Count characters, words, lines, sentences, bytes and estimated tokens and reading time of a text.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "count_text"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Count' plus 'estimated' strongly implies a non-mutating, approximate computation, which covers the safety profile implicitly, but there is no statement about encoding assumptions for byte counts, input size limits, or how tokens/reading time are estimated.
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?
One front-loaded sentence with the verb first and every item in the enumeration earning its place as a distinct returned metric. The trailing 'and reading time' is slightly awkwardly chained but nothing is redundant.
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?
There is no output schema, but the description effectively enumerates the return values (seven distinct counts plus reading time), which is exactly what the agent needs to know. For a one-parameter pure-computation tool with no annotations, this is close to complete; only edge-case behavior for empty or very large input is unaddressed.
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% for the single required 'text' parameter, so the description must compensate. It does so only minimally — 'of a text' identifies the input is the text to be measured — but adds no format, encoding, or size constraints.
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 a specific verb ('Count') with a concrete resource ('a text') and enumerates exactly what is measured: characters, words, lines, sentences, bytes, estimated tokens, and reading time. An agent can immediately tell this apart from calc, regex, or diff, though no sibling is named explicitly.
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 only implied by the metric list — an agent infers 'use this when you need text statistics' but the description states no when-to-use conditions, no exclusions, and no alternatives (e.g., calc for numeric work). For a simple, unambiguous utility this is adequate but not explicit.
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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