BakingTab — Baking Conversions for AI

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

In the absence of annotations, the description discloses useful behavioral traits: uses King Arthur + USDA density data, free tier limit of 20 calls/day per IP, and requirement of API key for more calls. It does not mention return format or error behavior, but the core behavior is transparent.

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 concise, with the main purpose and example in the first sentence and essential rate/access information in the second. The URL is slightly long but relevant. Overall, every sentence earns its place.

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?

Given no output schema and no annotations, the description provides enough context for a simple conversion tool: units, ingredient, example, and access limits. It does not explicitly describe the return value, but it is implied. Sibling tool names further clarify context.

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 coverage is 100%, so the baseline is 3. The description's example ('2 cups of all-purpose flour to grams') reinforces parameter usage, but it mostly duplicates unit list already in the schema. No significant additional meaning is added.

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 the tool converts baking ingredient amounts between specific units, using density data. It distinguishes itself from sibling tools like convert_temperature and scale_recipe by focusing on ingredient conversions, and provides an example.

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 provides a clear example and mentions rate limits and alternatives (API key, website). However, it does not explicitly state when not to use this tool versus siblings like convert_temperature, though the context implies it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the rate limit and the mass/volume classification, which is useful. However, it does not describe the return format (e.g., array, object structure) or any pagination/ordering behavior.

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?

Two sentences, front-loaded with the primary purpose and a concise rate-limit note. Every word earns its place; no fluff.

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 zero-parameter list tool with no output schema, the description provides the essential information: what is listed and the rate limit. It could optionally describe the exact output structure, but is otherwise complete for its simplicity.

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 tool has zero parameters, so the baseline is 4. The description clarifies what the output will contain (units and their types), adding meaning beyond the empty schema.

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 uses the specific verb 'List' and clearly identifies the resource ('supported baking units') and the key differentiator ('types (mass/volume)'). It distinguishes itself from sibling tools like convert_amount and search_ingredients, which perform different functions.

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 establishes clear context: this is the tool for listing all supported baking units. It does not explicitly mention alternatives or exclusions, but no alternatives are needed for a simple listing operation. The rate limit adds practical usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It adds a rate limit ('Free tier: 20 calls/day per IP') which is useful, but it does not describe whether the operation is read-only, idempotent, or what the return value looks like. The lack of these details for a seemingly pure function is a moderate gap.

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 concise sentences, front-loaded with the main purpose, and includes a critical rate-limit note without extra filler.

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 scaling tool, the description covers the core inputs and a key constraint (rate limit). It does not explain the return format, but since there is no output schema, a brief note on expected output would improve completeness. Still, it performs adequately for the tool's simplicity.

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 documents all three parameters. The description only repeats 'Pass servings and ingredients array' without adding new semantics beyond the schema, so it adds no extra 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?

The description clearly states 'Scale a recipe to a target number of servings' with a specific verb and resource. It distinguishes itself from sibling tools like convert_amount and list_units, which handle conversions and unit listing.

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 provides clear context for when to use this tool (scaling a recipe) and instructs on required inputs ('Pass servings and ingredients array'). It does not explicitly mention alternatives or exclusions, but the sibling tools are obviously different in purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits itself. It does disclose a meaningful rate limit ('20 calls/day per IP'), which is helpful. However, it does not state whether the operation is read-only, what happens on rate limit, or the response format, leaving some transparency gaps.

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, efficiently worded sentence that fronts the primary action and resource, followed by a useful rate-limit note. There is no redundant or extraneous content.

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?

Given the tool's simplicity (2 params, no output schema, no annotations), the description covers the key context: database size, data type (density), and rate limit. It lacks details about return structure, but for a search tool this is a minor gap, and the core purpose is fully communicated.

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% for both parameters (query and limit), so the schema already documents their purpose. The description adds useful context about the database contents (density in g/ml), but does not elaborate on parameter syntax or formatting, so the baseline score of 3 is appropriate.

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 action ('Search') applied to a specific resource ('baking ingredient database'), and adds context (100+ ingredients, density in g/ml). This distinguishes it from sibling tools like convert_amount and list_units, which serve different functions.

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 when to use the tool (when you need to search for ingredient info), but it does not explicitly state when to prefer this over alternatives or provide exclusion criteria. The sibling tool names hint at different use cases, but the description itself offers no direct guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.