Raw to Cooked Weight MCP

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile needs no restating. The description adds genuine context by disclosing the USDA cooking-yield basis and the default 3 oz (85 g) serving, but it says nothing about fallback behavior when the cut or method is unrecognized, or about what the result contains.

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 well-formed sentence that front-loads the core question being answered and tucks the default serving size at the end where it belongs. Nothing is wasted, though the one sentence carries the whole definition.

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?

With six parameters, no output schema, and a nontrivial yield-lookup concept, the description is adequate but thin: it does not say whether the response includes the yield used, the cut that was matched, or a confidence/fallback indicator. Schema coverage compensates for inputs, but the return side is entirely unexplained.

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 baseline is 3; each parameter including yield_id's 'overrides food and method' note is already documented in the schema. The description only reinforces the default serving size, adding no new meaning about how food/method and yield_id interact.

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 gives a specific verb-and-resource framing: it computes how much raw meat or poultry to buy to yield a given number of cooked servings, keyed to the USDA cooking yield for the cut and method. That is concrete enough to distinguish it from a plain unit conversion, but it never names or contrasts the siblings (convert_cooked_to_raw, find_cooking_yield) that an agent will be choosing between.

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 implied rather than stated: the wording makes clear this is the tool for 'how much do I buy' planning, but there is no explicit when-to-use, when-not-to-use, or pointer to convert_cooked_to_raw / find_cooking_yield as alternatives. An agent must infer the routing from the purpose sentence alone.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the marginal bar is what behavior is added. The description contributes the data source (USDA measured cooking yield) and discloses that uncertainty is exposed via USDA high/low end values, which is genuinely useful. It does not say what happens when the food or method is unrecognized, nor which method is assumed if omitted.

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?

Three sentences ordered purpose, then usage, then return values, all of which earn their place. The first sentence is a noun-phrase fragment rather than a verb-led statement, a minor stylistic inefficiency but not a comprehension problem.

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?

With five parameters, a single required input, and no output schema, the description correctly takes on the return-value burden by naming the four units and the high/low yield bounds. The remaining gap is failure and fallback behavior for unknown or mismatched food/method combinations, which matters for a lookup-driven conversion.

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% and every parameter including the yield_id override is fully documented in the schema, so the baseline of 3 applies. The description adds no parameter-level detail such as default unit handling or how food/method interact when both are supplied.

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 opening clause names the concrete transformation (raw weight a cooked portion came from) and the domain (meat or poultry), so the direction is unambiguous against the sibling convert_raw_to_cooked. It never names that sibling explicitly, so an agent must infer the inverse relationship from the verb alone rather than from stated contrast.

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?

Two concrete usage contexts are given: logging a cooked portion against raw nutrition data, and scaling a recipe written in cooked weight. That is real when-to-use guidance rather than implied usage. What is missing is any exclusion or pointer to alternatives, e.g. when to use convert_raw_to_cooked instead or when to bypass food/method in favor of yield_id.

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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds real context beyond that: it discloses the returned payload (g, oz, lb, kg, USDA sample range, weight lost, and the USDA row used), which is valuable given there is no output schema. It does not cover failure behavior for unmatched cuts or the precision/rounding of results.

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 core transformation and data source, followed by a return-value sentence that earns its place because no output schema exists. Dense but not padded; the lean-ratio note is the only slightly tacked-on clause.

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 5-parameter, read-only, no-output-schema tool, the description covers the transformation, source data, discretization of food/method, and the full return shape. The remaining gap is edge-case behavior (unknown cut or method, out-of-range handling) and how to chain to find_cooking_yield when yields are missing.

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%, including examples for food and method, so the baseline is 3. The description adds the notable semantic that food accepts ground-beef lean ratios like 80/20 or 93% lean, but otherwise restates what the schema already documents (yield_id overriding food and method is already in the 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 names a specific transformation (raw weight -> cooked weight) with a concrete data source (USDA Table of Cooking Yields) and domain scope (meat and poultry, including ground-beef lean ratios). It is clearly distinguishable from find_cooking_yield/list_cooking_yields because it is the conversion step, but it never names the reverse sibling convert_cooked_to_raw, so an agent must infer the direction split from the name alone.

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 implied rather than stated: an agent can infer 'call this when you have a raw weight and want cooked weight', and the yield_id note points at find_cooking_yield/list_cooking_yields as upstream sources. There is no explicit when-not guidance (e.g. use convert_cooked_to_raw for the inverse) and no statement of what happens for an unrecognized food or method.

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?

Annotations already establish the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real substance: the response payload contents (yield range, sample count, moisture and fat change) and the presence of alternative rows carrying yield_id. What it omits is matching behavior — whether the food/method lookup is fuzzy, and what happens on zero matches.

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 dense sentence, front-loaded on the primary lookup and then the returned fields, with no filler clauses. It is packed rather than padded, though splitting the return-content portion into its own sentence would improve scanability.

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?

With no output schema, the description carries the burden of describing results and does so adequately by listing every returned field and the alternative rows. The remaining gap is the resolution story — how closely the supplied food/method must match and what a miss looks like.

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 food, method (with example values) and limit; the description adds nothing about syntax or accepted forms. The only extra signal is the reference to alternative rows and yield_id, which weakly justifies the limit parameter but doesn't explain its trade-offs.

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 gives a specific verb ('look up') and resource ('USDA cooking yield percent') scoped to a cut plus a cooking method, and enumerates what comes back (range, sample count, moisture/fat change, alternatives). It is clearly a targeted lookup rather than the bulk enumeration implied by list_cooking_yields, though it never names the siblings to sharpen that boundary.

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: an agent can infer this is the tool to use when it has a specific food + method and wants the USDA yield figure, and the mention of 'up to 4 close alternative rows' hints at disambiguation. There is no explicit when-to-use statement, no exclusion of the conversion/calculation siblings, and no guidance on what to do when the food or method doesn't match.

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?

Annotations already declare read-only, idempotent, non-destructive behavior, so the description does not need to restate safety. It adds useful source context (USDA table) and filtering scope but does not describe return row structure, pagination, or how missing filters behave.

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, well-structured sentence that front-loads the action and resource. Every clause contributes to identifying the filtered USDA cooking-yield table.

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 two-optional-parameter reference-listing tool with rich annotations, the description gives enough context to understand what is being listed and how it can be filtered. It could be slightly more complete by noting that both filters are optional and that rows contain yield values, but no output schema exists and no critical usage detail is missing.

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%, and both parameters are fully documented in the schema, including accepted cooking-method terms and category values. The description restates the filter categories and method but adds no syntax or matching semantics beyond the 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 a specific verb (List) and resource (rows of the USDA Table of Cooking Yields) and names the filtering dimensions. It distinguishes itself from sibling calculation/conversion tools by framing this as a table-listing operation, though it does not explicitly mention alternatives like find_cooking_yield.

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 implied: browse or list cooking-yield rows filtered by category and cooking method. However, there is no explicit guidance on when to use this versus find_cooking_yield or the conversion/calculation siblings.

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