KaleMap dietary menu finder

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

Annotations already declare it read-only and non-destructive, and the description adds non-obvious behavior: fully vegan places are ranked first, and dietary labels are read from menus with an explicit allergy-confirmation caveat. It does not describe pagination or limit behavior, but the added context is substantive.

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 short sentences with no waste: the purpose is front-loaded, followed by the critical needs semantics and the city-slug prerequisite. 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?

For a read-only search tool with safety annotations and no output schema, the description is adequate but not complete. It does not explain what the only enum values mean, how limit affects results, or what a returned place record contains, leaving gaps an agent must infer.

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 50%, and the description reinforces the important one-dish rule for needs while pointing city slugs to list_cities. However, it leaves the only and limit parameters unexplained in both the schema and the description, so it only partially compensates for the coverage gap.

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 states a clear resource (diet-friendly places in a city) and an ordering rule (fully vegan first), so an agent knows what it returns. It does not differentiate from siblings such as place_vegan_options or search_places, leaving ambiguity about which tool to choose.

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?

It gives useful prerequisite guidance for the city parameter (slugs come from list_cities) and explains how needs must be applied jointly. However, it never states when to use this tool instead of search_places or place_vegan_options, and gives no exclusions or alternatives.

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 only declare a safe, non-open-world read; the description adds real operational context: API-key or consented-OAuth auth, per-call quota cost, pagination semantics, confirmation that no scraping fires, unresolved redistribution rights, and an explicit prompt-injection warning about untrusted source text. It does not contradict the annotations and materially fills in consequences the agent needs.

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?

It is front-loaded with purpose and most sentences carry signal, but the rights/licensing boilerplate is dense and unstructured, and several clauses could be compressed into a clearer list. The length is defensible for a tool with legal and injection risk, yet the presentation is flat.

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 six undocumented parameters, the description does well on safety, cost, auth and return-value character, but leaves include_changes and query semantics undefined. An agent could call it, but not configure it confidently.

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 0%, so the description carries the full burden, and it only partially does: 'twelve dietary labels' maps to needs, and pagination hints map to cursor/limit. query and especially include_changes are never explained, and there is no guidance on limit bounds or slug format.

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 resource and its contents (saved menu detail, twelve dietary labels, provenance, freshness, explicit unknowns), so an agent knows what it retrieves even though the sentence is a noun phrase rather than a verb. It never distinguishes itself from the overlapping sibling place_vegan_options, which is the main clarity gap.

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?

Operational hints exist (one trial or paid request per call, follow pagination even when filtered pages are empty), but there is no statement of when to choose this over place_vegan_options or place_vegan_options-style siblings. The agent must infer the selection rule from the name 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 declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety and scope are covered. The description adds useful behavioral context by stating that the result is the complete city inventory and that each entry includes a slug and vegan-friendly place count. It does not mention pagination, but for a small closed list that is a minor omission.

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?

It is a single efficient sentence with the resource and returned fields front-loaded. Every phrase earns its place: coverage, slug, and count are all useful details.

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, parameterless, read-only list tool with no output schema, the description is complete enough: it states the scope, the returned identity field, and the associated place count. Combined with annotations covering safety, an agent has what it needs to call it 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?

There are no parameters, so the baseline is 4. The description appropriately does not invent parameter details and instead focuses on what the parameterless call returns.

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/resource pair: it lists every city KaleMap covers, and it states the returned fields (slug and vegan-friendly place count). This clearly distinguishes it from sibling tools such as city_vegan_places or search_places, which operate on places rather than the city inventory.

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 'Every city KaleMap covers' implies this is the discovery endpoint for finding supported cities, but the description does not explicitly say when to use it versus city_vegan_places or search_places. Usage is inferable but not stated with alternatives or exclusions.

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 a safe, non-destructive, closed-world read, so the description only needs to add context. It does add the result ordering and the fields returned (status, score, best dishes), but says nothing about empty results, pagination behavior, or how the radius interacts with the limit.

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 tight sentences, front-loaded with what is returned and followed by the one non-obvious semantic rule. Nothing repeats the schema and nothing is 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?

With six parameters, no output schema and 17% schema coverage, the description partially compensates by describing output fields and the AND semantics of `needs`. It is still not enough for a geo search tool since the `only` filter and the radius/limit relationship remain undefined.

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 only 17%, but the description does the single most valuable thing: it pins down that `needs` is conjunctive (one dish must satisfy every need, not any). However, the ambiguous `only` enum (all/options/vegan) and the interaction of km with limit are left completely unexplained.

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 first sentence states a specific resource (restaurants), a scope (near a coordinate), an ordering (nearest first) and the returned fields (status, score, best dishes). That clearly distinguishes it from city-scoped siblings such as city_vegan_places, though it never names an alternative explicitly.

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 by geometry (use this for proximity queries) but there is no explicit when-to-use/when-not, and no mention of when search_places or place_vegan_options would be the better call. The second sentence guides how to pass `needs`, which is parameter guidance rather than tool-selection 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 readOnlyHint and destructiveHint already covering the safety profile, the description still adds real behavioral context: results are truncated to four dishes and full evidence requires a developer key. It stops short of describing the response shape or what happens when a slug is unknown or the place has no saved dishes.

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 short sentences, front-loaded with what the agent gets, then the escalation path, then the input sourcing. No filler and nothing repeated from the schema or annotations.

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-parameter, read-only tool with no output schema, the definition covers the key facts: result limit, cost/key model, escalation tool, and where the slug comes from. Edge cases such as an invalid slug or an empty result set are unaddressed but secondary.

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 the schema already explains the slug format, so the baseline is 3. The description goes beyond it by telling the agent where to obtain the value (the last segment of a search result URL), which is actionable provenance the schema does not supply.

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 states a concrete scope: a free preview capped at four saved dishes from a single restaurant, which is specific enough to act on. It also implicitly separates itself from get_menu by scope (preview vs. full evidence). The one gap is that 'vegan options' from the tool name never appears in the text, so the agent must infer the dietary framing 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?

It names the alternative explicitly — 'use get_menu with a developer key' for detailed paginated evidence and all dietary labels — so the free/limited vs. keyed/complete tradeoff is clear. It does not spell out an explicit 'do not use when' case, but the free-preview-versus-developer-key condition effectively covers selection.

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 readOnly, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely non-obvious semantics: what each result contains and, crucially, that 'score' is 0–100 where 100 means fully vegan — an interpretation an agent could easily get wrong. It omits ranking order and whether matching is case-insensitive.

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 tight sentences: the action first, then the return shape. Every clause carries information (match fields and score semantics) with no restatement of the tool name or 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?

Return values are described despite there being no output schema, and the score scale is explained, which is the highest-risk ambiguity. The remaining gap is behavioral sizing — no mention of the default 10 / max 25 limit or result ordering — but for a simple name lookup this is close to complete.

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 50%: 'query' is described inline (restaurant name or part of it), but 'limit' has only a default and a 1–25 range with no explanation. The description never mentions result count or the 25 cap, so it does not compensate for the coverage gap; baseline 3 applies for a two-parameter tool.

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: 'Search restaurants by name', which is precise about the lookup mode (name, not location or city). It does not, however, differentiate itself from siblings like places_near or city_vegan_places, leaving the agent to 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?

Usage is only implied by the phrase 'by name' — it hints that this is the tool to use when you already know a restaurant name, versus browsing by city or proximity. There is no explicit when/when-not statement or named alternative, so the routing decision rests on inference.

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