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NanoTools

by io.github.nanotools-x402

Api Csv2Json

api_csv2json

Convert CSV to JSON array. ?data=&delim=, [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

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does add one meaningful behavioral fact: the call is an HTTP request with an explicit $0.001 price. However, it does not disclose error behavior, size limits, or side effects, leaving the paid HTTP trait as the only real transparency gain.

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 short, front-loaded with the core action, and includes a compact usage example plus a price note. Every part earns its place and there is no 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 converter with an output schema, the core purpose, input format, delimiter, and cost are present, so it is not incomplete in the basic sense. But the delim parameter is missing from the input schema, there is no alternative routing, and no edge-case behavior is described, so an agent cannot fully trust that it will invoke the tool exactly as intended.

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 0%, so the description must compensate, and it does by explaining that data is URL-encoded CSV and that delim sets the separator. The downside is that delim is not present in the input schema, creating a mismatch between the documented call format and the structured parameter contract.

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 opens with a specific verb and resource, 'Convert CSV to JSON array', which clearly identifies what the tool does and distinguishes it from sibling conversion utilities like api_toml2json or api_yaml2json. The example query string reinforces the input and output without ambiguity.

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?

There is no guidance on when to choose this tool over alternatives, nor any exclusions. The query-string example is an invocation detail, not a when-to-use explanation, and the sibling set contains many conversion tools that are not differentiated.

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