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

No annotations are provided, so the description must fully disclose behavioral traits. It states that the tool queries 'live Supabase data' and computes scores, implying a non-destructive read operation. However, it does not explicitly declare read-only behavior, idempotency, or potential side effects. The listing of dimensions is informative but not sufficient for full 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 reasonably concise with a clear purpose statement upfront. It uses a bulleted list for dimensions, which improves readability. However, it could be slightly more streamlined by removing redundant phrase 'using live Supabase data' if context is obvious.

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 an output schema (context signal), the description adequately summarizes return values without needing full details. It covers the key components: overall score, certification eligibility, per-dimension breakdowns, and recommendations. Missing error handling or prerequisite conditions, but acceptable for a tool with simple parameters and existing output schema.

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%, so the baseline is 3. The description adds no significant meaning beyond what is already in the input schema. For instance, the default value and description of 'assessment_type' are already present in the schema. The description does not introduce new constraints or clarifications.

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 assesses a league's readiness using live data and lists five specific dimensions with weights. It outlines the return values (overall score, eligibility, breakdowns, recommendations). However, it does not explicitly differentiate from sibling tools like 'evaluate_league' or 'rank_league_maturity', which have overlapping 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 usage for readiness assessment but provides no explicit guidance on when to use this tool versus alternatives. There are no when-not-to-use statements or exclusions. Without such context, an agent may struggle to choose among related sibling tools.

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; description discloses read-like behavior (analyze, return score) but does not specify if it modifies data, requires prerequisites, or has rate limits. Adequate 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?

Two sentences, concise and to the point. The second sentence uses bullet-style listing but is effective. No superfluous 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 a single parameter and presence of an output schema, the description covers inputs, analysis focus (dimensions, gaps), and outputs (quality score, recommendations). Complete for a check 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?

Only one parameter (league_id) with full schema coverage. Description does not add extra meaning beyond the schema's 'UUID of the league.' Baseline score applies.

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 tool checks fingerprint quality and completeness, analyzing dimensions and returning a score with recommendations. Distinguishes from siblings like 'get_fingerprint' and 'generate_fingerprint'.

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?

Implied usage for assessing existing fingerprints, but lacks explicit guidance on when to use versus alternative tools like 'assess_league_readiness' or 'evaluate_league'. No exclusion criteria or context 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 clearly states what the tool returns: archetype classification, confidence level, key characteristics, and edge cases. This gives the agent good insight into the output. There is no mention of side effects or restrictions, but for a classification tool, 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 concise with four sentences. It starts with the core action, then lists archetypes, then provides context about their importance, and finally states the return fields. The structure is logical and front-loaded, but the middle sentence about 'distinct edge cases...' could be more compact.

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 complexity (classification into 5 archetypes with specific outputs), the description covers purpose, archetypes, and return fields. It compensates for the lack of annotations by describing output. However, it could mention that the tool expects a sport name and optionally a league name for context, but that is already in the schema. Overall, it provides sufficient context for the 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?

Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond the schema: it lists the archetypes but does not elaborate on parameter values. The examples in the description are for archetypes, not for parameters, so the description does not enhance parameter understanding beyond what the schema already provides.

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 explicitly states the tool's purpose: 'Classify a sport into one of 5 canonical archetypes.' It lists the archetypes with examples, making the function clear. The name matches the description, and it is distinct from sibling tools, which focus on leagues, readiness, and other 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?

The description implies usage for classifying sports into archetypes, but does not specify when to use this tool versus alternatives. It mentions that each archetype has distinct edge cases and market structures, but lacks explicit guidance on when not to use it or which other tools might be more appropriate. However, the sibling tools are sufficiently different, so this is a minor gap.

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 full burden. It describes the return format in detail but does not explicitly state it is read-only or disclose any behavioral traits like rate limits or permissions. 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?

Very concise: two short paragraphs covering functionality and usage. Every sentence adds value. No 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?

Given the simple schema and presence of output schema, the description is comprehensive. Lists all output fields and gives usage guidance. Could mention behavior for fewer than 2 or more than 10 league IDs, but schema already provides the recommended range.

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 100% with one parameter well-described. The description adds no extra semantics beyond what is already in the input schema; it restates the purpose. Baseline 3 applies.

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 verb 'compare' and resource 'leagues', lists all output fields, and distinguishes from siblings like compare_league_trajectories and assess_league_readiness.

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 states use cases: evaluating multiple partnership candidates or benchmarking leagues within a sport vertical. No explicit when-not-to-use or alternatives, but context is clear enough.

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 must convey behavior. It describes return format: side-by-side histories, trends, total change, growth trajectories. It does not explicitly state read-only nature, auth requirements, or data recency, but the description sufficiently indicates a non-destructive comparison 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?

Four sentences succinctly convey purpose, output, and usage context. No redundant or wasteful phrasing; every sentence adds value. Front-loaded with main function.

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 adequately covers purpose, output highlights, and usage suggestions. Could mention any prerequisites or data range limitation, but overall sufficient for a comparison 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?

Schema covers both parameters (league_id_1 and league_id_2) with descriptions mentioning UUIDs, achieving 100% coverage. Description does not add extra parameter detail beyond schema, so baseline score of 3 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?

Description states specific verb+resource: 'Head-to-head valuation trajectory comparison between two leagues.' It clearly distinguishes from sibling 'compare_leagues' by specifying 'valuation trajectory' and 'side-by-side score histories', making the tool's unique purpose clear.

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 use cases: 'Use this when a user asks "how does League A compare to League B over time?" or wants to evaluate two partnership candidates.' This clearly indicates when to use, though it does not explicitly exclude alternative scenarios.

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 full burden for behavioral disclosure. It does not state whether the tool is read-only, destructive, or has side effects. It mentions analyzing and returning outputs but lacks details on authentication, rate limits, or data persistence.

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 paragraphs. The first sentence clearly states the purpose, followed by a list of anomaly patterns and return items. It could be slightly tighter by removing redundancy, but it is well-structured 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?

Given an output schema exists and schema covers parameters, the description explains the tool's purpose and return values adequately. However, it lacks usage guidelines and behavioral transparency, leaving gaps for an agent to fully understand when and how to invoke it safely.

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%, so the schema adequately documents param purposes. The description adds no extra meaning beyond the schema for the parameters. It mentions 'Analyzes a sport type' and 'Optional URL of the league's data API', which aligns with schema descriptions but does not enhance them.

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 'Detect potential trading anomalies and integrity risks for a sport' with a clear verb and resource. It lists specific anomaly patterns, making the purpose specific. However, it does not explicitly differentiate from sibling tools, which all have distinct but related functions.

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 provided on when to use this tool versus alternatives (e.g., assess_league_readiness or evaluate_league). The description does not mention when not to use it or any prerequisites. The use case is implied but not explicitly 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?

Discloses that it returns a ranked list with summary profiles, implying read-only behavior. No mention of required permissions, rate limits, or potential side effects. With no annotations, the description carries the burden, and it meets minimum expectations but lacks depth.

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 sentences, front-loaded with the main purpose. Examples are helpful but slightly increase length. Overall efficient without redundancies.

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 an output schema (not shown) and description stating 'ranked list with summary profiles,' return value is adequately described. However, given many sibling tools, more context on how this differs from search_similar_leagues or query_leagues 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?

Schema coverage is 100%, so baseline is 3. Description adds examples for the query parameter and mentions filter types (sport, region, min_fans), which provides some additional context but does not detail each filter field or their formats.

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 it searches for alternative sports leagues using AI-powered discovery, with specific verb and resource. Distinguishes from siblings like query_leagues or search_similar_leagues by emphasizing natural language and alternative focus, but could be more explicit about uniqueness.

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 examples of natural language queries and mentions optional filters, which gives basic usage context. However, no explicit guidance on when to use this tool versus siblings like list_discovered_leagues or search_similar_leagues, and no conditions for 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?

No annotations provided, so description carries full burden. Discloses that it runs a full valuation model with weighted components and allows custom criteria overrides. Doesn't mention side effects, but given 'evaluate' likely read-only, this is sufficient.

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 clear paragraphs: first sentence states main purpose, second details model components and output, third mentions optional override. No wasted words, front-loaded with key 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?

Output schema exists, covering return values. Description fully explains the valuation model, scoring dimensions, and output format (score and tier). Complete for a complex evaluation 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?

Input schema has 100% coverage, so baseline 3. Description adds meaning for 'criteria' by specifying it can be custom evaluation criteria or weight overrides, which adds value beyond the 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?

Description states 'Evaluate a league's value across multiple dimensions and assign a tier' with specific verb and resource. Distinguishes from sibling 'assess_league_readiness' by focusing on comprehensive valuation rather than just readiness.

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?

Implies usage through description of what it does, but does not explicitly state when to use this over alternatives like 'assess_league_readiness' or 'classify_sport_archetype'. No when-not-to-use 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 provided, so description carries full burden. Fails to disclose whether this is a read or write operation, what happens if a fingerprint exists and force=false (idempotent vs. error), or side effects (e.g., database updates). This ambiguity limits 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?

Four sentences without wasted words. First sentence states purpose, second lists components, third explains purpose (similarity), fourth gives usage hint. Could be slightly more structured, but 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?

Explanation of what the fingerprint captures and its purpose is adequate. Missing prerequisites (e.g., league existence), error conditions, and idempotency behavior. Output schema exists, so return format is covered, but behavioral gaps lower 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?

Schema coverage is 100%, so baseline is 3. Description adds context for 'force' parameter ('even if a fingerprint already exists') beyond the schema's description, but adds nothing for 'league_id'. Minimal added meaning overall.

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 generates a unique fingerprint profile for a league, listing specific characteristics (sport archetype, data coverage, market positioning, etc.). It distinguishes from siblings like get_fingerprint (retrieve) and compare_leagues (compare) by focusing on creation and regeneration.

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 guidance on the force parameter ('Set force=True to regenerate even if a fingerprint already exists'), clarifying when to use it. However, does not explicitly state when to use this tool versus get_fingerprint or other alternatives, leaving some implied usage.

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 full burden. It explains the report components, dimension weights, and output categories. It does not disclose side effects, rate limits, or prerequisites, but for a read-only report generator this is reasonable.

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 paragraphs, concise but packed with useful detail. It front-loads the purpose and then lists dimensions and outputs effectively. Every sentence adds 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?

Given the presence of an output schema (implied), the description adequately covers return values. It does not mention prerequisites (e.g., need for prior API valuation), but otherwise provides a solid overview of what the tool produces.

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 100% with a single parameter 'league_id' described as 'UUID of the league.' The tool description adds no extra semantics beyond the schema, so baseline score of 3 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 'Generate a full readiness report for a league' and lists specific outputs such as composite score, per-dimension breakdown, ASD Certificate Tier, classification, and recommendations. It distinguishes itself from sibling tools like assess_league_readiness by emphasizing comprehensiveness.

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 (e.g., assess_league_readiness, get_league_quarterly_report). The description implies it is for a comprehensive report, but does not state exclusions or preferred contexts.

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?

Without annotations, the description carries the full burden. It discloses the analysis scope ('every profile field'), classification categories (scraped/verified/unknown), and return format (per-field breakdown, value previews, aggregate percentage). It does not mention side effects, but the tool is clearly read-only.

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 sentences long, front-loaded with the primary action, and adds necessary details without superfluous text. 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?

Given the presence of an output schema (not shown) and the detailed description of the return structure, the description is sufficiently complete for an agent to understand what the tool does and what it returns.

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% (league_id with 'UUID of the league'), so the baseline is 3. The tool description adds no additional parameter semantics beyond the 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 clearly states the tool's purpose: 'Get a detailed scraped-vs-verified field map for a league.' It explains what the tool does—analyzing profile fields for population status and data origin—and distinguishes itself from sibling tools by focusing on per-field data coverage.

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 context for when to use the tool ('understanding data confidence and identifying what still needs verification'), but does not explicitly contrast with sibling tools or mention 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?

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It does not mention whether the tool is read-only, idempotent, expensive, or rate-limited. The return fields are listed, but behavioral aspects like data freshness or caching are absent.

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 consists of two sentences, front-loaded with the purpose. Every word serves a function with no redundancy or wasted text.

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 an output schema exists, the description need not detail return values, but it still provides a useful summary of what is returned. However, it lacks context on data freshness, scope (e.g., time range), or whether results are cached.

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, so schema description coverage is 100%. Per the guidelines, the baseline is 4. The description adds no parameter info because none exist.

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 aggregate statistics about the discovery pipeline and lists specific metrics returned (total leagues, breakdowns, velocity, coverage). It is specific about the resource and action, though it does not explicitly differentiate from sibling tools like get_pipeline_overview or get_data_coverage.

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 when-to-use or when-not-to-use guidance is provided. The description only notes 'No parameters needed', implying it is a simple call, but does not clarify when to choose this tool over alternatives such as get_pipeline_overview or get_trending_discoveries.

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 discloses error condition (if no fingerprint generated) and return contents (archetype, scores, tags, metadata). Does not explicitly state read-only nature, but it's implied for a retrieval tool.

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 no wasted words. Purpose is front-loaded, and additional details are efficiently packed.

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 output schema exists, the description adequately covers key return elements and error condition. The tool complexity is low (single parameter, read operation), and the description meets all needs.

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 100% for the single parameter league_id. The description does not add semantics beyond the schema's description of 'UUID of the league', so baseline score applies.

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 it retrieves an existing fingerprint profile for a league, specifies return elements (archetype classification, dimensional scores, etc.), and distinguishes from sibling tools like generate_fingerprint which creates the fingerprint.

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 advises using generate_fingerprint first if no fingerprint exists, providing clear context. Does not explicitly exclude other siblings, but the error condition serves as usage 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 must disclose behavioral traits. It states the tool 'probes the league's API,' implying external calls, but does not mention authentication requirements, potential latency, rate limits, or that it is read-only. The outputs are described but behavioral context is lacking.

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 efficiently structured: first sentence states purpose, second clarifies the mechanism and outputs. It is clear but slightly verbose; could be trimmed slightly without losing meaning.

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 complexity (probing and mapping), the description covers the core outputs and purpose. An output schema exists, so return values are sufficiently explained. However, it omits prerequisites like API accessibility and potential errors.

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 100% (both parameters have descriptions), so baseline is 3. The description adds context that api_url is the endpoint and league_name is optional context, but does not provide additional specifics beyond the 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 clearly states the tool checks alignment with ASD canonical kb_catalog schema and lists specific outputs (coverage score, matched/missing schemas, data gaps, complexity estimate). It distinguishes from sibling tools like get_data_coverage by focusing on schema alignment rather than general data coverage.

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 assessing API alignment but does not explicitly state when to use this tool versus alternatives (e.g., get_data_coverage or assess_league_readiness). No exclusions or prerequisites 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 provided, so description carries full burden. It explains what data is returned and that it 'computes' analysis, but does not disclose whether it is read-only, has side effects, or any authentication requirements.

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 front-loaded with a clear one-liner and uses bullets for detail. It is concise relative to the information conveyed, though slightly verbose due to enumeration of data points.

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 existence of an output schema and numerous sibling tools, the description provides a thorough list of included data and computes a specific analysis, making it complete for a comprehensive 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?

The input schema has one parameter (league_id) with 100% description coverage, so baseline is 3. The tool description explains the tool's purpose but adds no additional semantic detail about the parameter.

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 'Assemble the complete intelligence context for a league' and identifies itself as the 'primary tell me everything tool', effectively differentiating from more specific sibling 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 positions this as the primary comprehensive tool, implying it should be used for broad context retrieval. However, it does not explicitly mention when to use alternatives or when not to use this tool.

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 the return metadata fields and examples, but lacks details on pagination, sorting, permissions, or error handling. 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?

Three sentences: first states purpose, second provides examples, third lists metadata fields. No extraneous words, front-loads key information. Highly 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?

With an output schema existing, the description does not need to detail return types. It explains what documents are returned and what fields are included. For a simple list tool with one parameter, this is 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 only parameter, league_id, is fully described in the input schema (100% coverage). The description adds no additional meaning beyond the schema, so baseline score of 3 applies.

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 documents for a league, with a specific verb and resource. It lists examples of document types, distinguishing it from other get_* tools that focus on different entities.

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 clear context that this tool is for getting league documents, but does not explicitly state when not to use it or mention alternatives. However, the sibling list shows no other tool serves the same purpose, making usage implicit.

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 full burden. It describes the returned data but does not disclose behavioral traits like side effects, rate limits, or authentication requirements. For a read-only aggregate tool, this is minimally acceptable but lacks 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 five concise sentences, front-loading the core purpose, then listing returned data, optional filter, and usage advice. No extraneous information, every sentence adds 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?

The tool is simple with one optional parameter and an output schema. The description covers what it returns and when to use it. It does not mention its read-only nature, but this is a minor gap given the tool's simplicity.

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 100% for the single optional parameter, so the schema already describes it. The description adds a usage phrase ('drill into a specific sport archetype vertical') but no additional parameter details beyond the schema. Score at baseline.

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 an aggregate league market index, listing specific returned elements (total count, averages, top movers/decliners) and optional filtering. It distinguishes itself from siblings by emphasizing a 'birds-eye view' of the marketplace.

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 advises when to use: for a birds-eye view of the entire marketplace or to drill into a specific sport vertical. While it doesn't name alternative tools, the context implies that more detailed tools exist for individual league analysis.

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 for behavioral disclosure. It implies read-only behavior and current data but does not detail data freshness, caching, or any potential side effects. Some additional context would be beneficial.

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?

Four concise sentences without redundancy. The description is efficiently structured: main action, return details, and usage guidance. 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?

Given the tool's simplicity (no parameters, no nested objects) and the existence of an output schema, the description fully covers what the agent needs to know. It specifies output fields and purpose, making it 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 input schema has no parameters, so the description adds value by explaining the output structure and fields. This meets the baseline expectation for zero-parameter tools and provides meaningful context beyond the 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 clearly states 'Retrieve the top movers and decliners across all leagues,' specifying a concrete verb and resource. It differentiates from sibling tools by focusing on momentum and includes details about the returned fields, making the purpose unambiguous.

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 advises 'Use this to find which leagues are trending up or down right now,' providing clear usage context. However, it does not explicitly mention when not to use this tool or contrast with siblings like compare_league_trajectories, 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 bears full responsibility for behavioral disclosure. It accurately describes the output but does not mention any potential side effects, permissions, or rate limits. Since the tool is read-only, the lack of additional disclosure is acceptable but not exemplary.

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 the primary purpose, and efficiently lists the comprehensive set of returned data without redundancy. Every sentence serves a clear purpose.

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 (not shown but indicated), the description appropriately does not repeat return value details. It fully covers the tool's purpose, parameter behavior, and scope. For a simple retrieval tool, this is 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 100%, and the description adds value by explaining that the quarter parameter can be omitted to return the latest report, supplementing the schema's default null description. This clarifies the optional behavior effectively.

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 an SEC 10-Q style quarterly report for a league, specifying the verb 'retrieve' and the resource 'league quarterly report'. It distinguishes from siblings by detailing the specific financial and analytical contents unique to this report.

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 clear guidance on parameter usage by stating that omitting the quarter parameter returns the latest report. While it does not explicitly contrast with alternatives, the tool's specialized function makes its context clear.

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 discloses that the tool returns a time series with composite scores, per-dimension breakdowns, change, trajectory, and confidence. This is sufficient transparency for a read-only data retrieval tool, though it omits details like authentication or rate limits.

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 paragraphs, which is somewhat lengthy but well-structured. It introduces the tool, details the dimensions, and provides a usage line. A minor redundancy exists (composite score mentioned twice), but overall it is 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 an output schema, the description does not need to detail return values. It adequately explains the concepts (time series, dimensions, change, trajectory, confidence). However, it could mention data freshness or update frequency for full 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?

Schema coverage is 100%, so baseline is 3. The description does not add new meaning beyond the schema; it repeats league_id and limit. The additional detail about 'most recent first' is already in the schema's limit description.

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 historical valuation snapshots for a league, using a stock price chart analogy. It lists the 6 weighted dimensions and what each snapshot includes, distinguishing it from siblings like 'get_league_momentum' or 'compare_league_trajectories'.

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 includes a usage statement ('Use this to understand how a league's value has changed over time') but does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare to alternative tools among the siblings.

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 full burden. It indicates a read operation with no side effects. The description is clear and sufficient 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?

Two concise sentences with no fluff. The main action is front-loaded, and every word is meaningful.

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 no parameters and a presumably simple output schema, the description covers the core functionality. Could mention update frequency or data freshness, but not essential.

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?

With zero parameters, schema coverage is 100%. Description adds value by explicitly stating 'No parameters needed,' reinforcing the schema's emptiness.

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 identifies market opportunities and specifies outputs (underserved sport types, high-demand regions, gaps). It distinguishes from siblings which focus on specific leagues or rankings.

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 use for high-level market analysis but lacks explicit when-to-use or when-not-to-use guidance. Sibling context provides some differentiation, but the description itself does not.

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 full burden. It discloses the returned data (stage, time-in-stage, completed steps, blocking items, next actions), indicating a read-only operation. The verb 'Get' also implies no side effects. This is sufficient for a simple retrieval tool.

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 short, well-structured sentences. First sentence states purpose, second lists return fields. No unnecessary words 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?

Given the tool has only one parameter and an output schema (inferred from context), the description fully explains what the tool does and what it returns. It covers the key aspects of the output without needing to describe return format since output schema exists.

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 documentation coverage is 100% (league_id described as 'UUID of the league'). The description adds no additional information about the parameter beyond the schema. Baseline of 3 is appropriate since the schema already adequately explains it.

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 goal: get onboarding pipeline progress for a specific league. The description distinguishes it from sibling tools like get_onboarding_stats or get_pipeline_overview by specifying the exact return fields (stage, time-in-stage, steps, etc.).

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?

Implied usage: use this tool to retrieve details about a league's onboarding progress. However, it does not explicitly state when to prefer this over alternatives (e.g., get_pipeline_overview) or when not to use it. No exclusions or prerequisites 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?

With no annotations, the description provides transparency by listing specific statistics returned and explicitly stating 'No parameters needed.' It does not discuss potential side effects, but as a read-only stats tool, this is sufficient.

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 (two sentences), front-loaded with the purpose, and every sentence adds 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?

Given the presence of an output schema and zero parameters, the description adequately covers what the tool does. It lists the return types but could be more complete by noting data freshness or time range.

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 schema coverage is 100%. The description adds value by explicitly stating 'No parameters needed,' confirming the parameter-free nature.

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 it gets aggregate statistics about the onboarding pipeline and lists specific outputs. However, it does not differentiate from many sibling tools with similar names (e.g., get_discovery_stats, get_pipeline_overview).

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. The description only states what it returns, with no when/when-not context.

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, and the description does not disclose any behavioral traits such as read-only nature, permissions required, or side effects. While the verb 'get' implies a safe operation, explicit transparency is missing.

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, consisting of two short sentences. It front-loads the main purpose and efficiently covers the key information without unnecessary detail.

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 sufficiently explains what the tool returns (stage-by-stage breakdown, counts, average time, bottlenecks, recently progressed). There is no missing context for an agent to invoke 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?

There are no parameters, and the description correctly states 'No parameters needed.' With 100% schema coverage and no params to document, the description adds value by confirming the absence of required inputs.

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: to get an overview of all leagues in the onboarding pipeline. It specifies the returned breakdowns, which distinguishes it from other sibling tools that focus on individual league analysis or 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?

The description explicitly mentions that no parameters are needed, which is a usage guideline. However, it does not provide guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or 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?

No annotations are provided, so the description carries full burden. It accurately describes the tool as a read operation returning a decision matrix with specific components. However, it does not mention any potential side effects or authentication needs, though for a simple get tool, these are minimal. The description is clear but could be slightly more explicit about 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?

The description is two short paragraphs, front-loaded with the main purpose, and each sentence adds necessary information. No redundant or vague phrasing. It is as concise as possible while being informative.

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 that a returntype exists (output schema), the description does not need to detail the return format. It sufficiently describes the matrix contents and covers the essential information for a simple retrieval tool. No gaps are apparent.

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?

With no parameters and 100% schema coverage, the description adds value by explaining what the output contains (dimension thresholds, weighting logic, tier boundaries). This goes beyond the empty schema and helps the agent understand the tool's purpose 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 tool returns the qualification decision matrix for league assessment, listing specific outputs (fast-track, invest, monitor, pass) and contents (thresholds, weighting, tier boundaries). This verb+resource statement is specific and distinguishes it from sibling tools like get_tier_definitions.

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 does not explicitly state when to use this tool versus alternatives like assess_league_readiness or get_tier_definitions. While it implies retrieval of the full matrix, it lacks context on prerequisites or when not to use it. The mention of 'no parameters needed' is helpful but insufficient.

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?

Discloses return format (states, section percentages, confidence tiers) and no side effects mentioned. No annotations, so description carries burden; could explicitly state non-destructive nature.

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, no fluff, front-loaded with purpose. Every sentence adds 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?

Complete given single required param and presence of output schema; description covers return types and granularity.

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?

Only one parameter with 100% schema coverage; description adds no additional meaning beyond schema's UUID specification.

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?

Description clearly states the tool retrieves processing status of a questionnaire with specific states and section-level details, distinguishing it from sibling tools that deal with other league data.

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?

Implied usage context: when needing status of a specific questionnaire. No explicit alternatives or exclusions, but purpose is narrow and well-defined.

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 fully describes the tool's behavior: it returns static definitions without side effects. It adds detail on the returned data elements. While it could mention idempotency or read-only nature, the context of 'get' and 'definitions' implies 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?

The description is two sentences long, with the first sentence stating the purpose and the second elaborating on outputs and noting no parameters. Every sentence adds value, and the key 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?

Given the tool has no parameters and returns tier definitions, the description covers the purpose, output details, and usage note. It is sufficiently complete for an agent to decide when and how to use it, even without seeing the output schema.

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 empty. The description adds meaning by explaining what the tool returns, which goes beyond the schema. There is no need for parameter 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 clearly states 'Get the tier classification system definitions and criteria' and lists specific outputs (tier levels, score thresholds, contract value ranges, pricing flavors, qualifying criteria). It distinguishes itself from more than 30 sibling tools that focus on leagues, fingerprints, and other entities.

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 notes 'No parameters needed,' making the usage context clear. However, it does not provide explicit when-not-to-use guidance or alternative tool comparisons. Given the simplicity of the tool, this is adequate.

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 full burden. It states it returns three types of data and requires no parameters, but omits behavioral details like data freshness, performance expectations, or whether it is a read-only 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?

Three succinct sentences that front-load the purpose, list return types, and state lack of parameters. No unnecessary 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?

Given zero parameters, a simple purpose, and the presence of an output schema, the description provides all necessary context. It explains what the tool returns without needing to detail output structure due to the output schema.

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 with 100% schema coverage, so baseline is 4. The description correctly notes 'No parameters needed' which adds clarity but does not need to elaborate.

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 trending sports and regions for new league discoveries, listing specific output types. It is distinct from sibling tools like discover_leagues which actually finds leagues, and get_market_opportunities which focuses on opportunities.

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 getting trending data and notes no parameters required, but does not explicitly state when to use this tool over alternatives among the many sibling tools.

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 describes the tool as analyzing for known edge cases and returning severity, frequency, affected markets, and mitigations, indicating a read-only analytical operation. It does not mention auth, rate limits, or state changes, but the description is sufficient for a safe analytical tool.

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 relatively concise with two paragraphs, front-loading the purpose. It could be slightly more compact, but every sentence adds value by clarifying examples and output details. No 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?

Given that there is an output schema (true) and the schema covers 100% of parameters, the description completes the picture by mentioning the types of edge cases and output attributes. It is adequate for a tool of this complexity.

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 already covers both parameters (sport_type and market_preferences) with descriptions. The description adds value by providing concrete examples for market_preferences ('H2H, props, futures, etc.') and outlining the output attributes (severity, frequency) that relate to the tool's functionality.

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: 'Identify sport-specific edge cases that affect betting market integrity.' It provides specific examples like race delays, weather disruptions, and disqualifications, making the scope unambiguous. This distinguishes it from siblings like 'detect_trading_anomalies' 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?

The description implies usage for analyzing a sport type for edge cases and optionally filtering by market types. However, it does not provide explicit guidance on when to use this tool over alternatives (e.g., 'detect_trading_anomalies' or 'research_league'). There are no when-not-to-use or exclusion statements.

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 implies read-only behavior and explicitly describes paginated output and ordering. Could explicitly state 'read-only' but adequately communicates 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 concise sentences: purpose, output behavior, usage guidance. No fluff, well-structured.

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 output schema present, description covers purpose, usage, and pagination behavior. Complete for a listing 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?

Schema has 100% coverage with descriptions and defaults. Description reiterates pagination but adds no extra meaning beyond schema. Baseline 3 applies.

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 tool lists previously discovered leagues, with explicit mention of browsing before new searches. Distinguishes from sibling tools like discover_leagues.

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 usage context: 'Use this to browse what has already been found before running new searches.' Clearly guides when to use vs. when not to use.

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 exist, so the description carries full burden. It mentions sorting by most recently updated but lacks disclosure on destructive actions, auth needs, rate limits, or pagination.

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 brief intro and a bullet list of filters. Every sentence contributes, though it could be slightly more streamlined.

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 output schema existence and full parameter coverage, the description explains return behavior (summaries sorted by update) and all filters, making it complete for this query 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?

With 100% schema coverage, baseline is 3. The description adds value by listing filter options, providing examples (e.g., tier_range '1.0-2.9'), and clarifying search scope (league name and sport type).

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 'query' and resource 'leagues' with filtering, distinguishing it from sibling tools like 'discover_leagues' or 'search_similar_leagues' by emphasizing broad, flexible search across all leagues.

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 general search and filtering but does not explicitly specify when to use this tool versus alternatives, nor does it provide conditions for 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?

No annotations are provided, so the description must bear full weight. It only states the tool's function and output, without disclosing side effects, permission requirements, or whether it is read-only. This is insufficient for behavioral 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 extremely concise—three sentences that are front-loaded with the primary action and output. 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?

The description explains the tool's purpose and outputs, but given the complexity (4 required parameters, many sibling tools) and lack of annotations, it lacks contextual details such as prerequisites, edge cases, or when to prefer this tool over similar ones. The presence of an output schema mitigates the need to explain return values.

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 covers 100% of parameters with descriptions. The tool description adds minimal semantic value beyond the schema, merely referring to 'key league metrics'. A score of 3 is appropriate given the 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 states it ranks a league's maturity and production readiness, listing specific output metrics (score, grade, etc.). However, it does not explicitly differentiate from sibling tools like 'assess_league_readiness' or 'evaluate_league', which may have overlapping 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 includes a usage hint ('Useful for prioritizing which leagues to onboard first'), but lacks explicit guidance on when to use this tool versus alternatives, and does not provide when-not 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?

Describes the AI research pipeline (web scraping, social media analysis, etc.) and output, but does not mention side effects, permissions, or caveats. No annotations provided.

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 well-structured paragraphs with front-loaded purpose. Every sentence adds value; no 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?

Given the output schema exists and the complexity is moderate, the description fully covers the tool's behavior and pipeline.

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 100% and descriptions are adequate; the tool description adds little beyond what the schema already provides.

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 it 'deep-research a specific league' and enumerates the pipeline components, distinguishing it from siblings like 'discover_leagues' or 'get_league_context'.

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?

Implies usage for in-depth research but does not explicitly state when to use versus alternatives or when not to use.

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 of behavioral disclosure. It only mentions that the tool returns a score with recommendations, but does not discuss permissions, side effects, rate limits, or any other behavioral traits. This is insufficient for an agent to fully understand the tool's 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 extremely concise, with two short paragraphs that front-load the purpose. Every sentence adds value, and there is no redundancy or unnecessary detail.

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 (which should document return values), the description sufficiently covers inputs, evaluation factors, and output nature. It could include more detail on scoring range or recommendation format, but it is largely complete for the tool's purpose.

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 100%, providing basic descriptions for parameters. The description adds value by explaining the factors considered (data quality, market demand, etc.) and the nature of the output (multi-factor score with recommendations), which go beyond the schema's simple parameter descriptions.

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: to score a partnership opportunity with a league, evaluating multiple factors and returning a score with recommendations. It distinguishes from siblings by focusing specifically on partnership scoring, which is unique among the listed sibling 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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. Given the many sibling tools, this lack of usage context is a significant gap.

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 explains the tool uses the league's fingerprint and searches across multiple dimensions, which is transparent. It does not mention auth requirements or rate limits, but for a read-only tool, 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?

The description is concise (three sentences) with the purpose stated first, followed by mechanism and use cases. 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?

Given the tool has two well-documented parameters and an output schema, the description covers what is needed: purpose, mechanism, and use cases. No gaps remain.

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?

Both parameters are fully described in the schema (100% coverage). The description adds context about using fingerprints but does not add specific parameter-level meaning beyond what the schema provides. Baseline 3 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 it finds leagues with similar fingerprint profiles, using specific dimensions (archetype, operational maturity, market positioning, data characteristics). This distinguishes it from siblings like compare_leagues or compare_league_trajectories, 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 lists use cases (benchmarking, competitive analysis, finding comparable partnership candidates), providing clear context. However, it lacks explicit guidance on when not to use this tool versus alternatives, missing slightly on exclusions.

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