character_countCount Unicode characters, words, lines and UTF-8 bytes from text.
This call uses example arguments supplied in the tool’s input schema.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "Hello world.\nHi!"
},
"name": "character_count"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds one genuinely useful behavioral detail — that characters means Unicode characters and bytes means UTF-8 — which clarifies the counting semantics beyond annotations, but it omits ambiguity-relevant behavior such as whitespace/newline handling or grapheme-cluster treatment.
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?
A single front-loaded sentence with zero filler; the enumerated outputs are the highest-value information and appear immediately.
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 one-parameter, read-only counting utility with a full output schema, this is nearly complete — the output schema carries the return-value burden and the annotations carry the safety burden. The only residual gap is lack of differentiation from the sibling word_count and unspecified edge-case behavior.
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% and the single 'text' parameter has an empty description, so the description carries the burden of clarifying input. 'from text' confirms the input is raw text, but adds no format, length-limit, or encoding guidance beyond the schema's own example value.
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?
States a specific verb ('Count') and enumerates the exact units measured (Unicode characters, words, lines, UTF-8 bytes), so the agent knows precisely what this tool computes. It does not, however, explicitly distinguish itself from the sibling word_count tool, which is the closest overlap in the list.
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, no prerequisites, and no routing to alternatives. The presence of a sibling 'word_count' makes this gap meaningful — the agent gets no signal about when to pick character_count over word_count (or vice versa).
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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