toolforte

JSON Formatter

format_json

Read-onlyIdempotent

Validate and pretty-print JSON with a real parser. Returns formatted JSON or the exact parse error.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`json`	Yes	The JSON text to validate and format
`indent`	No	Spaces per indentation level, 0 to 8 (default 2)

Output Schema

TableJSON Schema

Name	Required	Description
`valid`	No	Whether the input passed every check
`output`	No	The final output of the workflow
`result`	No	The result, when it is not an object

Schema Changelog

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

Changed3 schema fields changedSep 5, 2026

addedInput schema / properties / indent / description

Added value: +"Spaces per indentation level, 0 to 8 (default 2)"

addedInput schema / properties / json / description

Added value: +"The JSON text to validate and format"

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "properties": {
+    "output": {
+      "description": "The final output of the workflow",
+      "type": "string"
+    },
+    "result": {
+      "description": "The result, when it is not an object"
+    },
+    "valid": {
+      "description": "Whether the input passed every check",
+      "type": "boolean"
+    }
+  },
+  "type": "object"
+}

First observedSep 1, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already carry readOnlyHint=true, idempotentHint=true, and non-destructive status, so the bar is lower. The description adds genuine value beyond annotations by disclosing both output paths: success yields formatted JSON, and failure yields 'the exact parse error' — which tells the agent invalid input returns a diagnostic rather than crashing or silently passing through.

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 sentences and roughly 16 words with zero filler. The primary action is front-loaded, and the second sentence earns its place by clarifying both success and failure return behavior.

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?

For a simple 2-parameter utility, everything needed is present: the output schema relieves the description of explaining return structure, annotations cover safety and idempotency, the schema documents all parameters at 100%, and the description discloses the error path. No material gap remains.

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 the input schema already documents both the 'json' string and the 'indent' integer with range and default. The description's 'pretty-print' only loosely maps to the indent parameter and adds no parameter-level detail beyond the schema, so the baseline 3 applies.

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 states a specific verb-resource pair ('Validate and pretty-print JSON') and adds a differentiator ('with a real parser') that distinguishes it from regex-based hacks. No sibling tool formats or validates JSON, so there is zero ambiguity about what this tool is for.

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 rather than explicit: the agent can infer 'use this when a JSON string needs validation or formatting,' and 'with a real parser' hints it is the reliable option. However, there is no explicit when-to-use/when-not-to-use statement or named alternatives, though none of the 37 siblings compete directly with this tool.

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

A3.8/5.0

Disambiguation5/5

Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.

Tool Count2/5

Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.

Completeness3/5

The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.

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