples but is reasonably complete for the complexity level.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has zero description coverage, so the description fully compensates by explaining that 'asset_id' can be either 'id' or 'assetBaseId'. This adds essential meaning beyond the schema's type definition.

    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 'Get the download URLs for a specific asset by its ID,' specifying the verb 'get' and the resource 'download URLs' for an asset. It distinguishes from the sibling tool 'search_blendkit_assets' which is for searching, not retrieving URLs.

    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?

    No explicit guidance is provided on when to use this tool versus alternatives (e.g., after searching for an asset ID). The description implies usage after obtaining an asset ID but does not state prerequisites or exclude scenarios.

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

  • Behavior4/5

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

    Discloses return format (dictionary with asset objects) and important details like thumbnailUrls, files, downloadUrl. Mentions limit max 15. No annotations exist, so description carries full burden sufficiently.

    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?

    Well-structured with Args and Returns sections, front-loaded with main purpose. Could be slightly more concise, but every sentence adds value.

    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?

    No output schema, so return structure description is valuable. Covers purpose, parameters, and returns adequately. Missing error handling or rate limits, but sufficient for a search tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, but description compensates fully by explaining each parameter with examples (e.g., 'chair', 'wood'), enum values, and adding behavior like max limit of 15 not present in 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?

    Clearly states it searches for 3D assets in the Blendkit library, with explicit mention of search term and asset type. Distinguishes from sibling 'get_asset_download_url', which is for downloading, not searching.

    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 context for when to use (searching assets) and describes parameters. However, does not explicitly state when not to use or mention alternative tools beyond the sibling.

    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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