Tanod MCP server

utilpeek: convert data between JSON, YAML, CSV, TOML and XML

convert_data

Read-onlyIdempotent

Convert JSON, YAML, CSV, TOML, or XML input to JSON, YAML, CSV, or TOML for config files, API payloads, and datasets, rejecting lossy or unsafe conversions.

Instructions

utilpeek: Convert data between JSON, YAML, CSV, TOML and XML. To check JSON against a schema use validate_json. Returns the document converted from json, yaml, csv, toml or xml to json, yaml, csv or toml. YAML uses libyaml's restricted loader (alias bombs refused); XML with DTDs or entities is a 422 xml_forbidden; lossy conversions are a 422 (not_tabular, not_representable, ...) rather than silently changed. CSV values stay strings; dates become ISO strings (native dates in TOML). Parsed in an isolated, resource-limited child process without network; input over its limits is a 422 (not charged). Typically under 1 s, up to a few seconds for 1 MB. 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/convert-data-formats-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	Yes	Output format: json, yaml, csv or toml.
`from`	Yes	Input format: json, yaml, csv, toml or xml.
`input`	Yes	The document as a string (at most 1 MB UTF-8).
`indent`	No	JSON output indentation (0 = compact, default 2).
`delimiter`	No	CSV delimiter (input and output).	,

Output Schema

TableJSON Schema

Name	Required	Description
`to`	No
`from`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`output`	No
`source`	No
`output_bytes`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds substantial context beyond them: YAML restricted loader refusing alias bombs, XML with DTDs/entities returning 422 xml_forbidden, lossy conversions surfaced as 422 instead of silent changes, CSV strings / ISO date coercion, isolated no-network child process, latency and 1 MB caps, pricing, free tier, and a no-logging guarantee. This is far richer than the annotations alone.

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?

Dense but front-loaded: the core purpose and the sibling routing come first, then errors, then operational details. Each sentence carries information, though the run-on stream of 422 codes is slightly crowded and could be tightened.

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?

With an output schema present, return values need not be explained, and the description fills the remaining gaps: error semantics, format limitations, size/time limits, cost, and privacy. An agent has everything needed to call it correctly and interpret failures.

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 description coverage is 100%, so the baseline is 3. The description still adds real semantic value: it clarifies that CSV values remain strings, dates become ISO strings while TOML keeps native dates, and that both `from` and `to` have asymmetric format sets (XML is input-only). That exceeds what the schema states.

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 specific verb (convert) and resource (data), enumerates the exact source and target formats (JSON, YAML, CSV, TOML, XML), and explicitly distinguishes itself from the sibling validate_json. An agent can identify this tool 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 Guidelines4/5

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

Names the alternative explicitly: 'To check JSON against a schema use validate_json.' It gives clear context for what the tool is for, but does not spell out when-not-to-use (e.g., large-document workarounds or batch needs beyond the individual-call model).

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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