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@melis-ai/x402-tools-mcp

@melis-ai/x402-tools-mcp

by mizukaizen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

schemagate

Validate LLM outputs against a JSON Schema and get a correction hint if validation fails.

Instructions

[$0.001 USDC] Validate that an LLM output conforms to a JSON Schema. Returns valid:true/false with a correction hint on failure.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "response": "<response>",
      "schema": "<schema>"
    },
    "name": "schemagate"
  }
}

Input Schema

NameRequiredDescriptionDefault
schemaYes
responseYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses return format (valid:true/false with correction hint) and cost ($0.001 USDC), but doesn't specify if the tool is read-only or any side effects. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and to the point, but the cost prefix is somewhat extraneous. One sentence suffices, though it could be better organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple validation function and no output schema, the description covers the core purpose and basic return value. However, it lacks details on correction hint format or parameter nuances, making it minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It fails to explain what the 'schema' and 'response' parameters expect beyond schema types. No elaboration on format or constraints, leaving the agent underinformed.

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 validates LLM output against a JSON Schema, with a specific verb (validate) and resource. It distinguishes from siblings like docconvert_text or promptguard by focusing on JSON Schema conformance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The sibling tools list includes many unrelated services, but the description provides no context for selection or exclusion.

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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