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bharatr21

mcp-nvidia

by bharatr21
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.5.0

  • Disambiguation5/5

    Both tools have clearly distinct purposes: one focuses on discovering educational content (videos, courses, etc.) while the other is a broader search across all NVIDIA domains. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (discover_nvidia_content and search_nvidia), using underscores and placing the verb first. The naming style is uniform and predictable.

    Tool Count2/5

    With only 2 tools for a vast domain like NVIDIA resources, the surface is too limited. While search and discovery are covered, many common operations (e.g., retrieving specific content, filtering by type) are absent, making the count feel insufficient.

    Completeness2/5

    The tool set covers only search and discovery, missing essential operations like detailed retrieval, filtering, or CRUD actions. For a server that claims to cover 'multiple NVIDIA domains,' the lack of more specific tools leaves significant gaps.

  • Average 3.9/5 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 exist, so the description carries the full burden. It discloses the output format (ranked results with relevance scores and direct links), which is sufficient for a read-only discovery tool. It does not mention side effects or requirements.

    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?

    Three sentences, each serving a purpose: core function, use case, and return format. No extraneous information. Very 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 100% schema coverage, an output schema, and the description covering return format, the tool is well-documented. It does not elaborate on pagination or default behavior for 'max_results', but the schema covers that.

    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 coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema; it lists content types already defined in the enum. No extra semantic detail is provided for parameters like 'topic' or 'max_results'.

    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 starts with a specific verb 'Discover' and the resource 'NVIDIA content', clearly stating the tool's function. It enumerates content types (videos, courses, tutorials, webinars, blog posts) which distinguishes it from the sibling 'search_nvidia'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

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

    The description implies usage for finding educational content but provides no explicit guidance on when to use this tool versus the sibling 'search_nvidia'. No alternatives or exclusions are mentioned.

    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 carries the full burden. It mentions results include citations and categorization by domain type, but does not disclose rate limits, authentication needs, or behavior with no results. This is adequate but not thorough.

    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 a single paragraph of about four sentences, listing domain types and result features. It is concise and front-loaded, with no unnecessary repetition, though it could be slightly tighter.

    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 the tool has 10 parameters and an output schema, the description covers the key aspects: search scope, domain types, and result structure (citations, categorization). It is mostly complete for a search tool.

    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 coverage is 100%, so baseline is 3. The description adds context about the overall search scope (domains, content types), but does not significantly enhance understanding of individual parameters beyond what their descriptions provide.

    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 tool searches across multiple NVIDIA domains and lists examples, distinguishing it from the sibling 'discover_nvidia_content' which implies browsing rather than targeted search.

    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 explains the tool is for finding relevant information about NVIDIA technologies, but does not explicitly state when to use it versus the sibling tool or when not to use it. However, the sibling name implies discovery, so context is clear.

    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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  • Evaluate tool definition quality.

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