signals

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

No annotations are provided, so the description carries the burden. It clarifies the scope (settled races, GB & IRE, archive) but does not mention read-only behavior, output structure, sorting, or other operational details. It is not contradictory, just minimal.

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, efficient sentence that communicates the essential domain and scope without any filler. Every word adds value, making it appropriately sized and 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 query tool with clear schema and no output schema, the description provides enough core context to understand what the tool does and its scope. However, it does not describe the expected return format or any notable caveats, leaving some ambiguity for an agent.

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 fully documents both parameters (limit and min_runs) with descriptions and defaults, so the description does not need to add parameter details. The description itself adds no extra semantic context beyond the schema, which is acceptable at the baseline for high 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 identifies the tool's purpose: providing trainer×jockey combination strike rates over settled GB & IRE races. It specifies the domain and scope, distinguishing it from sibling tools like draw bias or race signals, but lacks an explicit action verb like 'returns' or 'lists'.

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 by the description: use when needing combo strike rates. However, it does not provide explicit guidance on when to prefer this tool over siblings, nor any exclusions or alternative recommendations.

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 must disclose behavioral traits. It specifies the exact metrics and grouping, giving a clear expectation of the output content. As a read-only query tool, there are no destructive side effects to disclose. It could add more about default behavior without a course filter, but the description is transparent about what is returned.

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, dense sentence that packs all essential information without unnecessary words. It is front-loaded with the key purpose and includes specific metrics and groupings, making every word earn 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 tool with one optional parameter and no output schema, the description covers the main content areas thoroughly. It mentions the metrics and grouping dimensions, giving the agent a solid understanding of what data will be returned. It does not detail the output format or default when no course is specified, but this is not critical for selection and invocation of a read-only query.

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 has one optional parameter 'course' with a clear description and example. Schema description coverage is 100%, so the description adds no additional parameter semantics. Baseline of 3 is appropriate since the schema already handles parameter meaning 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 what the tool provides: 3-year draw-bias aggregates for GB & IRE flat courses, listing specific metrics (IV, A-E, PRB) and grouping by stall third, course, surface, and distance band. This is a specific verbless resource description that distinguishes this tool from siblings like get_combo_stats and get_race_signals, which focus on different data 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 usage context: when draw-bias information is needed, this tool provides the relevant aggregates. It does not explicitly name alternatives or provide when-not-to-use guidance, but the scope is clear from the data focus. Since there are no annotations, the description carries the burden and offers enough context for an agent to select it appropriately.

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 provided, the description carries the full burden. It discloses what data is returned, the score range (0-100), and the conditional 'if the race made the public card,' which is valuable behavioral context. It does not cover edge cases (e.g., invalid race_id or race not on card), but for a read-only retrieval tool this is reasonably transparent.

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, well-structured sentence that front-loads the purpose and then lists the key return elements in a clear, scannable format. No filler or 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 single-parameter retrieval tool with no output schema, the description thoroughly describes the return payload, including per-runner and race-level signals and a conditional about the model pick. This gives an agent sufficient context to know what the tool offers and what to expect, even without error handling details.

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% for the single race_id parameter, and the schema already explains it comes from get_today_races. The description adds no additional parameter-level meaning beyond implying the race must be a public-card race, so it stays at the baseline of 3.

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 ('get') and resource ('Racing Alpha's derived signals for one race') and enumerates exact contents (AI score, fair price, value, draw-bias verdict, model pick). It clearly differentiates from siblings like get_draw_bias by including race-level draw-bias verdict as one component among many.

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 clearly indicates this tool is for a single race's derived signals, which implies usage when you need these signals for one race. It does not explicitly state when not to use it or name alternatives, but the phrase 'for one race' and the specific signal list provide clear context versus siblings like get_today_races.

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 provided, the description carries the full burden. It discloses that picks are 'database-frozen at publication' (immutable snapshot), includes losses, and provides result/P/L/CLV summary. It also implies public access, covering auth needs. It does not mention rate limits or pagination, but for a simple read-only ledger this 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?

The description is two sentences with no wasted words. The first sentence front-loads the core purpose, and the second emphasizes a key feature (including losses). It is appropriately sized for the tool's simplicity.

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 only one optional parameter and no output schema, the description covers the essential content and behavioral traits. It explains what data is included (result, P/L, CLV) and the crucial caveat about losses. It doesn't explicitly describe the return format, but the ledger implies a list, and the schema covers limit. This is sufficiently complete for a low-complexity 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?

The schema fully describes the only parameter (limit) with type, range, default, and ordering. The description adds no parameter-level detail, but since schema coverage is 100%, the baseline of 3 applies. No compensation needed.

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 a tips ledger for Racing Alpha, listing its contents (every pick with result, level-stakes P/L, closing-line-value summary). The phrase 'public tips ledger' and the detail about including losses distinguish it from siblings like get_combo_stats or get_today_races, which serve different purposes.

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 tool is for reviewing complete historical tips with full transparency, emphasizing 'Includes losses; that is the point.' This hints at when to choose it over a filtered or best-of tool, but it does not explicitly name alternatives or state when-not-to-use conditions.

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 transparency burden. It does disclose the important trait that advised prices are database-frozen at publication, and scopes to GB & IRE. However, it doesn't explicitly state read-only behavior, response format details, or other operational nuances, leaving some gaps.

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 with the primary purpose front-loaded and the integration point clearly stated. Every word adds value, and there is no fluff 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?

Without an output schema, the description reasonably indicates the return includes race ids and positions the tool for use with get_race_signals. However, the phrase 'with model picks' is ambiguous as to whether picks are part of the returned data or merely a characteristic of the races, creating a slight gap in complete understanding.

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 empty schema is fully covered. The description doesn't need to explain any parameters, and with 0 params the baseline is 4. No additional parameter context is needed.

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 defines the tool as returning today's GB & IRE horse races with model picks, and explicitly states it returns race ids for use with get_race_signals. This distinguishes it from sibling tools like get_draw_bias or get_tips_ledger, which focus on different aspects.

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 a clear use case by saying 'Returns race ids usable with get_race_signals,' indicating a workflow. However, it does not explicitly mention when not to use this tool or compare it to alternatives, so it falls 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.