Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@APIMatic Validator MCP Server validate this OpenAPI 3.0 YAML file for compliance issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This repository provides a Model Context Protocol (MCP) Server for validating OpenAPI specifications using APIMatic. The server processes OpenAPI files and returns validation summaries by leveraging APIMatic’s API.
Validates OpenAPI 2.0 and 3.0 files
Uses APIMatic’s API for comprehensive validation
Supports both JSON and YAML formats
Implements Model Context Protocol (MCP) for seamless integration
Related MCP server: MCP Gatekeeper
Ensure that Node.js v18+ is installed.
git clone https://github.com/apimatic/apimatic-validator-mcp.git
cd apimatic-validator-mcpnpm installnpm run buildTo use the server, an APIMatic API key is required. Sign up at APIMatic and obtain the API key.
Modify the claude_desktop_config.json file to integrate the MCP server. If the file does not exist, create one in the following location:
code $env:AppData\Claude\claude_desktop_config.jsoncode ~/Library/Application\ Support/Claude/claude_desktop_config.json{
"mcpServers": {
"APIMatic": {
"command": "node",
"args": [
"C:\\PATH\\TO\\PARENT\\FOLDER\\build\\index.js"
],
"env": {
"APIMATIC_API_KEY": "<Add your APIMatic token here>"
}
}
}
}Once configured, a hammer icon should appear in Claude Desktop. Open it to verify that the validate-openapi-using-apimatic tool is successfully integrated.
Add an OpenAPI file.
Provide a prompt to validate it.
The validation results will be returned.
Get validation summary for your OpenAPI spec using APIMatic
| Name | Required | Description | Default |
|---|---|---|---|
| openApiFile | Yes | The OpenAPI file content as a string | |
| isYaml | Yes | Whether the OpenAPI file is in YAML format |
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.
Is 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.
Given 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.
Does 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.
Does 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.
Does 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0validate-openapi-using-apimaticWith only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A 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).
A 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.
The 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.
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If you are the server author, to access and configure the admin panel.
MCP Spec Compliance MCP — audits any MCP server.json against the official Model Context Protocol
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
Deterministic validation for AI-generated artifacts: JSON Schema, OpenAPI response, SQL syntax.
Detect breaking changes, generate changelogs, diff, and validate OpenAPI specs.
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