get_llms_txtDevuelve el contenido actual de /llms.txt de VamosAlicante como texto plano. Este recurso contiene instrucciones y recursos destinados a agentes de IA.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=false, and idempotentHint=true, so the safety profile is covered. The description adds the return format ('texto plano') as a behavioral detail, but nothing else — no mention of content size, freshness, or how links within the document should be handled.
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, each earning its place: the first states the action and output format; the second provides context about the resource's purpose for AI agents. No redundancy and no 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 zero-parameter, no-output-schema, read-only tool, the description is nearly complete. It states what is returned, in what format, and why the resource matters (instructions/resources for AI agents). A brief note that the content may be lengthy or contain links to other endpoints would push it to a 5, but 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 takes zero parameters and schema coverage is 100% (an empty properties object). Per rubric, 0 params earns a baseline of 4. Parameter semantics are a non-issue here since there is nothing to configure.
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 ('Devuelve') and a specific resource ('/llms.txt de VamosAlicante'), and clarifies the output format ('texto plano'). It is clearly distinguishable from all sibling tools, none of which relate to llms.txt — siblings cover beaches, vehicles, food, places, transit, parking, weather, and events.
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 second sentence notes the resource contains 'instrucciones y recursos destinados a agentes de IA', which implies the tool is useful when an agent needs site-specific guidance. However, there is no explicit statement of when to use it over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in purpose: find_transit_stop and resolve_entity both resolve stops, while get_current_state, next_departures, favorite_stop, parking_availability, and beach_conditions all provide state/arrival information in different forms. The detailed descriptions help, but an agent could easily select the wrong tool when asked for simple arrival or status information.
All names use snake_case and are readable, but the pattern is mixed: some are verb-led (find_place, get_current_state, resolve_entity), some are noun phrases (beach_conditions, fuel_prices, flight_status), and others are quirky (whats_on, catch_vehicle_decision). This is not chaotic, but it lacks a strong consistent convention.
With 16 tools, the server is just above the ideal 3-15 range, but the broad Alicante information domain justifies the count. Each tool covers a distinct vertical or query path, and the set feels reasonably scoped rather than bloated.
The tool surface covers the main Alicante information domains well: transit, beaches, parking, food, places, flights, fuel, weather, and events, plus discovery/resolution helpers. There are minor gaps such as no direct traffic or bike-sharing tool, but agents can work around them via find_place or search_alicante.