api_text_statsText statistics: words, chars, sentences, reading time. ?text=... [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: +{
+ "text": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the transparency burden. It does add some behavioral context by showing the query-parameter form ('?text=...') and the HTTP x402 price, but it does not disclose request method, edge-case behavior for null/empty text, or conventions used for sentence detection and reading-time calculation. For a read-only stats tool this is not critical, but the disclosure is incomplete.
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 description is a single compact, well-structured line: output metrics are listed first, followed by a usage pattern and the price. There is no filler or redundant prose, and every phrase adds useful information for an agent.
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 simple tool with one optional parameter and an available output schema, the description covers the basics of selection and invocation. It lacks caveats that could matter in practice, such as reading-time speed assumptions, sentence-boundary handling, or empty-input behavior, but the existence of an output schema reduces the need to document return values.
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?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. The '?text=...' hint and the phrase 'Text statistics' make the meaning of the single 'text' parameter reasonably clear. However, the description does not clarify that the parameter is optional, how the value should be encoded, maximum length, or what happens when it is null, so it only minimally compensates for the schema gap.
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 description names the resource ('text') and itemizes the specific outputs (words, chars, sentences, reading time), so an agent can tell this is a text-statistics utility. It does not explicitly contrast with sibling tools, but the metric list is enough to distinguish it from the many other api_* tools.
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?
No when-to-use guidance or alternatives are provided. The description gives only a raw '?text=...' query hint, with no indication of when to pick this tool over similar text-oriented siblings like api_readability or api_render_text. An agent selecting from 40+ sibling tools is left to infer the appropriate context.
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.