Football Atlas

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: it returns claims with sources and confidence and 'never an unsourced answer.' This conveys the tool's guarantee about answer quality, which is 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 compact sentences with zero redundancy. The purpose and usage are front-loaded, and the behavioral guarantee is stated clearly. 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 tool with one parameter and a rich output schema, the description covers purpose, usage, and key behavior. It could mention what happens when no match is found or confidence is low, but given the output schema exists and the tool is simple, the description is sufficiently complete 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 coverage is 0%, so the description must compensate. It clarifies that the 'question' parameter is a natural-language question (user asked in words), which adds semantic meaning beyond the schema's type and length constraints. It does not provide examples or format details, but the single parameter is well contextualized.

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 ('Answer') and resource ('the corpus'), and clarifies the output type (matched object's claims with sources and confidence). It also explicitly distinguishes this tool from 'search', so an agent can tell them apart without opening schemas.

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?

Provides explicit when-to-use guidance: 'Use this when the user asked a question in words; use search when you want to see the candidates yourself.' This directly names the alternative and the condition that selects it, leaving no ambiguity.

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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the returned content (claims and cited sources) but does not disclose error behavior or missing-id handling; with annotations carrying the safety burden, this is acceptable.

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 efficient sentences: the first states the operation and return contents, the second gives the usage context. Nothing is redundant, and the most important 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 simple one-parameter read-only tool with an output schema and strong annotations, the description is nearly complete. It explains what the tool returns and when to call it; the only minor gap is explicit differentiation from closely related sibling tools, which is not essential here given the clear by-id semantics.

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 has no description for the 'id' parameter and 0% description coverage, so the description must compensate. It does so by clarifying that 'id' is a knowledge object id and that it should come from search, answer, or get_topic, which gives the agent actionable context beyond the bare 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 states a specific verb ('Fetch'), a precise resource ('one knowledge object by id'), and what the result includes ('its claims and the sources each claim cites'). This clearly distinguishes it from broader search or topic tools and from likely sibling getters that look up different facets.

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 trigger for use: 'Use this once search, answer or get_topic has given you an id.' It clearly tells the agent when this tool is appropriate, though it does not mention alternatives or explicitly say 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 establish read-only, idempotent, and non-destructive behavior. The description adds that results are limited to verified objects and ordered by recency, which is useful context, but it does not disclose details like pagination or whether the list is exhaustive. The safety profile is fully covered by 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 tight sentences with no filler. The core result ('most recently verified knowledge objects') is front-loaded, followed by practical use cases.

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 one optional limit parameter, the description combined with annotations and an output schema leaves no critical gaps. The use cases clarify why an agent would invoke it, and the safety profile is fully annotated.

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 fully documents the single optional 'limit' parameter with type, default, minimum, and maximum, making its semantics self-evident. The description adds no parameter-specific meaning, and with 0% schema description coverage it does not compensate, but the parameter is simple enough that no additional explanation is necessary.

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 resource ('verified knowledge objects') and the selection criterion ('most recently verified'), and frames it as a freshness signal. It does not explicitly contrast itself with sibling tools like get_overview or search, but the scope is specific enough to be distinguishable.

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 concrete use cases: judging how current the corpus is and seeing what changed since the last read. It does not state when not to use the tool or name alternatives, so it stops short of full routing 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?

Annotations already establish that the tool is read-only and idempotent (readOnlyHint=true, idempotentHint=true, destructiveHint=false). The description adds meaningful context by outlining the type of information returned (counts, tags, freshness), which helps set expectations without contradicting any annotation. It does not delve into potential limitations like authorization or rate limits, but such details are less critical for a discovery tool with an output 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 highly concise: two sentences, with the first listing the tool's contents and the second giving a clear use case. Every word carries weight, and the structure front-loads the core purpose before adding usage guidance. No fluff exists.

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 parameterless discovery tool with an output schema, the description covers all necessary aspects: what it does, what it returns, and when to use it. It even frames the tool within a cognitive workflow ('land here first'), which is rare and useful. There is no missing information that would prevent an agent from correctly invoking this tool.

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?

This tool has zero parameters, so the schema is fully descriptive by default (100% coverage). A baseline of 4 applies here because there are no parameters for the description to clarify. The description wisely focuses on the output and when to call the tool, which is the right use of space.

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 the tool's purpose with a specific verb and resource: it provides a 'corpus overview' including 'counts by type, published tags, freshness.' It clearly distinguishes itself from siblings by explaining its role as a first-stop for determining if the corpus can answer a question, which sets it apart from search or get_entity tools.

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 states when to use the tool: 'Use this first when you land here and do not yet know whether this corpus can answer your question.' While the trigger is clear, it does not explicitly name sibling tools or state when not to use it, leaving the agent to infer that more specific tools should be used for targeted lookups.

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 establish readOnly, idempotent, non-destructive behavior. The description adds meaningful behavioral detail: the graph traversal semantics (outgoing and incoming relations with relation types) and the side-effect-free widening intent. It does not mention potential limits or pagination, but output schema covers return structure.

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 concise sentences with no filler. The core behavior is front-loaded, and the usage instruction is placed second for immediate clarity.

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 parameter), has an output schema, and the description supplies the key workflow context (after get_entity). The only notable gap is the under-specified id parameter, but overall an agent has enough to call this 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?

Schema description coverage is 0%, so the description must compensate for the 'id' parameter. It only says 'of an object', which vaguely implies id identifies an entity, but does not explain what kind of id is expected or how to obtain it. The instruction to use after get_entity hints at the source, but the parameter semantics are under-specified.

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 uses a specific verb ('Graph') and resource ('neighbours of an object'), explicitly listing what is returned (outgoing and incoming relations with their relation types). It clearly distinguishes from get_entity by positioning itself as a widening step, so an agent can tell sibling tools apart.

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 says to use it 'after get_entity' and for the purpose of widening an answer with adjacent objects. It names the relevant alternative (get_entity) and the sequencing, though it does not explicitly mention when not to use it versus other siblings like get_overview or search.

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 safety is covered. The description adds behavioral context by explaining the dual mode: returning the whole registry or filtering by one object. This goes beyond the annotations and helps the agent understand what effect the optional parameter has on the output size and scope.

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. The first sentence states the core behavior (whole registry vs. filtered by object), and the second gives usage intent. The information is front-loaded, making it easy for an agent to quickly assess the tool's purpose and relevance.

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's simplicity (one optional parameter), presence of an output schema, and comprehensive annotations, the description is nearly complete. It covers the two modes of operation and when to use the tool. It does not explicitly describe the structure of a 'source' or edge cases like invalid object_id, but these are minor given the output schema and the straightforward nature of the tool. A small gap is the lack of an explicit statement that object_id is optional, though that is inferable from 'or'.

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 explain the parameter. It does so implicitly by saying 'or just the sources cited by one object', which clarifies that object_id is optional and filters the result to sources associated with that object. This provides the necessary semantic meaning that the schema lacks.

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 retrieves the source registry, either in whole or filtered by a single object. The verb 'get' is implicit but the resource and scope are explicit. It distinguishes from sibling tools by focusing on sources rather than entities, topics, or search.

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 provides explicit use cases: checking what a claim rests on, and judging the corpus before trusting it. It gives clear context for when to call this tool but does not explicitly mention alternatives or exclusions. Since the use cases are specific, an agent can infer when not to use it, but the guidance could be stronger by naming sibling 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 already declare read-only, idempotent, and non-destructive behavior, so the safety profile is covered. The description adds a small clarification that 'topics are content-backed tags', which gives context but does not disclose additional behavioral aspects like pagination, ordering, or limits. Given annotations, this is adequate but not rich.

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 fluff. It front-loads the primary action, then adds usage guidance and alternative pointers. Every sentence earns its place, and the structure is clear.

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 a single parameter and a straightforward output (list of knowledge objects), the description is complete: it states what it lists, how to use it, and when not to use it. It does not need to explain return format since that is presumably in the output schema. The agent has enough context to invoke 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?

The schema only defines 'tag' with length constraints and has no description. The description clarifies that the parameter is a 'tag' representing a topic, and that topics are content-backed tags. This provides minimal meaning beyond the schema but does not specify valid formats or provide examples. Since schema coverage is 0%, the description partially compensates but remains thin.

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 verb ('List') and resource ('knowledge objects') and scopes it by 'tag'. It also distinguishes itself from siblings by naming 'search' and 'get_overview' explicitly, so an agent can tell them apart without opening schemas.

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 when to use this tool: 'Use this to browse a known topic'. It then directs the agent to alternatives: 'use search when you have a question rather than a tag, and get_overview to see which tags exist'. This provides clear usage guidance and 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 indicate read-only, idempotent, and non-destructive behavior. The description adds behavioral details such as the search ignoring accents and apostrophes and that each hit includes a relevance score and matched fields, which complements the annotations without contradiction.

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 concise and well-structured, front-loading the purpose and then adding usage and behavioral notes. It avoids unnecessary fluff and is easy to scan.

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 description gives sufficient context for typical usage, including when to use the tool and what to expect in results. It does not cover edge cases or error handling, but given the presence of annotations and the simplicity of the tool, it is 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?

The description elaborates on the query parameter by advising to use the user's own words, but it does not explicitly explain the limit parameter. Since the schema provides no property descriptions, the description only partially compensates for the 0% 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 performs a full-text search over the knowledge graph, and explicitly distinguishes it from get_entity by instructing to use it when you have a question rather than an 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?

It provides explicit usage guidance: 'Use this whenever you have a question rather than an id' and advises to query in the user's own words due to accent/apostrophe insensitivity, making it clear when to choose this tool over alternatives.

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