dumaguete.uk places

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

With no annotations provided, the description bears the full burden of behavioral disclosure. It conveys that the operation returns one record, implying a read, but it does not mention what happens when the id is invalid or not found, whether a full or partial record is returned, or any authentication/error behavior. The minimal 'Return one place record' leaves significant behavioral ambiguity for a tool with zero annotation coverage.

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, front-loaded sentence that carries all essential information with zero filler. It names the action, the resource, and the parameter's origin in the fewest possible words. Every word in the description 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 tool with one integer parameter, no output schema, and no annotations, the description covers the key operational facts: what it returns and where the id comes from. The only notable omissions are failure behavior and return field details, which are secondary for selecting and invoking a simple get-by-id tool. The combination of concise description and simple schema makes this adequately 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 description coverage is 0%, so the description must compensate for the bare 'id: integer' property. It adds meaningful semantics by clarifying that the id comes from search_places, telling the agent the source and nature of the value. However, it does not elaborate on the format, scope, or uniqueness of the id beyond that origin, so compensation is partial and moderate.

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 specific verb ('Return') and a specific resource ('one place record'), and ties the identifier to a sibling tool ('the id returned from search_places'). This clearly distinguishes it from search_places (plural results) and the other listing siblings. The agent immediately knows this is a single-record fetch by ID.

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 the correct usage pattern: call this after search_places to retrieve the full record for a specific id. It provides clear context for when to use this tool versus search_places, though it does not explicitly state exclusions or alternative conditions. This is clear context without formal when-not guidance, matching the 'clear context, no exclusions' level.

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 behavioral disclosure burden. It discloses that the vocabulary is normalized, includes counts, and belongs to the site, which is meaningful behavioral context beyond the tool name. It does not explicitly state side-effect-free behavior, but the list-like nature and 0-param signature make this a reasonable and useful disclosure.

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, tightly worded sentence with no filler. It front-loads the core result—normalised category vocabulary with counts—and then gives a crisp usage directive with an important qualifier.

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 tool with no output schema, this description is complete: it states what is returned, that counts are included, when to call it, and why the site-owned vocabulary matters. An agent has everything it needs to invoke this tool 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?

The tool has zero parameters, so the schema provides no parameter information that needs elaboration. The description appropriately focuses on output and usage rather than parameters, matching the baseline for a parameter-less 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?

The description identifies a specific resource—the normalised category vocabulary with counts—and clearly differentiates it from sibling tools like list_cities by focusing on categories, not cities or places. The title supplies the list verb, and the description adds the distinguishing normalization and count details.

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 explicit when-to-use guidance: 'Call this before filtering on a category.' It also warns that the vocabulary is the site's, not the user's, which is essential for correct usage. It does not name explicit alternatives or when-not-to-use conditions, so it stops short of a 5.

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 full responsibility for behavioral disclosure. It clearly states the tool always returns an empty list and the underlying reason (records carry no city field). While it does not mention side effects or permissions, none are expected for a read-only stub, so the transparency is adequate.

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, no filler, and the critical 'returns empty list' fact is front-loaded. The alternative guidance is brief and directly actionable.

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 has no parameters, no output schema, and a trivial behavior, the description covers everything an agent needs: it knows the result will be empty and that other tools should be used instead. Nothing essential 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?

The tool has zero parameters, so the schema is empty and the description need not add parameter details. The baseline of 4 applies because there is no need to explain parameters; the description provides sufficient context about the tool's behavior.

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's function: it returns an empty list and explains why (no city field). It also distinguishes itself from siblings by explicitly pointing to alternatives ('narrow by near coordinates or by category instead'), so an agent immediately understands it is not the tool for city data.

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?

Explicitly tells the agent when not to use this tool and what to use instead: 'Narrow by near coordinates or by category instead.' This is direct usage guidance that prevents the agent from calling this tool expecting results.

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 burden of behavioral disclosure. It transparently exposes the coverage limitation (OSM relation 147488 and the specific out-of-coverage cities) and the list_categories prerequisite. It does not, however, disclose how the filters combine (AND vs OR) or what fields the returned result contains, which would aid the agent further.

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 deliver the purpose, the key prerequisite, and a precise coverage boundary. The text is front-loaded: the action and resource appear first, and every clause contributes either a functional description or a constraint. No fluff or repetition.

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 5 parameters and no output schema, the description needs to clarify both the mapping of the 'city' mode and the shape of results (e.g., place names, IDs, ordering). It provides the crucial scope rule and the category prerequisite, but the indeterminate city filter and the absent result description leave enough ambiguity that it is not fully 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?

Schemas already describe 100% of the parameters, so the description needs only to add clarifications. Instead, it introduces a 'city' search mode that does not map to any property in the schema, requiring the agent to guess whether 'city' is represented by `q`, `category`, or an undeclared parameter. This ambiguity makes the description misleading rather than informative.

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 specific verb and resource — 'Find places' in Metro Manila — and enumerates the search dimensions (city, category, name fragment, near a coordinate). The explicit geographic scoping immediately distinguishes it from the sibling tools like get_place or list_categories.

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 an explicit prerequisite: 'Call list_categories before filtering on a category.' It also flags the geographic coverage boundary ('no Cebu, Davao, Baguio, or Dumaguete'), telling the agent when not to rely on this tool. It doesn't name alternative tools for those other regions, so it stops short of a perfect 5.

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 burden. It discloses the output elements (record count, discovered table, build time) but does not state whether the operation is read-only, has side effects, or requires any authorization. For a stats tool it likely is safe, but that is not made explicit.

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 short sentences: the first lists the returned data in a compact list, the second gives a clear usage directive. No wasted words; the key information is front-loaded.

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 tool with no output schema, the description covers the return values and a usage scenario. It could clarify what 'discovered table' means, but that is a minor omission; an agent can likely infer it from context. Overall, sufficient for correct invocation.

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 and the schema is empty, providing full coverage vacuously. The description correctly adds no parameter details, and per the baseline for 0-param tools, a 4 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 names the resource (site statistics) and enumerates the specific data points returned (record count, discovered table, build time), implying a retrieval action. It distinguishes itself from sibling tools that handle places by being about dataset-level stats, though the verb is not explicitly stated.

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 provides an explicit call condition ('Call this before reporting that something does not exist'), giving clear when-to-use guidance. It does not mention alternatives or when not to use it, but the directive is specific and actionable.

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