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Tools for Agents

by io.github.max124vip-dev

json_flatten

Flatten nested JSON

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesJSON object or array to flatten
prefixNo

Schema Changelog

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

  1. Changed4 schema fields changed
    • removedInput schema / properties / args
      Removed value: -{
-  "description": "Tool arguments",
-  "properties": {
-    "text": {
-      "description": "Primary input text",
-      "type": "string"
-    }
-  },
-  "type": "object"
-}
    • addedInput schema / properties / data
      Added value: +{
+  "description": "JSON object or array to flatten"
+}
    • addedInput schema / properties / prefix
      Added value: +{
+  "default": "",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[]New value: +[
+  "data"
+]
  2. Added

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does not explain how arrays are handled, whether keys are merged, or any side effects. The phrase gives only a superficial hint of functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and to the point, with no unnecessary words. However, it is too terse to be genuinely helpful; it reads more like a placeholder than a properly informative description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema and annotations, the description should explain what the flattened output looks like, but it does not. The tool's simplicity does not excuse the omission of return format and edge case behavior.

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

Parameters2/5

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

Schema coverage is 50%; only 'data' is described. The 'prefix' parameter lacks a description in the schema, and the tool description does not compensate by explaining its purpose or effect. This leaves a significant gap for agents trying to use the parameter correctly.

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 'Flatten nested JSON' uses a clear action verb and resource, distinguishing it from siblings like json_parse and extract_json. However, it does not fully specify the output format or flattening convention (e.g., dot notation), which leaves slight 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?

The description provides no guidance on when to use this tool versus alternatives such as json_parse or json_ld. It merely states what it does without any context, prerequisites, or exclusions.

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

C2/5.0
Disambiguation1/5

Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.

Naming Consistency2/5

Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.

Tool Count1/5

With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.

Completeness3/5

The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.

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