count_wordsExact word, character, sentence, and paragraph counts plus reading time for a text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to count |
| Name | Required | Description | Default |
|---|---|---|---|
| words | No | ||
| result | No | The result, when it is not an object | |
| sentences | No | ||
| characters | No | ||
| readingTimeMinutes | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / text / descriptionAdded value: +"The text to count"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "characters": {
+ "type": "number"
+ },
+ "readingTimeMinutes": {
+ "type": "number"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "sentences": {
+ "type": "number"
+ },
+ "words": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish that the tool is read-only, idempotent, and non-destructive, so the description does not need to restate that. It adds 'exact' and a list of outputs, but it does not explain how words, sentences, or paragraphs are tokenized, or how reading time is calculated.
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 sentence that front-loads the concrete output metrics and contains no filler. It is appropriately sized for a low-complexity utility.
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?
This is a simple, one-parameter tool with full schema coverage, an output schema, and read-only/idempotent annotations. The description combined with the structured metadata provides everything needed for an agent to select and invoke the tool correctly.
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 input schema fully documents the single `text` parameter, including its maxLength and description. The description's mention of 'a text' adds no meaningful information beyond what the schema already provides, so the baseline of 3 applies.
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 clearly states what the tool does: it produces exact counts for words, characters, sentences, and paragraphs, plus reading time. This is specific enough to distinguish it from sibling tools like summarize_text or calculate_readability.
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?
The intended use is implied: an agent should choose this tool when it needs quantitative text statistics. However, the description does not explicitly contrast it with related siblings such as calculate_readability or text_analysis, nor does it give any when-not-to-use guidance.
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.
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.
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.
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.
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.