# JSON Schema Validator MCP for AI Agents AI Agent Connect > JSON Schema Validator MCP. This tool checks your LLM-generated JSON against strict schemas to ensure every field is exactly what your application expects. It prevents malformed JSON hallucinations from hitting your production environment by using the Ajv library for industrial-grade validation. ## Overview - **Category:** development - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_wcVN6ru2KXI4lPbS9isndc7khYOy12YDatbPmrNo/ai-agent-connect - **Tags:** json-schema, validation, linting, developer-tools, automation ## Description Imagine you're asking your AI client to output a complex JSON payload for a user profile. Most of the time it works, but eventually, it'll miss a required zip code or add a random field that your database doesn't recognize. If that data goes straight into your system, your app crashes. This Connector acts as a gatekeeper for your data flow. It takes the raw JSON from your agent and runs it through a rigorous check against your actual schema. If the data is wrong, you get a specific list of errors—like a missing property or a string that's too long—so you can fix it before it breaks anything. It's about moving from hoping the AI got it right to knowing the data is correct. You'll find this is a staple for anyone building production-ready tools using the Vinkius catalog because it handles the heavy lifting of data integrity. You don't have to write manual checks for every single key. This tool ensures that your agent's output perfectly matches your API contract every single time. ## Tools ### lint_json_schema Performs structural linting on a JSON Schema ### validate_json_document Validates a JSON document against a JSON Schema ### evaluate_schema_compatibility Evaluates if a new schema is compatible with a base schema ## Prompt Examples **Prompt:** ``` Check if this user object is missing any required fields for our billing system. ``` **Response:** ``` ❌ **Validation Failed:** - `billingAddress.zipCode`: Missing required property. - `paymentMethod.token`: Missing required property. **Status:** The payload is missing 2 mandatory fields required by the billing schema. ``` **Prompt:** ``` Validate this JSON against a schema that requires a 10-digit phone number. ``` **Response:** ``` ❌ **Validation Failed:** - `phoneNumber`: String length must be between 10 and 10 characters. **Current Value:** "555-0199" (8 characters) **Status:** The phone number does not meet the required length constraints. ``` **Prompt:** ``` Does this list of product names exceed the 50 character limit for our UI? ``` **Response:** ``` ✅ **Validation Status:** Valid. All 4 items in the `product_names` array are within the 50-character limit. No violations detected. ``` ## Capabilities ### Check JSON data types The Connector ensures every value in your JSON object matches the required type like string, number, or boolean. ### Find missing required fields It identifies exactly which mandatory properties are missing from the AI-generated payload. ### Enforce string length limits The tool validates that your text fields stay within the character limits defined in your schema. ### Identify extra properties It flags any unexpected keys that the AI added to the JSON object but your system doesn't support. ### Report specific validation errors You get a clear list of every single error found so you can debug the AI's output quickly. ## Use Cases ### Preventing broken webhooks An engineer uses this to make sure an AI agent's webhook payload contains every required field before the system tries to process the payment. ### Cleaning batch data A data scientist validates thousands of rows of AI-generated product descriptions to ensure they don't exceed the 500-character limit. ### Safe API integration A developer uses this to verify that a user profile generated by an AI client matches their OpenAPI specification exactly. ### Dynamic form filling A product manager ensures that an AI agent filling out a lead form doesn't leave out mandatory contact information. ## Benefits - Stop production crashes by catching malformed JSON before it hits your database using validate_json_schema. - Get specific error messages like 'missing property' instead of generic 'invalid JSON' errors to speed up debugging. - Enforce strict data types and length constraints on all LLM-generated strings and numbers automatically. - Reduce the time spent debugging hallucinated keys in your application's backend by enforcing a strict API contract. - Maintain high data integrity for complex nested objects by using industrial-grade Ajv validation logic. ## How It Works The bottom line is you get guaranteed data integrity for every LLM output. 1. Provide the JSON data and the schema to your agent. 2. The Connector runs the Ajv validation engine against the input. 3. You get a clear pass or fail result with a list of specific errors. ## Frequently Asked Questions **How does JSON Schema Validator work with my AI agent?** Your agent sends a JSON string to the Connector, which checks it against your rules. It then reports back whether the data is correct or lists the specific errors it found. **Can JSON Schema Validator check for missing fields?** Yes. It identifies any required properties that the AI left out of the JSON payload so you can ensure your data is complete. **Will JSON Schema Validator help with OpenAPI schemas?** It's designed to work with them. You can use your OpenAPI definitions as the schema to ensure AI outputs match your API contracts. **How does JSON Schema Validator handle nested JSON?** It handles deeply nested objects perfectly, checking every level of your JSON to ensure types and constraints are met throughout. **What happens if the AI output is invalid?** The Connector returns a specific list of errors. You can then show these errors to your agent so it knows exactly how to fix the JSON. **Can I use JSON Schema Validator for data integrity?** That is its primary purpose. It provides a mathematical guarantee that the data your agent produces follows your required structure. **Does it support Draft-07?** Yes, it perfectly implements JSON Schema Draft-07. **Will it point out specific errors?** Yes, it returns the exact path and validation failure reason. **Can it validate OpenAPI specs?** Yes, it evaluates all nested types and definitions. **What does `validate_json_document` do?** It checks a JSON document against a provided schema and returns validation errors with exact JSON Pointers. **How can I find logical errors in my schema?** You can use the `lint_json_schema` tool to detect unreachable constraints or conflicting types within your schema definition. **Can I check if a new schema version breaks my existing integration?** Yes, the `evaluate_schema_compatibility` tool compares a base schema with a new one to identify any breaking changes.