Convert length, mass, temperature, volume, time, area, speed, and data-size units.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "unit_convert"
}
}| 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?
No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing: not the request/response shape, not whether unit names are case-sensitive or use symbols vs. words (e.g. 'kg' vs 'kilogram'), not whether temperature conversion requires a direction flag. The single sentence covers only the category taxonomy.
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 short, front-loaded sentence with no filler or redundancy. It is efficiently written, though its brevity comes at the cost of the specification detail scored elsewhere.
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 free-form POST-body endpoint with no annotations and no output schema, the description should explain at minimum how to express a conversion request and what comes back. It does neither, so an agent knows the domain but not how to actually 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 formal parameter count is 0, but the schema is an opaque POST body with additionalProperties: true and no declared fields, so the agent must infer the body keys (value, source unit, target unit) entirely on its own. The description's category list gives a hint about valid units but never names a single input field, leaving meaningful ambiguity despite the nominally '100%' coverage.
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 (Convert) and resource (units) and enumerates the supported measurement categories: length, mass, temperature, volume, time, area, speed, data-size. An agent can immediately tell this apart from the encoding/formatting siblings, though the description never explicitly contrasts itself with any of them.
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?
There is no when-to-use guidance, no indication of prerequisites, and no mention of an alternative tool or a case where this tool would be the wrong choice. The category list implies a conversion use case, but nothing tells the agent when to reach for it versus, say, a formatting tool.
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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