APIMatic Validator MCP Server
OfficialServer Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name follows a clear verb-noun pattern (validate-openapi-using-apimatic).
Tool Count2/5A single tool is too few for a server focused on OpenAPI validation, as it lacks coverage for related operations like detailed error reporting, schema linting, or batch validation. This minimal set limits agent workflows and feels incomplete for the domain.
Completeness2/5The tool surface is severely incomplete for OpenAPI validation. It only provides a summary validation, missing essential functions such as retrieving detailed validation errors, checking specific API endpoints, or supporting different OpenAPI versions, which are common needs in this domain.
Average 2.9/5 across 1 of 1 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 status not available
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it indicates this is a validation operation (likely read-only and non-destructive), it doesn't specify whether this requires authentication, has rate limits, what format the 'validation summary' returns, or any error conditions. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and efficiently communicates the essential information. Every word earns its place in this minimal description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's purpose (validation), lack of annotations, and absence of an output schema, the description is insufficiently complete. It doesn't explain what constitutes a 'validation summary', what validation checks APIMatic performs, what format the results come in, or any error handling. For a validation tool with no structured output documentation, this leaves too many unanswered questions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's already documented in the schema (which has 100% coverage). It doesn't explain the relationship between the two parameters, provide examples of valid OpenAPI content, or clarify what 'validation summary' means in the context of these inputs. With complete schema documentation, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get validation summary for your OpenAPI spec using APIMatic'. It specifies the action ('Get validation summary'), the resource ('OpenAPI spec'), and the method ('using APIMatic'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 other validation methods or tools. It lacks context about prerequisites, alternatives, or specific scenarios where this validation approach is preferred. Without any usage context or exclusions mentioned, it offers minimal practical guidance for an AI agent.
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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