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Glama

JobDoneBot

by io.github.acromoney888

Word Counter

word-counter

Count characters, words, and estimate reading time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to analyze
languageNoText languageauto

Schema Changelog

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

  1. Changed2 schema fields changed
    • removedInput schema / properties / countNewlines
      Removed value: -{
-  "default": true,
-  "description": "Count newlines",
-  "type": "boolean"
-}
    • removedInput schema / properties / countSpaces
      Removed value: -{
-  "default": true,
-  "description": "Count spaces",
-  "type": "boolean"
-}
  2. Changed6 schema fields changed
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / properties / countNewlines
      Added value: +{
+  "default": true,
+  "description": "Count newlines",
+  "type": "boolean"
+}
    • addedInput schema / properties / countSpaces
      Added value: +{
+  "default": true,
+  "description": "Count spaces",
+  "type": "boolean"
+}
    • addedInput schema / properties / language
      Added value: +{
+  "default": "auto",
+  "description": "Text language",
+  "enum": [
+    "auto",
+    "ja",
+    "en"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / text
      Added value: +{
+  "description": "Text to analyze",
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[
+  "text"
+]
  3. First observed

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It only lists the outputs without explaining counting conventions, how the language parameter affects results, or the response format. This leaves significant ambiguity about the tool's actual behavior.

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 a single, clear sentence that front-loads the core actions. It contains no redundant wording and every word contributes to conveying the tool's purpose.

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 tool with no output schema, the description minimally covers the core functionality but omits details about the role of the language parameter and the exact output. It does not address potential edge cases or differentiate from similar tools, leaving a noticeable completeness gap.

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?

The schema description covers both parameters ('Text to analyze' and 'Text language') with 100% coverage. The tool description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline.

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 clearly states the tool counts characters, words, and estimates reading time, using specific verbs and resources. However, it does not differentiate the tool from sibling tools like text-statistics, which may offer overlapping functionality.

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 use this tool versus alternatives. The description only states what it does, without mentioning suitability, exclusions, or comparing to related tools like text-statistics.

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

Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.

Naming Consistency4/5

Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.

Tool Count1/5

202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.

Completeness4/5

The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.

Resources