Convert oven temperature between Celsius, Fahrenheit and Kelvin. Free tier: 20 calls/day per IP.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target unit: c, f, or k. | |
| from | Yes | Source unit: c, f, or k. | |
| value | Yes | Temperature value. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It adds a rate limit ('Free tier: 20 calls/day per IP'), which is important operational context beyond the schema. It does not mention side effects, but the nature of a conversion tool implies none, and the rate limit disclosure partially compensates.
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 two short sentences, front-loaded with the core functionality and followed by the rate-limit note. Each sentence provides distinct value and there is no waste.
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 simple conversion tool with fully documented parameters and no output schema, the description provides sufficient context including the rate limit. The purpose is clear, and no critical information is missing for this level of complexity.
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 100%, so the schema already fully describes the parameters and their allowed values. The description adds no additional parameter meaning beyond listing the units, so the baseline score 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 a specific verb ('Convert') and resource ('oven temperature') with explicit units (Celsius, Fahrenheit, Kelvin). This uniquely identifies the tool and distinguishes it from siblings like convert_amount, which handles monetary or numeric conversions.
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 description implies usage for temperature conversion but does not explicitly mention when to use this tool over alternatives or provide exclusion conditions. Context is clear but not fully explicit, so it stops at implied usage.
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.
Each tool targets a distinct operation: ingredient amount conversion, temperature conversion, unit listing, recipe scaling, and ingredient searching. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: convert_amount, convert_temperature, list_units, scale_recipe, search_ingredients. The naming is uniform and predictable.
With 5 tools, the server is well-scoped for a baking conversion domain. Each tool provides a necessary function without unnecessary bloat or redundancy.
The tool set covers the core domain needs: conversions (amount and temperature), unit discovery, ingredient lookup, and recipe scaling. There are no obvious gaps for typical baking conversion use cases.
