Tanod MCP server

utilpeek: validate JSON against a JSON Schema

validate_json

Read-onlyIdempotent

Validate JSON against a JSON Schema, returning up to 100 errors with instance and schema paths; invalid schemas and remote $refs are rejected.

Instructions

utilpeek: Validate JSON against a JSON Schema. To pull JSON out of prose use extract_json_with_schema; to convert formats use convert_data. Returns whether a JSON value is valid against a JSON Schema (draft from $schema, default 2020-12; 2019-09, 7, 6, 4 and 3 recognised), with up to 100 errors (instance path, schema path, message, validator) and the formats checked. Remote $ref documents are refused (422 remote_ref_forbidden), never fetched. Input: instance and schema; together at most 1 MB. An invalid schema is a 422 invalid_schema. Parsed in an isolated, resource-limited child process without network; input over its limits is a 422 (not charged). Typically under 0.5 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/json-schema-validation-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`schema`	Yes	JSON Schema (draft from $schema; default 2020-12). Remote $ref documents are refused, never fetched.
`instance`	Yes	Any JSON value to validate.

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No
`draft`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`valid`	No
`errors`	No
`source`	No
`truncated`	No
`error_count`	No
`formats_checked`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent/openWorld annotations: draft resolution from $schema with default and supported drafts, a 100-error cap with error field names, refusal of remote $refs (422 remote_ref_forbidden), the 1 MB combined input limit, 422 invalid_schema, isolated resource-limited no-network execution, uncharged over-limit responses, latency, price, free tier, and no logging/storage.

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?

Front-loaded with purpose and sibling routing, then constraints and operational facts. Dense but essentially every sentence carries distinct information; it is a touch packed with pricing/limits detail that could be trimmed.

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?

An output schema exists, so return values need not be explained, yet the description still summarizes the error payload shape. Combined with the schema, annotations, and stated failure modes, an agent has everything needed to call this correctly.

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

Parameters4/5

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

Schema coverage is 100% so both parameters are documented in the schema, establishing the baseline of 3. The description adds meaningful semantics on top — the combined 1 MB limit, the draft-resolution behavior of `schema`, and the fact that `instance` accepts any JSON value.

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?

States a precise verb+resource ('Validate JSON against a JSON Schema') and immediately distinguishes itself from two named siblings via different purposes. An agent can identify the tool's job without opening the schema.

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

Usage Guidelines5/5

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

Explicitly routes the agent: 'To pull JSON out of prose use extract_json_with_schema; to convert formats use convert_data.' Clear alternatives with the conditions that select them, plus the key constraint that remote $ref documents are refused rather than fetched.

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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