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

With no annotations provided, the description must convey all behavioral traits. It mentions '12 specialized agents' and the return structure (consensus, confidence, edge), but omits details like execution time, failure modes, or required permissions. This is adequate but not comprehensive.

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 extremely concise: a one-sentence purpose, followed by two bullet-like lines for parameters and return. Every word adds value, with no fluff or repetition. Front-loading the core action makes it 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?

Given two simple required parameters, no nested objects, and an output schema, the description provides sufficient context. It explains the input sources and output structure. The sibling tools list confirms this is the only deep analysis tool, so the description stands alone well.

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 provides no descriptions (0% coverage), so the description adds significant value by clarifying the 'sport' allowed values and sourcing 'game_id' from get_live_odds. This meaningfully aids correct parameter usage 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 uses a specific verb ('analyze') and clearly identifies the resource ('game'). It distinguishes from sibling tools (get_live_odds, get_todays_picks, etc.) by highlighting the multi-agent analysis scope, leaving no ambiguity about the tool's unique function.

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 the purpose and references the source of game_id ('from the get_live_odds tool'), which guides correct invocation. However, it does not include when-not-to-use or alternative tools for similar tasks, 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.

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

No annotations provided, so description carries burden. It indicates the tool retrieves active alerts and returns a list with severity and details, but does not mention side effects or safety profile. Adequate for a simple read operation.

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, concise and front-loaded. Every sentence adds value with no wasted words.

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?

Tool has no parameters and output schema exists; description suffices by explaining return content. Could clarify what 'active' means, but overall complete for a simple retrieval 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?

No parameters in schema; schema coverage is 100%. Baseline for 0 parameters is 4, description adds no param info but none 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?

Clearly states the verb 'Get' and the resource 'active alerts' with specific examples like line movements and injury impacts. Distinguishes from siblings like get_system_status and get_model_stats.

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 explicit guidance on when to use this tool versus alternatives. Usage is only implied, with no conditions or exclusions provided.

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 full burden. It accurately describes a read-only operation returning completed picks with results, which is transparent. However, it could be more explicit about ordering or time range (e.g., 'recently' is vague).

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 extremely concise with two sentences plus a structured Args/Returns section. Every sentence adds value without extraneous 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?

Given the tool's simplicity and the presence of an output schema (not shown), the description provides sufficient context: it lists the output as 'List of completed picks with results (W/L) and details'. This is adequate for understanding the return value without being overly detailed.

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 significant meaning beyond the input schema: it explains the limit parameter's purpose (number of recent completed picks), default (20), and maximum (50). The schema only shows type and default, so the description provides essential constraints and context.

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 gets 'recently completed (resolved) picks with results', which distinguishes it from siblings like get_pending_picks (unresolved) and get_todays_picks (today's picks, potentially not completed).

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 for recently completed picks but does not explicitly provide when-to-use or when-not-to-use guidance compared to alternatives. The context from sibling names helps, but no direct exclusions are given.

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 provided, and the description only states basic return fields (rankings, win rates, total picks). It does not disclose read-only nature, data freshness, ordering, or any side effects, leaving a gap for an agent assessing safety.

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, front-loaded with the action, no extraneous information; every word 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 description explains what the tool returns (leaderboard with rankings, win rates, total picks). With an output schema present, the description does not need to detail return structure further, though ordering or scope could be added.

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 zero parameters, so the description has no need to add parameter info. The baseline score of 4 applies per guidelines for 0-parameter tools.

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 a pick leaderboard with user rankings by win rate, distinguishing it from siblings like get_win_rate (individual) and get_top_pick.

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 explicit guidance on when to use this tool vs alternatives; usage is implied from the description but no when-not or exclusions are stated.

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 bears full responsibility. It states the tool retrieves 'live odds' and returns specific odds types (moneyline, spread, total), but does not disclose behavioral traits like rate limits, authorization requirements, or whether the operation is read-only. The description implies a read operation but lacks explicit safety guarantees.

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 with a clear front-loaded purpose sentence and an optional Args section. However, the Args format could be integrated into the main text for brevity; the current structure is still efficient.

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 presence of an output schema, the description need not detail return values, but it usefully summarizes what the odds include. For a simple tool with one parameter, the description covers essential aspects (sources, sport list, return type), though it omits edge-case behavior like handling invalid sports or empty games.

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 0% description coverage, leaving the parameter 'sport' underspecified. The description compensates by listing valid sport values ('nba', 'nhl', 'ncaab', 'mlb'), which adds essential meaning beyond the schema's type constraint.

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 ('Get'), resource ('live odds'), and data sources ('FanDuel and BetMGM'), distinguishing it from sibling tools like get_todays_picks or get_pending_picks which deal with picks rather than odds.

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 on when to use this tool vs alternatives (e.g., when to use get_live_odds vs get_todays_picks). No explicit conditions or exclusions provided, leaving the agent to infer usage context from the tool 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 carries the full burden. It states the tool returns stats but does not disclose behavioral traits such as whether data is live or cached, any authentication requirements, or performance implications. For a read-only tool with no parameters, the absence of such detail is acceptable but not ideal.

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 (two sentences) and front-loaded with the core purpose. However, the 'Returns:' line is redundant with the first sentence and could be merged for greater conciseness.

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 (no parameters, clear output), the description adequately lists the returned metrics. An output schema exists, so deeper details are delegated there. Still, a brief mention of the output schema would improve completeness.

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 input schema is fully covered (100%). The description adds no parameter semantics, but the baseline for zero parameters is 4. No further information 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 states the tool retrieves model performance statistics, including win rates by confidence level, total picks, and discipline. It is specific about the resource and metrics, but does not differentiate from the sibling tool get_win_rate, which likely provides a simpler overall win rate.

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 no guidance on when to use this tool versus alternatives like get_win_rate or get_completed_picks. It lacks any context for appropriate usage scenarios or exclusion criteria.

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 discloses the return type and content (list of pending picks with details and pending duration). It implies a read-only operation without side effects, which is adequate for the tool's simplicity.

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 with two sentences, front-loading the action 'Get all currently unresolved (pending) picks'. No redundant information is present.

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 zero parameters and the existence of an output schema (which covers return values), the description is complete. It explains the purpose and return data adequately without needing further elaboration.

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 100%. The description adds no parameter information, which is acceptable as there are none to document.

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 all currently unresolved (pending) picks waiting for game results. It distinguishes itself from sibling tools like get_completed_picks by specifying 'pending' status.

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 for pending picks but does not explicitly provide guidance on when to use this tool versus siblings like get_completed_picks or get_todays_picks. No exclusions or alternatives are 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?

No annotations are provided, so the description carries the full burden. It lists what the tool returns (uptime, DB connectivity, service health), which gives some behavioral insight. However, it does not mention whether the operation is read-only, idempotent, or any side effects, leaving gaps in transparency.

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 with two sentences, clearly stating the purpose and the return information. It is front-loaded with the key purpose. Slight improvement could be a more structured list for the return values, but it remains efficient.

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 no parameters and an output schema exists, the description adequately covers the return items (uptime, DB status, etc.). It is complete enough for an agent to understand what the tool does and what to expect.

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?

There are zero parameters, and the input schema has no properties with descriptions. The rule sets a baseline of 4 for 0 parameters, and the description does not need to add parameter information since there are none.

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 'Health check' and specifies what it checks: uptime, database status, API key status, and scheduler health. This precisely identifies the verb (get) and resource (system status), and distinguishes it from sibling tools that are unrelated.

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 checking system health, and since there are no other health-related sibling tools, the context is clear. However, it does not explicitly state when to use or not use this tool, which would improve 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?

No annotations are provided, so the description carries the full burden. It describes a read operation ('Get'), but does not explicitly state that it is read-only, safe, or any limits such as rate limiting or authentication requirements. However, the behavior is straightforward and not misleading.

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 with no wasted words, front-loading the purpose in the first sentence. It includes structured Args and Returns sections. However, it could be slightly more streamlined by integrating the default value into the purpose sentence.

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 simplicity of the tool (1 optional parameter, no output schema, no nested objects), the description is fairly complete. It details the return fields (pick name, bet type, line, odds, confidence, edge score). It lacks information on pagination or limits, but these are not critical for this type of 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 input schema has 0% description coverage, but the description lists the acceptable values for the 'sport' parameter: 'nba', 'nhl', 'ncaab', 'mlb', or 'all' (default). This adds significant meaning beyond the schema, though it does not mention case sensitivity or error handling.

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 purpose: 'Get today's AI-generated picks with confidence and edge scores.' It specifies the verb 'Get', the resource 'today's AI-generated picks', and the included scores. It is distinct from sibling tools like get_top_pick (singular) and get_completed_picks (past).

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 for today's picks via the filter by sport, but provides no explicit guidance on when to use this tool versus alternatives (e.g., get_top_pick for a single pick, get_completed_picks for past picks). No exclusions or prerequisites are given.

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 cover behavioral traits. It discloses that the tool returns the best pick with analysis details, indicating a read operation. However, it lacks information on caching, freshness, or required permissions, which is acceptable for a simple tool but not thorough.

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, with the core purpose in the first line followed by a succinct parameter and return value explanation. No extraneous information is present.

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 simplicity of the tool (1 optional parameter, no output schema), the description is minimally adequate. It explains the parameter and return type at a high level but lacks details on the returned pick structure or analysis format. For a tool this straightforward, it suffices.

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 essential meaning to the sole parameter 'sport', listing allowed values ('nba', 'nhl', 'ncaab', 'mlb', 'all') and the default 'all'. This compensates for the 0% schema coverage completely.

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 single highest-confidence pick available today. The verb 'Get' and resource 'the single highest-confidence pick' are specific, and it distinguishes from sibling 'get_todays_picks' which likely returns multiple picks.

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 for obtaining the top pick, but does not explicitly state when to use this tool versus alternatives like get_todays_picks or analyze_game. No guidance on exclusions or prerequisites is provided.

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 full burden. It discloses it retrieves documented win rate with resolved picks, implying a read operation. No contradictions, but lacks details on potential rate limits or data freshness.

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 structured Args/Returns sections. Every line is necessary; no fluff. Exceptionally concise.

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 low complexity and one parameter, description covers purpose, parameter options, and return structure. Output schema exists but isn't shown; description still provides key fields (win rate, record, breakdown). Missing guidance on when to use among siblings.

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?

Input schema has one parameter 'period' with only type and default. Description adds allowed values 'all', '30d', '90d', which is helpful. Without schema description coverage, this compensates well.

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 gets the betting analyzer's documented win rate with real resolved picks. It uses a specific verb (get) and resource (win rate), and distinguishes from siblings like get_model_stats or get_leaderboard.

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 on when to use this tool versus alternatives like get_model_stats or get_leaderboard. The description only states what it does without providing context for selection.

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