Nueve de Diciembre — River Plate

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 is fully covered. The description adds useful context about the returned content (full text, tags, players, highlighted data) but does not describe edge cases like missing slugs or locale-dependent behavior beyond the schema.

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 two sentences with no filler: the first sentence defines the core operation and input, the second lists return contents and gives a usage fallback. Information is front-loaded and every phrase 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 read-only retrieval tool with strong annotations, this description is complete: it identifies the input, the output contents, and a clear alternative for when the input is unknown. No output schema exists, but the description sufficiently explains what will be returned.

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 only 50%, and the critical 'slug' parameter has no schema description; the tool description compensates by explaining what a slug is with a concrete URL example. The 'locale' parameter is already documented in the schema, so no further description is needed there.

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 returns the full text of a published article by slug, and it specifies what the response includes (tags, mentioned players, highlighted data). This distinguishes it from siblings like search_site and list_articles by naming the exact resource and retrieval key.

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 explicitly instructs agents to use search_site when the slug is unknown, directly addressing the main prerequisite and providing a clear fallback. This is an explicit when-not condition with an alternative tool.

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, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value by explaining the content type (statistic/record/anecdote) and that it includes a link to the source note, providing context beyond the annotations. It does not mention any rate limits or authentication, but these are not necessary given the tool's simplicity and annotations.

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, compact sentence that immediately conveys the tool's purpose and output. It is front-loaded with the core concept and contains no redundant information.

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 tool with one well-documented parameter and no output schema, the description adequately explains what the tool returns (a fact with a link) and the source context. It is complete for an agent to call it correctly without needing additional clarification.

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 schema provides 100% coverage for the single locale parameter, including its enum values, default, and description. The tool description does not add any parameter-specific detail beyond what the schema already states, so the baseline of 3 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 clearly states the tool provides the highlighted fact of the day—a statistic, record, or anecdote—drawn from a published note and includes a link to that source. The verb 'get' combined with the resource 'fact_of_the_day' is specific, and the description's content distinguishes it from sibling tools like get_article or get_matches.

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 implicitly signals when to use this tool (when a user asks for the daily fact), but it does not explicitly mention alternatives or exclusion criteria. The name and description make the use case obvious, so the guidance is clear but not 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context by stating that totals are computed from records (not hardcoded) and that each title includes a verification source. This goes beyond the annotations and helps the agent understand data provenance and reliability.

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 two concise sentences, front-loaded with the core purpose. It efficiently lists the returned fields and the key behavioral note about totals and sources without any filler. 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?

The tool has no required parameters and no output schema, so the description carries the burden of explaining the return content. It lists the fields included per title and clarifies how totals are derived. It does not mention pagination or limits, but for a read-only list of titles this is a minor gap. Overall, it provides sufficient context 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?

Schema description coverage is 50%: the 'competition' parameter has a description, and 'sport' has an enum and default. The description does not add further meaning about how filtering works or what sport values imply. Since coverage is moderate and the tool is simple, the description's lack of parameter elaboration is acceptable but not outstanding.

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 verb and resource: it returns the complete honours list of River Plate, including details like year, opponent, result, venue, coach, and source. This distinguishes it from siblings like get_matches or get_standings, which cover different data types. The purpose is unambiguous and specific.

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 implicitly indicates when to use this tool (to retrieve River Plate's titles), but it does not explicitly contrast it with alternatives or state when not to use it. Given the sibling set, an agent can infer that this is the tool for honours rather than matches or players, but the guidance is not 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?

Annotations already indicate read-only and idempotent behavior. The description adds valuable context beyond annotations: data comes from an external provider, may lag behind live results, and `syncedAt` signals freshness. This helps set expectations about data accuracy.

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 compact and front-loaded: it states the core purpose, then clarifies list semantics, and ends with a caveat about data freshness. Every clause adds useful information without unnecessary 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 simple read-only tool with two optional parameters and no output schema, the description covers the important behavioral aspects: return sections, live-match inclusion, freshness indicator, and external data caveat. The main gap is the lack of any description of `limit` semantics, but overall an agent can call 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 description adds no meaning for the two parameters. `locale` has a schema description, but `limit` has only type/default/range constraints; the description does not explain how `limit` affects the lists or whether it applies per category. With 50% schema coverage, the description should compensate more.

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 provides River's fixture and recent results, with explicit definitions of 'upcoming' and 'recent'. It is distinct from siblings like get_match_report or get_standings by its focus on fixture lists and results, though it does not explicitly name those alternatives.

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: use this tool for upcoming and recent fixtures, and the note about external data lagging gives caution for live-result needs. However, it does not explicitly say when to use this tool over siblings 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.

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 is covered. The description adds valuable behavioral context beyond the annotations: the editorial score is an opinion of nuevedediciembre.com rather than an official statistic and must be attributed to the site, and the match article is included only when it exists.

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 three short sentences with no fluff. The first sentence front-loads the core payload, the second adds the important editorial-opinion caveat, and the third states the conditional inclusion of the match article. 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?

With no output schema, the description carries the burden of explaining the return payload, and it does so well by listing formation, per-player statistics, editorial scores, and the optional article. It is nearly complete, though it does not describe response nesting or how missing fields beyond the article are handled.

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 both id and locale are already documented in the input schema. The description does not add parameter-specific meaning, but the schema fully handles parameter semantics, so the baseline score of 3 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 is clear about what the tool returns: a single match with its formation, per-player minutes, goals, assists, cards, and an editorial 0–10 rating, plus the published match article when available. It is specific about the resource and content, but it does not explicitly contrast this with sibling tools like get_matches or get_article, so sibling differentiation is mostly implicit.

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 context is implied: an agent should call this when it needs a detailed match report with player ratings and stats. However, there is no explicit statement of when to use it versus alternatives, no pointer to get_matches for finding match IDs, and no mention of 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.

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

Annotations already indicate a read-only, idempotent operation. The description goes beyond them by explaining the semantics of statsSource and explicitly warning that 'matches' totals are partial and must never be presented as career totals. This is genuinely useful behavioral 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?

Three compact sentences: the purpose is stated first, then the statsSource field is explained, then a crucial presentation caveat is given. There is no filler and every sentence carries important information.

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 takes on the burden of describing the response contents Agreement it covers key profile fields and the statsSource logic. It does not enumerate every possible response field or describe not-found behavior, but it is sufficient for a two-parameter read-only lookup.

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 input schema already documents slug and locale fully, including an example and default, with 100% schema description coverage. The description adds no parameter-level meaning beyond what the schema provides, so the baseline of 3 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 clearly specifies the resource: a player, coach, or historical figure of River, and lists concrete contents (biography, position, years, totals). It is distinguishable from siblings like get_matches or get_article by content even though no sibling is explicitly named.

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 first sentence establishes exactly when to use this tool: to fetch a profile card. The statsSource explanation and the warning against presenting 'matches' totals as career totals provide valuable context for using the result correctly, though alternative tools are not mentioned.

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, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond this: data comes from the site cache and the response includes a source that should always be cited. This is meaningful supplementary information without contradicting the annotations.

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 two sentences with no filler. It front-loads the core purpose, then explains the parameter variants and the citation requirement. Every sentence contributes essential information.

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?

The tool is simple (one optional parameter), annotations cover safety and idempotency, and the description explains the parameter semantics and the response source instruction. There is no output schema, so a bit more detail on the exact shape of the returned standings could improve completeness, but it is not necessary for correct selection and 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?

Schema description coverage is 0%, so the description carries the full burden for parameter semantics. It thoroughly explains both enum values: 'zone' means the current tournament table restricted to River's zone, and 'season' means the cumulative annual table for all teams. This goes well beyond the bare enum names 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 clearly identifies the tool as returning standings ('Tabla de posiciones desde la caché del sitio') and explicitly differentiates the two modes: zone returns the current tournament table limited to River's zone, and season returns the annual accumulated table. This is a specific verb+resource pairing that distinguishes the tool from its siblings.

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 context for when to use each parameter value, including the nuance that Argentine tournaments are played in zones and positions restart at 1 in each zone. It also instructs the agent to always cite the source in the response. It does not explicitly name alternative sibling tools or exclusion conditions, but the guidance is clear enough for correct use.

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 mark this as read-only, idempotent, and non-destructive, so the bar is lower. The description adds valuable behavioral context beyond annotations: it explains that results are derived from site data and therefore reflect what's loaded, not a complete transfer market. This helps set expectations about data accuracy and completeness.

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 two sentences with zero filler. It front-loads the core purpose and then provides a succinct, relevant caveat about data provenance. 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 simple list tool with two well-documented parameters and no output schema, the description covers the essential context: what the tool returns (arrivals, departures, coach changes), the data source, and its limitation. It could mention response format, but that is not critical for this level of complexity, so a 4 is appropriate.

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%: both 'season' and 'locale' have clear, self-descriptive descriptions. The tool description adds no further parameter-level meaning, so the baseline score of 3 applies (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?

The description clearly states the resource ('squad movements in a season') and the specific verb ('get who arrived and who left, including coaching changes'). It is unambiguous and distinguishes itself from the listed siblings (matches, standings, articles, etc.), which are clearly different domains.

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 when to use it (when you need squad movements for a season) and adds a caveat about the data being site-derived, not a full transfer market. However, it never explicitly says when NOT to use it or names any alternative tools, so the guidance is 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?

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral nuance: the stats are computed from records rather than manually maintained, matches without loaded results are counted separately instead of as draws, and the tool merges recent matches with the full archive. This goes beyond the annotations without contradicting them.

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 with no waste: first sentence defines purpose and result summary, second reveals behavioral caveats, third explains data source and provides a parameter example. Purpose is front-loaded and each 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?

Given the simple read-only nature, annotations covering safety, and 75% schema parameter coverage, the description is complete. It explains what the tool returns, how missing results are handled, how historical and recent data are combined, and when to use the rivalry parameter. No critical information appears 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 covers 75% of parameters with descriptions. The description repeats the rivalry example 'superclasico', which is already present in the schema, and adds no new semantic information for limit, sport, or opponent. The description neither compensates for nor worsens the schema coverage.

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 returns River Plate's head-to-head history against a specific rival, including games played, wins, draws, losses, and goals. This distinguishes it from generic match-listing tools, though it does not explicitly name a sibling tool for 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?

The description implies usage when an agent needs historical aggregate stats against an opponent, and it gives a concrete example for using the rivalry parameter. However, it never explicitly states when to prefer this tool over siblings like get_matches or get_match_report, nor does it mention any 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 cover readOnly, idempotent, and non-destructive. The description adds that results are ordered newest-to-oldest, contain summaries/URLs, exclude full bodies, and are filterable—valuable behavioral context beyond the annotations.

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 with no filler. The core behavior and the alternative are front-loaded, and each clause adds value—ordering, content shape, filtering, and the get_article pointer.

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 list tool with rich annotations and a simple schema, the description covers ordering, content shape, filtering, and how to proceed to full content. It omits details like response envelope or error cases, but these are not critical given the tool's simplicity and the safety hints.

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 67% (4 of 6 params have descriptions). The description reinforces the filtering params (type, sport, tag) but adds nothing about pagination (page, limit), which are only partially documented in the schema. It does not fully compensate for the missing descriptions, so baseline 3 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 clearly states it lists published notes from newest to oldest, includes summary and URL but not the full body, and can be filtered by type, sport, or tag. This is a specific verb+resource that distinguishes it from get_article, which retrieves full articles.

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 explicitly instructs the agent to use get_article with the returned slug to read a full note, providing a direct alternative. This makes the usage context unambiguous, though it doesn't cover other siblings (which are for different resource types, so not needed).

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 and idempotentHint=true, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it tolerates missing accents and the ñ, which affects query matching. It also instructs the agent to return URLs when citing, which is a usage directive rather than tool behavior, but still useful. No contradiction found.

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, compact sentence that front-loads the purpose, then delivers two key behaviors (accent tolerance and URL instruction) in a logical order. Every word contributes; there is no padding or redundancy.

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?

The tool has no output schema, so the description partially carries the burden of explaining what the agent receives. It tells the agent to return URLs, implying results include URLs, but doesn't specify result structure, pagination, or sorting. For a search tool with 3 parameters and a well-defined scope, this is fairly complete but could mention result format or limits on results. The limit parameter exists but its behavior is not elaborated.

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 67% (query and locale have descriptions; limit does not). The description does not add much parameter-level detail beyond what the schema provides, except the accent tolerance which is about query semantics. The minLength=3 is already in the schema. The description does not explain limit's range or default, but that's in the schema. Overall, the description adds moderate value, but doesn't fully compensate for the missing limit description.

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 ('Búsqueda de texto completo') and clearly delimits the resource scope: 'notas publicadas, jugadores y partidos de River Plate'. This differentiates it from siblings like get_player, get_matches, or list_articles, which target specific entities. It also adds a distinctive behavioral trait (accent/ñ tolerance) that further characterizes 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 description implies when to use this tool: when a full-text search across multiple content types is needed, rather than fetching a known item. It does not explicitly name alternative tools or provide exclusion conditions, but the phrase 'Búsqueda de texto completo' and the scope give clear context. The instruction to return URLs when citing also provides practical guidance for the agent's response.

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