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NanoTools

by io.github.nanotools-x402

Api Toml2Json

api_toml2json

Convert TOML to JSON. ?data= [HTTP x402 price: $0.001]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • removedInput schema / properties / params / additionalProperties
      Removed value: -true
    • addedInput schema / properties / params / properties
      Added value: +{
+  "data": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  }
+}
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It reveals that the input must be URL-encoded TOML and that the call costs $0.001, which is useful. It does not discuss failure behavior, payload limits, or whether the operation is purely read-only, but the stateless conversion nature is reasonably transparent.

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?

The description is compact and front-loaded, with the core conversion purpose stated first, followed by the input format and pricing. Every element earns its place and there is no redundant filler.

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?

For a simple one-parameter converter with an existing output schema, the description covers the essential invocation detail (URL-encoded TOML in 'data'). It lacks guidance on edge cases, errors, or request method, and does not reference sibling tools, leaving some context for an agent to infer.

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?

The schema has 0% description coverage, so the description must compensate. It does so by explaining that the 'data' parameter holds TOML content and must be URL-encoded, adding meaning beyond the bare string/null schema definition. No other parameters exist, so this is adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the operation: 'Convert TOML to JSON' with a specific input format. It differentiates implicitly from sibling converters like api_yaml2json and api_csv2json by naming the exact source and target formats, though it does not explicitly name alternatives.

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?

Usage is implied from the conversion verb and the example query format '?data=<toml urlencoded>'. However, there is no explicit guidance on when to choose this tool over the many sibling conversion or data-format tools, and no exclusions or prerequisites are mentioned.

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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TDQS

B3.1/5.0
Disambiguation2/5

Several tools have unclear boundaries: api_search and api_serp_google both return Google results, api_scrape and api_render_text both extract page text, and api_hash_multi overlaps with api_sha256 for SHA-256/SHA-512. While many tools are distinct, these overlapping pairs create real misselection risk.

Naming Consistency5/5

Every tool follows the same api_<snake_case> pattern with no mixed conventions or casing styles. The prefix makes the server immediately recognizable and the action/resource is consistently readable across all 44 tools.

Tool Count2/5

44 tools is well over the 25+ threshold for a well-scoped set, making the server feel like a grab-bag of unrelated utilities. Even though each tool is small and individually useful, the overall surface is too large and would benefit from consolidation into focused sub-servers.

Completeness3/5

The set covers many common utility categories—encodings, conversions, text analysis, web scraping, SEO, and trends—but has notable one-way gaps: CSV/YAML/TOML all convert to JSON but not back, and markdown converts to HTML but not the reverse. The broad domain makes full completeness hard to define, so only major reverse-conversion gaps stand out.

Resources