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

Annotations already convey a safe read-only, idempotent operation. The description adds value beyond that by disclosing that retrieved_at is not price freshness, that pages are metered, and what data is preserved (participant identity, line value, price updated_at). These are meaningful behavioral notes not present in 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 three concise sentences, each serving a distinct purpose: the action, the call-order constraint, data semantics, and operational cautions. It is front-loaded and contains zero filler, making it efficiently readable by an agent.

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 covers the essential aspects: when to call, what data is returned (participant, line value, timestamps), and operational constraints like metering. It lacks a detailed return format description and explicit pagination details, but given the tool's simplicity and that the data-property hint implies the return structure, it is reasonably 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 adds context for event_id (must originate from list_events) and market_ids (explicitly request live markets 41/42/43), going slightly beyond the schema's descriptions. However, page and limit lack schema descriptions, and the description only hints at pagination through the metering note without explaining those parameters. Given the 60% coverage, it partially compensates but not fully.

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 action (Fetch), the resource (open per-affiliate main lines), and the scope (for one event_id). It distinguishes itself from siblings like list_events and list_markets by its specific focus on main lines, leaving no ambiguity about what the tool does.

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 guidance: only call after list_events returns the exact ID, don't treat retrieved_at as price freshness, request live markets 41/42/43 explicitly, and be mindful of page metering. It lacks an explicit 'when not to use' or alternative tool mention, but the context is sufficient for correct invocation.

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?

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context beyond that: the result is current and excludes retired affiliates, and the warning about not treating catalog rows as proof of a plan or opportunity. No contradictions exist.

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, the first front-loads the action and scope, the second is a purposeful caveat. Every phrase earns its place and nothing is redundant.

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 parameter-less list tool, the description fully covers the output content (IDs and names), the output scope (currently published, excluding retired), and includes an important caveat on interpretation. No output schema is required, and the description gives enough context to call the 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 takes zero params and schema coverage is 100%, so the baseline is 4. The description does not need to explain any parameters or add field-level details.

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 and resource: 'List currently published affiliate IDs and names'. It clearly scopes the list to current, non-retired affiliates, which makes it distinguishable from sibling tools like list_sports or list_markets without needing to open their 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 usage is implied by the tool's purpose, and the caveat warns against using catalog rows as proof of plan access or open offer. However, it does not explicitly compare with sibling list tools or state when to switch to alternatives, so it misses the top levels of 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 cover read-only/idempotent behavior. The description adds useful nuance: it returns open main lines by defaultable market/affiliate IDshare and warns that each local page refetches a metered snapshot. It does not contradict 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?

Three short sentences, front-loaded with the core purpose; every sentence adds useful information with 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 no-output-schema tool, it explains the returned value, tells when not to use it, points to get_main_lines for follow-up, and notes pagination/metering. Minor gaps remain around exact response shape.

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 date, offset, and list IDs, and the description adds context for market/affiliate defaults and the sport/date scope. However, sport_id and pagination parameters are only implied; with 57% schema coverage the description could explain 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 first sentence clearly states what the tool returns ('event IDs and summaries') and its scope ('one sport and date'), which also sets it apart from list_futures.

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 warns against using it for futures or price quotes and directs the agent to use the returned event IDs with get_main_lines. This is concrete, actionable 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 cover safety and world-openness, but the description adds real behavioral context: it requires an eligible Ultra plan and uses opaque cursor pagination. This goes beyond the structured annotations and helps the agent predict authorization and iteration behavior.

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, information-dense sentences. Each sentence carries scoping, exclusion, operational requirements, and pagination without fluff.

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 list tool with no output schema, this covers the critical agent needs: what it lists, what it does not handle, access requirement, and pagination. It does not mention return shape, but that's acceptable with no output schema and simple list 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?

Schema coverage is 33% — only market_ids and affiliate_ids have descriptions. The description mentions futures page and cursor pagination, which helps with cursor, but leaves sport_id, limit, and include_settled unexplained in prose. Existing schema defaults compensate partially.

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 ('Read') and resource ('futures competition page' scoped by sport). It explicitly separates itself from dated event discovery, which is enough to distinguish it from list_events and other list 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?

It gives clear when-not-to-use guidance ('do not use for dated event discovery') and warns against treating partial pages as complete. It does not name sibling tools explicitly, but the exclusions semantically route to list_events.

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 read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations: market definitions are not price quotes, live and date-based discovery are incompatible, and sport/live filters rely on catalog metadata. These are valuable operational warnings an agent would not otherwise know.

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 short and front-loaded with the primary purpose. The first two sentences are efficient and informative, but the third sentence, 'Catalog sport/live filters use catalog metadata,' is vague and likely to confuse an agent without adding clear value.

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 and six optional parameters, the description gives useful warnings but does not explain what result shape to expect, how to select between 'market definitions' and 'available markets,' or pagination behavior. It is adequate for a simple list tool but leaves notable gaps for fully 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 only 17%, so the description must compensate for six parameters. It explains the relevance of sport, date, and live, and touches on date-based offset behavior, but it does not describe page/limit pagination or clarify how the two discovery modes map to specific parameter combinations. Partial compensation, not full.

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 discovering market definitions or available markets for a sport/date, with a specific verb and resource. It distinguishes the two discovery modes but does not explicitly differentiate from sibling tools such as list_events or get_main_lines, so it falls short of a 5.

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 clear when-not guidance: do not treat definitions as price quotes, do not combine live with date-based discovery, and use catalog metadata for catalog filters. It provides useful usage context but never names a specific alternative tool, such as get_main_lines for prices, so it lacks full alternative routing.

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 read-only, open-world, and idempotent behavior. The description adds meaningful context beyond that: the rows are 'canonical' and must not be used as evidence of live prices, current events, or plan access. This gives an agent a useful behavioral constraint that annotations alone do not express.

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 short sentences with no filler. The purpose is front-loaded, and the second sentence earns its place by preventing a realistic misuse of the returned catalog data.

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 list tool with no output schema, the description specifies the output contents and adds an important usage caveat. It does not describe pagination or response format, but the lack of parameters and the simplicity of the output make the description sufficiently 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 tool has zero parameters and the schema coverage is trivially complete, so the baseline of 4 applies. The description's mention of 'current canonical sport IDs and names' clarifies exactly what the returned rows represent, which is sufficient for parameter-free operation.

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 opens with a specific verb and object: 'List current canonical sport IDs and names.' It clearly states both the action and the exact output content, making it obvious what this tool does and distinguishing it from sibling list tools that target different entities like markets, affiliates, or futures.

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 a clear negative usage guideline: the returned catalog rows should not be treated as evidence of current events, prices, or plan access. It does not explicitly name sibling tools or state when to choose this one over them, but the resource type (sports) and output scope are unambiguous enough for an agent to route correctly.

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