MCP For Claude Marketplace
Constraint Satisfaction Verifier

Constraint Satisfaction Verifier MCP Connector for Claude

A+

A deterministic validator for LLM outputs to enforce strict business and structural constraints.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Constraint Satisfaction Verifier acts as a guardrail for AI agents, ensuring that structured data produced by LLMs meets exact requirements. By using tools like verify_constraints, get_schema_template, and validate_path_syntax, agents can programmatically confirm that their outputs adhere to specific schemas and logical rules. This is essential for production-grade agent workflows where data integrity is critical.

validationllmguardrailsjsondata-integrity

3 tools expose this connector's capabilities to your AI agent.

validate_path_syntax

Verifies if a string is a validly formatted path for traversing complex nested objects and arrays

verify_constraints

Performs a full evaluation of a dataset against a provided set of validation rules

get_schema_template

Provides a structural template to help users construct valid fieldPath strings

See how to talk to your AI agent using Constraint Satisfaction Verifier.

Verify if the user's age in this JSON is greater than 18: {"user": {"age": 25}}

{"isSatisfied": true, "failedConstraints": []}

Check if the list of tags contains 'urgent': {"tags": ["low", "medium"]}

{"isSatisfied": false, "failedConstraints": [{"fieldPath": "tags", "expected": "urgent", "actual": ["low", "medium"]}]}

Is the path 'metadata.id' valid for this object: {"metadata": {"id": 123}}?

{"isValid": true}

You can use the `verify_constraints` tool to evaluate your data against a set of rules, or `get_schema_template` to discover valid paths for your data structure.

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