api_jsonValidate + pretty-print JSON. ?data={...} (urlencoded). [HTTP x402 price: $0.001]
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / params / additionalPropertiesRemoved value: -trueInput schema / properties / params / propertiesAdded value: +{
+ "data": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It discloses the invocation convention (?data={...} urlencoded) and the cost, and the operation is clearly a stateless read/transform. However, it does not mention what happens on invalid JSON or any input-size constraints, leaving a small transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The descrption is one front-loaded sentence with no filler; every chunk either says what the tool does, how input is passed, or what it costs. The '[HTTP x402 price...]' fragment is cryptic, but not redundant, so the description remains appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter stateless utility with an output schema available, the description gives the essential input convention and cost, so an agent can call it correctly. A brief note on invalid JSON handling would make it fully complete, but this is a minor gap given the simplicity of the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only an optional params.data string with no descriptive coverage, so the description must compensate. It does so by identifying the data parameter and specifying that the JSON is URL-encoded. This adds the key semantic beyond the raw schema, even though it does not discuss the null default or nested wrapper.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The descrption starts with specific verbs, 'Validate + pretty-print JSON', making the tool's purpose immediately clear and distinct from the long list of sibling utility tools. It names both the resource (JSON) and the actions, so an agent can identify what this tool does 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied through the action phrase: use this tool when you need JSON validation or pretty-printing. It does not explicitly name alternatives or when-not-to-use conditions, but for a one-purpose utility the context is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.