Weight Forecast

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

With no annotations, the description carries the full burden and does well: it discloses the algorithm (linear fit over 30 calendar days), the minimum-3-weigh-in precondition, and that results are scoped to the authenticated user. It stops short of saying what happens when the precondition fails (error vs. partial result) or any accuracy caveats.

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 dense sentence, front-loaded with the primary output (statistics + projected goal date) and followed by the qualifying details. No filler words.

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 single-optional-parameter read tool with no output schema, the description adequately covers return content and preconditions. Minor gap: it does not describe behavior for users with fewer than 3 weigh-ins or how statistics are shaped.

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 horizonDays is already fully documented with default and max in the schema. The description adds nothing about it, which is the expected baseline when the schema does the heavy lifting.

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 and resource ('Return weigh-in statistics and a trend-based projected goal date') and even names the method (linear fit over the last 30 days). This clearly separates it from listEntries/getToday, which merely surface raw data rather than analytic projections.

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: it never says 'use this when the user asks for a goal-date projection' or excludes cases. It does supply a useful precondition (min 3 weigh-ins), which hints at when the tool is viable, but there is no explicit comparison to the sibling list/read tools.

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 carries the full behavioral burden. It implies authentication via 'authenticated user' and hints at aggregated read data, but says nothing about read-only safety, empty-state behavior when nothing is logged, timezone/date-rollover semantics, or error handling — all relevant for a 'today' query.

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 that names all three returned data points. Every word 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?

For a simple one-parameter read tool with no output schema, the description adequately summarizes the return contents (weight entry, food, calories). It falls short only on edge behavior (no data logged, date boundary) and any sibling routing, which keeps it from a 5.

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 the single 'date' parameter is fully documented in the schema (ISO format, defaults to today). The description adds only the implicit notion of 'today', which is already covered, so baseline 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?

States a specific verb ('Return') and enumerates the resources returned: today's weight entry, logged food, and calorie total, scoped to the authenticated user. It is clear what the tool does, but it never names or contrasts with siblings like listEntries or getForecast, so an agent must infer the distinction.

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 statement of when to use this versus listEntries, getForecast, or logFood, and no mention of prerequisites. The 'today' framing implies a daily-summary use case but the description never makes that explicit.

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 carries the full behavioral burden. It usefully discloses ordering (most-recent first) and that results are scoped to the authenticated user, but it omits pagination behavior, what happens past the limit, and any auth/permission specifics.

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 tight sentence that is well front-loaded, leading with the verb and resource. It is efficiently sized, though the ordering detail sits in parentheses and could be integrated more cleanly.

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 read-only list tool with one optional param and no output schema, the description covers purpose, ordering, and user scoping. It stops short of describing the returned entry fields or how the limit interacts with the result set, which the absent output schema leaves as a gap.

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?

There is one parameter (limit) with 0% schema description coverage, so the description must compensate. It doesn't mention limit, its default of 30, its range of 1-365, or what the value controls, leaving the parameter's semantics entirely to the raw schema constraints.

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 (list) and resource (weight entries) plus the ordering (most-recent first) and scope (authenticated user). It doesn't explicitly name or contrast against siblings like getToday or getForecast, but the resource and scope are clear enough to identify the tool.

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 phrase 'recent weight entries' implies a use case distinct from getToday or getForecast, but the description never states when to choose this tool over those siblings. Usage is only weakly implied rather than explicit.

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 carries the full behavioral burden. It notes the tool acts for the 'authenticated user' but never says whether repeated logging creates duplicates, whether entries are editable/overwritable, what validation or errors occur, or what the response looks like for this mutation tool.

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 tight sentence with the core fields front-loaded and zero wasted wording. Nothing needs trimming.

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 7-parameter mutation tool with no annotations, no output schema, and 14% schema description coverage, the description is too thin. It should disclose duplicate semantics, date defaulting behavior, and mealType usage to be callable with confidence.

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 very low (14%), so the description must compensate. It does group the optional fat/carbs/protein as 'macros' and names the two required fields, but it omits the date parameter (which defaults to today) and the mealType enum entirely.

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 ('Log') and resource ('a food item') and identifies the required fields (name + calories) and optional ones (macros). It is clearly distinct from siblings like addWeight, getForecast, and listEntries, though it does not explicitly contrast itself with 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?

The description gives no when-to-use guidance, no exclusions, and never references the sibling tools an agent might confuse this with (e.g., addWeight). Usage is only implied by the tool name and resource.

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