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perf-mcp

perf-mcp

Official
by Perf-Technology
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

perf_validate

Validates LLM-generated JSON against a schema, automatically repairing common violations like malformed enums, truncated arrays, and hallucinated fields. Returns compliant output or a detailed rejection.

Instructions

Validate LLM-generated JSON against a schema and auto-repair violations. Fixes malformed enums, truncated arrays, mixed types, hallucinated fields, and missing required properties. Returns valid, schema-compliant output or a detailed rejection.

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": {
      "content": "<content>",
      "target_schema": "<target_schema>"
    },
    "name": "perf_validate"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentYesThe LLM-generated structured output (JSON string).
target_schemaYesJSON Schema the output must conform to.
repair_modeNo'strict' rejects low-confidence repairs. 'best_effort' infers to fill gaps. Default: 'best_effort'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description discloses behaviors: it fixes specific violations (malformed enums, truncated arrays, etc.) and returns valid output or rejection. This is sufficient for a validation tool, though it could mention side effects or permissions.

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

Conciseness5/5

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

Two concise sentences front-load the purpose and list key behaviors. Every sentence adds value without extraneous information.

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

Completeness5/5

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

Given the three parameters and lack of annotations, the description fully covers the tool's function, repair capabilities, and output, making it complete for an agent to invoke correctly.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description does not add information beyond the schema's parameter descriptions, which already define content, target_schema, and repair_mode adequately.

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 and auto-repairs LLM-generated JSON against a schema, using specific verbs and resources. It differentiates from siblings like perf_chat and perf_correct by focusing on validation and repair.

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

Usage Guidelines3/5

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

The description implies usage for validating and repairing JSON, but does not provide explicit guidance on when to use it versus alternatives like perf_correct or perf_verify, nor does it state when not to use it.

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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