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automatikstudio

ProofBase MCP Server

by automatikstudio
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently has consistent naming, as there are no other tools to compare against. The name 'generate_testimonial' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for a server named 'ProofBase MCP Server', which suggests a broader scope related to social proof or testimonials. A single tool feels thin and limited for this apparent domain.

    Completeness2/5

    The server's name implies a domain of social proof or testimonials, but with only a generation tool, there are significant gaps. Missing operations like retrieving, updating, deleting, or managing testimonials make the surface severely incomplete for the inferred purpose.

  • Average 4/5 across 1 of 1 tools scored.

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

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  • This repository includes a README.md file.

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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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the testimonial is 'fictional but believable' and includes 'customer details', which clarifies it's not real data. However, it doesn't mention potential limitations like length, format, or whether it might generate repetitive content, leaving some gaps in transparency.

    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 two sentences, front-loaded with the core purpose and followed by additional context. Every sentence earns its place: the first defines the action and output, the second clarifies realism and use case. There's no redundancy or unnecessary information, making it highly efficient.

    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's moderate complexity (5 parameters, no output schema, no annotations), the description is fairly complete. It covers the purpose, output nature, and use case. However, it lacks details on the return format (e.g., text structure) and doesn't fully compensate for the absence of annotations, such as disclosing if there are rate limits or quality constraints.

    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 100% description coverage, so the baseline is 3. The description adds value by contextualizing the parameters: it implies that 'customer details' are generated based on inputs like 'customer_type', and 'outcome' relates to 'key benefit or outcome'. This enhances understanding beyond the schema, though it doesn't detail exact mappings or examples.

    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's purpose with specific verbs ('generate', 'creates') and resources ('realistic customer testimonial', 'fictional but believable testimonial with customer details'). It distinguishes the output as 'suitable for social proof', which adds context about its intended use case. Since there are no sibling tools, full differentiation isn't needed.

    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 context by mentioning 'suitable for social proof', suggesting it's for marketing or presentation purposes. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., real testimonials, other content generation tools) or any prerequisites. With no sibling tools, this is adequate but not comprehensive.

    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.
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  • Evaluate tool definition quality.

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