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

With no annotations provided, the description carries the full burden. It clearly states that the tool strips margin and returns probabilities, implying a pure calculation with no side effects. It doesn't explicitly state read-only behavior, but the nature of the operation makes that obvious. It also clarifies the input format (decimal prices) which is helpful.

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, front-loaded sentence stating the purpose, followed by a clear parameter explanation. Every word earns its place; 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?

For a simple single-parameter calculation tool with an output schema available, the description fully explains the input and the conceptual output. It doesn't need to describe the return format since the output schema covers that. There are no missing details an agent would need 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?

Schema description coverage is 0%, so the description must compensate. It does: it explains that 'prices' are decimal prices of all outcomes of the same market and provides a concrete example ([1.75, 3.6, 4.4] for 1X2), adding meaning far beyond the raw 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 action (strip bookmaker margin) on a specific resource (one market's prices) and clearly differentiates from siblings like evaluate_price or parlay_math by focusing on devigging a single market. The example clarifies the intended use.

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 makes the purpose clear and it's obvious this is the only tool for devigging among the siblings, but it doesn't explicitly mention alternatives or when not to use it. The context is clear enough for an agent to select it correctly.

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 must disclose behavioral traits. It states the tool 'reports' status, implying a read-only operation, but does not explicitly mention safety, side effects, error conditions, or required setup. This is a minimal disclosure that leaves the agent to infer 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?

The description is a single, concise sentence that conveys the essential purpose with no fluff or redundant information. 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?

The tool has an output schema (not shown here) and no parameters, so the description only needs to convey what the tool does. It does that clearly. It does not mention potential nuances like what happens if components are not configured, but the output schema likely covers return details. Overall, it is sufficient for a simple status check.

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 input schema is empty and the description coverage is trivially 100%. With no parameters, there is nothing to describe; the baseline of 4 for zero-parameter tools 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's action: 'Report whether' a specific subject ('optional private analysis engine and private plugins') are wired up. It is specific about the resource and distinguishes it from all sibling tools, which deal with betting data and calculations.

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 a diagnostic usage—checking the wiring status of optional components—but does not explicitly state when to use this tool versus alternatives. Since there are no obvious siblings that perform a similar status check, the context 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?

No annotations are provided, so the description must disclose all behavioral traits. It clarifies input formats (probability as decimal, not percentage) and parameter semantics, but it does not explicitly state that the tool is non-destructive or has no side effects. For a calculation tool, this is acceptable but not fully transparent about output 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?

The description is concise, front-loaded with a one-line summary, and uses a clear Args block. Every sentence adds value, with no redundant or filler content.

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 tool's purpose, all parameters, and example formats. An output schema exists, so return values are not required. Minor gaps like input range validation (probability between 0 and 1, odds >1) are not mentioned, but these are likely enforced elsewhere or implied. Overall, sufficient for an agent 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?

Schema coverage is 0%, so the description fully compensates by explaining each parameter: probability as a decimal, odds as decimal price, margin_pct as the required edge, and kelly_fraction as stake scaling. It adds meaning well beyond the schema's bare names and defaults.

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 clear action ('Judge a price against your own probability') and lists the computed outputs (fair odds, EV, minimum odds, scaled Kelly stake). This distinguishes it from siblings like devig_market and parlay_math, which address different betting calculations.

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 purpose implies usage for single-price evaluation, but there is no explicit guidance on when to choose this over alternatives (e.g., devig_market for market-wide vig removal). No when-not conditions are stated, leaving some inference to the agent.

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 carries the behavioral burden. It discloses the broad scope, the conditional inclusion of scores, and the effect of only_finished. It does not mention edge cases like timezone handling or live-match behavior, but these are secondary for a read-only fixtures query.

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 main behavior is front-loaded in one clear sentence, followed by a compact parameter list where each line adds meaningful detail. There is no filler or redundant repetition of schema types.

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?

All three parameters are explained, an output schema exists, and the tool's scope is unambiguous. The only notable gap is the absence of a brief pointer to a sibling tool like get_results when result-specific data is needed, but that is not required to 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?

Schema description coverage is 0%, and the description fully compensates: date gets an ISO format with an explicit example, league is defined as a case-insensitive substring filter on the competition name, and only_finished is explained as returning only matches with a final score.

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 names a specific operation: retrieving all football fixtures for one date across every competition, with scores when played. It states the resource and scope clearly, and the date-focused framing helps distinguish it from result- or bet-oriented 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?

It clearly establishes when to use the tool: when a caller wants all fixtures for a single day, optionally filtered by league or finished matches. It does not explicitly contrast it with get_results or state when not to use it, so it stops short of full 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?

With no annotations provided, the description carries the behavioral disclosure burden. It conveys a read-style retrieval of final scores and adds the 'input for settling bets' context, but it does not explicitly state read-only guarantees, permissions, or pagination behavior, so the disclosure 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?

One front-loaded purpose sentence plus a compact Args block; every line adds information and there is 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 two-parameter query with an output schema, the description covers the date scope, league filtering, and the downstream use case. It lacks an explicit pointer to get_fixtures for non-finished matches, but the 'finished matches' framing largely compensates.

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 explain the parameters, and it does: date is specified as an ISO date with an example, and league is described as an optional substring filter. This goes beyond the schema's bare type and default information.

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 that the tool returns finished matches with final scores for a single day and identifies it as the input for settling bets, which clearly distinguishes it from get_fixtures. The verb and resource scope are specific enough for an agent to know exactly what it retrieves.

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 clearly specifies the context — one day's finalized results, with an optional league filter — and ties it to the settle_picks workflow. It does not explicitly name when to use get_fixtures instead, but 'finished matches' implies the contrast with upcoming fixtures.

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 behavior, but it only states what is computed. It does not mention side effects, error handling, assumptions about odds format, or the meaning of 'edge decay' beyond the phrase. This is insufficient for a tool with a complex input.

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 front-loaded with the primary purpose. The args example is helpful, though presented informally rather than as a structured spec. Overall, it is efficient and easy to parse.

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 a single parameter that is a free-form array of objects, yet the description only gives an example. It does not define required fields, acceptable ranges, or how the output is structured (though an output schema exists). An agent may struggle to correctly construct valid input beyond the given example.

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 offers zero description for the 'legs' array items, but the description provides a concrete example showing the expected structure (probability and odds per leg). This adds some value beyond the empty schema, though it does not fully specify all possible fields or constraints.

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 computes combined odds, EV, and edge decay for an accumulator, with a specific verb and resource. This distinguishes it from siblings like get_fixtures or evaluate_price, 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?

No guidance is given on when to use this tool versus alternatives. It does not mention prerequisites, scenarios, or exclusions, leaving the agent to infer usage solely from the name and description.

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 is the only behavioral disclosure, and it provides useful traits: supported markets, fuzzy team-name matching, fallback date handling, and the output shape. It leaves edge behaviors implicit, such as how unmatched picks are treated and whether anything is persisted, so it is not a 5.

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 tightly structured: purpose sentence, a worked pick example, market list, matching note, and an Args block. It is front-loaded and every sentence contributes.

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 moderate-complexity settlement tool, it covers input shape, market domain, matching tolerance, and date fallback, while the output schema covers return values. Slightly more detail on unmatched picks and exact market encodings would make it fully 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?

Schema coverage is 0%, but the description compensates by documenting each pick's required fields with a concrete JSON example and explaining the optional date fallback. It stops short of listing exact accepted strings for every market and any odds constraints, which is the main gap.

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 names a concrete action ('settle'), the object ('list of picks'), the data source ('real scorelines'), and the output ('per-pick results plus totals'). This clearly distinguishes it from siblings like get_results and get_fixtures, even without naming them.

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 makes the invocation context clear: pass picks that need settling and an optional fallback date, and it resolves them against the day's fixtures. It does not include explicit when-not-to-use guidance or name alternatives, so it misses the top tier.

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