HSH Data-on-Demand

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

The description adds behavioral context like SMTP validation, low bounce rate, and pricing tiers. However, it lacks information about data freshness, accuracy guarantees beyond bounce rate, or any potential limitations. No annotations are provided, so the description carries full burden.

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 (4 sentences) with front-loaded main offering. Every sentence adds value: what you get, quality, filters, pricing. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description covers inputs, output type (name, email, company), quality, and pricing. Lacks details on return format, pagination, or error handling, but is fairly complete for a data purchase 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 coverage is 100% with parameter descriptions. The description adds context by mentioning 'Industry/title filters' corresponding to role/industry parameters and pricing tiers for the quantity parameter, providing additional 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?

The description clearly states it provides verified B2B contact records with name, business email, and company, and mentions filters and pricing. However, it does not differentiate from sibling tools like hsh-b2b-enriched and hsh-b2b-full.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. alternatives such as hsh-b2b-enriched or hsh-b2b-full. No when-to-use or when-not-to-use information.

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

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

The description mentions cross-referencing from 3+ sources for accuracy, which is positive, but lacks disclosure of limitations, destructive actions, or behavior under edge cases (e.g., no results, rate limits). No annotations are present to compensate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two sentences covering core value and accuracy. The pricing tiers add context but could be moved to a separate annotation. Overall, minimal waste and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and 5 parameters, the description lacks information on return format, error handling, pagination, or scaling behavior. The pricing tiers hint at quantity limits but are insufficient for a complete understanding of tool behavior.

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 only 40% schema description coverage, the description should clarify parameter use, but it only lists general fields (name, email, etc.) without mapping to input schema properties like role, industry, or quantity. The meaning and constraints of each parameter remain unclear.

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 provides enriched B2B records with fields like name, email, LinkedIn URL, and cross-referencing for accuracy. However, it does not differentiate from sibling tools such as hsh-b2b-contact or hsh-b2b-full, leaving the agent uncertain about when to use this specific variant.

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. The pricing tiers do not clarify use cases (e.g., single lookup vs bulk enrichment). An agent would not know if this tool is appropriate for a given request without comparing to 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 are provided, so the description must disclose behavioral traits. It describes what data is fused (multi-source) but does not clarify whether the tool is read-only, destructive, or requires permissions. No mention of side effects, rate limits, or output format. The description leaves the agent uncertain about operational 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 relatively brief (two lines) and front-loaded with key capabilities. However, it includes pricing tiers which are tangential and could distract from pure functional clarity. Structure is adequate but not efficiently organized.

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 5 parameters (only 1 required), no output schema, and context signals indicating low schema coverage, the description should provide comprehensive context. It lists data outputs but does not explain how parameters filter results, the meaning of tiers, or return format. The description feels incomplete for effective agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 20% (only tech_stack has a description). The tool description mentions tech stack, industry, funding, and revenue in general terms but does not map clearly to the parameters (industry, funding_stage, revenue_range). It adds minimal semantic value beyond what the sparse schema provides, failing to compensate for the low 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 the tool provides deep enrichment including name, email, phone, LinkedIn, firmographic data, tech stack, funding, and revenue. It identifies the target audience (sales teams) and mentions multi-source fusion. However, it does not differentiate from siblings like hsh-b2b-contact or hsh-b2b-enriched, which could perform similar tasks with different scope.

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 comprehensive enrichment needs ('need everything'), and provides pricing tiers suggesting batch sizes. But it offers no explicit when-to-use or when-not-to-use guidance, nor does it compare with sibling tools. The context is present but not directive.

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

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

The description transparently explains the internal logic (check warm inventory first, then produce a quote) and notes the two possible outcomes. However, it omits behavioral traits like whether the tool mutates state (creates a request/quote), required authentication, error handling, or rate limits. With no annotations provided, this gap is notable.

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 wasted words. The first sentence explains the mechanism, the second states when to use. It is front-loaded and 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?

Given 4 parameters, 1 required, and no output schema, the description lacks details about the return format (quote structure, fields), error responses, or what constitutes a successful call. The agent is left guessing what a 'quote' contains. More context is needed for full autonomy.

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 covers 100% of parameters, yet the description adds value by specifying 'need' as 'plain-language description' and 'keywords' as 'match keywords (e.g. tickers, topics),' which clarifies usage beyond the schema texts. It could further explain how keywords influence matching, but still enriches understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('Route a data need through HSH') and explains the two fulfillment paths (warm vs fresh inventory), clearly stating what the tool does. However, it does not explicitly distinguish itself from specialized sibling tools like 'hsh-company-intelligence' or 'hsh-b2b-contact'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states 'Use this when you need data mid-task and want HSH to fulfill it,' which is clear context. But it does not provide when-not-to-use guidance or compare to the many sibling tools that handle specific data types, leaving ambiguity for an agent choosing between this and a specialized 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, but the tool is a simple status check, implying read-only behavior. The description adds that it's free and uses an order reference. However, it does not explicitly state that the tool does not modify any data or have side effects, which would be helpful for an AI agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that conveys all essential information: free, purpose, and parameter format. Every word earns its place; there is no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 required parameter, no output schema), the description covers the purpose and parameter well. A minor gap is the lack of information about the return value, but for a status check, an AI agent can infer a status string. Overall, it is largely complete for the context.

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 already describes the parameter 'order_ref' with a type and description, achieving 100% coverage. The tool description adds value by providing a concrete example (HSH-1A2B3C4D) and clarifying the format pattern, which helps the agent format the parameter correctly.

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 ('Check fulfillment status'), the resource ('paid order by order reference'), and provides the exact format example (HSH-XXXXXXXX). It distinguishes from sibling tools like hsh_check_quote, which check quotes, not orders.

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 says 'FREE' and gives the format for the order reference, making it clear when to use this tool (when you have a paid order reference). It could be improved by explicitly stating when not to use it (e.g., if you need a quote, use hsh_check_quote), but the purpose is sufficiently distinct from 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 are provided, so the description carries full responsibility. It discloses the tool is free and returns status, frozen price, expiry, and pay_url. It does not mention read-only nature, error handling, or rate limits, but for a simple lookup 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 a single sentence with all essential information. It front-loads 'FREE' and clearly states purpose and output, with 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?

For a simple tool with one parameter and no output schema, the description covers the main return fields and cost. It lacks details on error cases (e.g., invalid quote_ref) or updates, but overall it provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, baseline is 3. The description adds value by specifying the origin of the quote_ref (from hsh_describe_data_need), which clarifies context beyond the schema's example format.

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 'Check' and the resource 'quote_ref', specifies the source (hsh_describe_data_need), and lists the returned fields (status, frozen price, expiry, pay_url). It distinguishes from siblings by defining its specific role in the workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context: it checks a quote_ref obtained from hsh_describe_data_need. It mentions 'FREE', hinting at cost implications. However, it does not explicitly exclude use cases or provide alternatives among 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; description only mentions 'FREE' but lacks details on authentication, rate limits, or side effects. Insufficient for a mutation-free 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?

Single sentence, front-loaded with 'FREE', no wasted words. All essential information included.

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?

Adequate for a simple tool with 1 parameter and no output schema. Clarifies input format and output content, though output structure not specified.

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 100%; description adds value by specifying sub_ref format (HSHSUB-XXXXXXXXXX) beyond schema description, aiding correct invocation.

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 action (check), resource (subscription), identifier format (sub_ref with example), and return info (watching, status, match criteria). Distinct from 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?

No guidance on when to use this tool over siblings like hsh_check_order or hsh_check_quote. Implicitly for subscription checks only.

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 pay-per-call via x402 (USDC on Base) and 'real-time' data, but does not mention rate limits, data freshness guarantees, or any destructive behavior. The payment detail is useful but incomplete.

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 cover the tool's purpose, returns, modes, and payment. No fluff, but the structure could be improved by separating functionality from payment into distinct paragraphs.

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 4 parameters, no output schema, and no annotations, the description covers core functionality and modes. However, it lacks details on the discover query syntax, return format, error behavior, and pagination (if any). This is adequate but not thorough.

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 50% (empty for mode and limit). The description adds meaning by explaining the 'mode' enum (lookup vs discover) and that 'query' can be a company name/domain or a discovery query. It also describes the 'filters' object content for discover mode. This compensates well for schema gaps.

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 provides 'real-time intelligence on a startup/company by name or domain' and lists returns (profile, founders, tech stack, hiring signals, YC batch). It distinguishes between lookup and discover modes, and from sibling tools focused on contacts or scraping. However, 'intelligence' is slightly vague.

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 explains two modes (lookup for one company, discover for filtered list), providing some guidance on when to use each. But it offers no explicit exclusions or comparisons to sibling tools like hsh-b2b-contact, leaving the agent to infer the best 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?

Discloses pay-per-call via x402, adding cost context. No annotations; description does not detail safety, side effects, or idempotency beyond the read-only nature implied by grading.

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: first states purpose and output, second notes payment. No redundancy, every part 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?

Covers purpose, output categories (Easy to Very Hard), and cost. No output schema, but description compensates. Could add example or output type, but sufficient for a simple single-parameter 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 coverage is 100% with 'target' described as 'The target to be chased.' Description adds no extra meaning beyond schema, baseline 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 grades chase difficulty (Easy to Very Hard) at innings break from historical data. Distinguishes from sibling like win-probability tools via specific verb and resource.

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 at innings break for target evaluation but lacks explicit when-not-to-use or comparison to alternatives like chase-winprob.

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 reveals the model's performance (AUC 0.88, beats heuristic by 8.6pts), update frequency (per ball), and cost model (pay per call via x402). It does not mention whether it uses live or simulated data, but sufficient for most use cases.

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, front-loaded with the core purpose, and includes relevant performance metrics and use case. No redundant words; 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?

The tool has no output schema, so the description should explain the return value. It does not mention the output format (e.g., probability as a decimal between 0 and 1) or any additional response fields. This omission makes it incomplete for an agent to process the result.

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 covers 100% of parameters with descriptions, so baseline is 3. The description adds no additional parameter-level meaning 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 the tool provides 'Real-time chase win-probability for a T20 run chase', specifying the sport (T20), context (run chase), and metric (win-probability). It distinguishes itself from siblings like 'hsh-cricket-chase-difficulty' by focusing on probability rather than difficulty.

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 mentions 'In-play betting signal' and 'Pay per call via x402', implying usage for betting and cost awareness. However, it does not explicitly state when to use this over alternatives (e.g., chase-difficulty) or provide 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?

No annotations are provided, so the description carries the full burden. It discloses the pay-per-call model (x402), claims a captain hit rate (35.6%, 2x random), and indicates predictive ranking. While it does not explicitly state non-destructiveness, the read-only nature is implied. The performance metric adds useful behavioral insight beyond basic functionality.

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, front-loading the core purpose ('Fantasy cricket picks + captain') and immediately adding key details (format, performance claim, pricing model). Every clause earns its place with 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 tool has 3 parameters, no output schema, and no annotations, the description provides sufficient context: it returns ranked players and a captain pick. However, it doesn't specify the exact output structure (e.g., list of objects with rank, name, score) or whether the captain is included in the ranking. Still, for a fantasy picks tool, it covers the essential behavioral expectations.

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 adds context by explaining the purpose of each parameter (e.g., 'venue for pitch context', 'players list of names', 'is_chase binary'). This enhances the schema's bare definitions, especially by framing 'players' as the squad list and 'is_chase' as a binary indicator.

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 provides fantasy cricket picks and optimal captain for a T20 match, ranking players by predicted fantasy value. It distinguishes itself from sibling cricket tools (e.g., form, matchup, chase probability) by focusing on fantasy-specific output (captain nomination, Dream11-style).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states the tool is for T20 fantasy picks but does not explicitly specify when not to use it or compare to alternatives. However, the context (sibling tools like chase difficulty, form, etc.) implies this is for fantasy squad selection, and the 'Dream11-style' cue helps identify use cases.

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 model accuracy (AUC 0.76) and cost model ('Pay per call via x402'), but lacks details on error handling, output format, rate limits, or determinism.

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 superfluous information. Key purpose and cost are 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?

No output schema and no description of return value. Parameters are undocumented. For a complex prediction tool, this is inadequate.

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 0% description coverage, and the description does not explain the individual parameters beyond mentioning 'venue + team strength'. For a 6-parameter tool, this is insufficient.

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 computes first-innings win probability while a team is setting a total, using venue and team strength. This distinguishes it from sibling tools like chase win probability.

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 specifies the context ('while a team is still setting a total'), implying when to use. However, it does not explicitly mention when not to use or compare with alternatives like chase win probability or other cricket models.

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

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

With no annotations, the description must disclose behavioral traits. It mentions 'pay per call via x402', which is a critical cost implication. However, it does not explain side effects (none expected), error handling for unknown players, or any authentication/rate limits. The disclosure is partial but adds value beyond no annotation.

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 (two sentences) and front-loaded with essential information: the core function, specific outputs, and a notable usage condition (pay-per-call). Every word serves a purpose with 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?

For a simple tool with one parameter and no output schema, the description is moderately complete. It specifies the type of output (ratings, flags) and target players (IPL established). However, it lacks details on output format (e.g., numeric scale, boolean flags) and does not clarify what happens if the player is not found or if partial match yields multiple results. Some 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?

Schema coverage is 100% with a clear description of the 'player' parameter (partial match ok). The description adds no further semantic detail about the parameter (e.g., what constitutes a valid player, case sensitivity). Baseline of 3 is appropriate as the schema already provides sufficient meaning.

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 provides 'Player form & fatigue rating' with specific indicators (hot/cold form, bowling-workload fatigue flag) for established IPL players. It distinguishes itself from sibling cricket tools like hsh-cricket-matchup or hsh-cricket-timeline by focusing on form and fatigue, and mentions 'Fantasy signal' as a use case, clarifying its purpose.

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 other cricket tools. While 'Fantasy signal' hints at a context, it does not specify prerequisites, exclusions (e.g., when not to use), or alternatives among siblings. The description lacks actionable usage direction for an AI agent.

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

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

With no annotations provided, the description carries full burden. It discloses that this is a paid tool ('Pay per call via x402'), a behavioral trait not evident from the schema. It also notes the data is 'Bayesian-adjusted', indicating how results are computed.

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: two sentences that front-load the core purpose and key details. Every sentence adds value with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter lookup tool, the description covers the output fields (balls, runs, dismissals, dominance score) and use case. It lacks specifics on data limits or time ranges, but is largely 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% with both parameters described as 'partial match ok'. The description adds no additional meaning beyond what the schema provides, so a 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 it provides 'Batter-vs-bowler head-to-head record' with specific metrics (balls, runs, dismissals, dominance score) and mentions the dataset size and Bayesian adjustment. It distinguishes itself from sibling cricket tools by focusing on head-to-head matchups.

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 mentions 'Fantasy + in-play edge', which gives context for when to use (fantasy cricket or in-play betting). However, it does not explicitly state when to avoid this tool or provide alternatives among the many cricket 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 are provided, so the description must fully cover behavioral traits. It only mentions cost ('Pay per call via x402') but does not disclose whether the tool is read-only, requires authentication, has rate limits, or any side effects. This is insufficient for safe invocation.

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: two sentences front-load purpose and output, then add usage and cost. Every sentence earns its place with no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 params, nested objects, no output schema), the description lacks completeness. It does not explain return value format, prerequisites, or how to interpret the pressure score, leaving agents without sufficient context for correct use.

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 100% description coverage, so each parameter's purpose is clear from the schema itself. The tool description adds no additional meaning beyond what the schema already provides, meeting the baseline expectation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes momentum and pressure index for a live T20 chase, quantified as swing signal and pressure score. It distinguishes from sibling cricket tools like chase-winprob or chase-difficulty by focusing on momentum/pressure rather than probability or difficulty.

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 as an 'in-play trader signal' and mentions pay-per-call, but does not explicitly state when to use this tool versus alternatives or when not to use it. More directional guidance would improve clarity.

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 burden. It discloses accuracy (+/-8.6 runs in death overs) and cost (Pay per call via x402), which is helpful. However, it lacks details on read-only nature, required permissions, rate limits, or side effects. The behavioral context is partial.

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 at three sentences, with the purpose front-loaded. Each sentence adds value: purpose, accuracy/performance, and usage context. Minor improvement could be removing the 'Pay per call' line if not essential, 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 lack of output schema and empty parameter descriptions, the description should provide more context. It does not describe return format, prerequisites (e.g., needing mid-innings data), or limitations beyond death-overs accuracy. The description is insufficient for an agent to fully understand usage without external knowledge.

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 4 parameters with 0% description coverage. The description does not explain any parameter meanings, formats, or how they relate to the 'mid-innings state'. The parameter names are somewhat self-explanatory, but no schema descriptions or additional text clarify units, ranges, or the role of 'venue'. This is a severe gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Par-score / projected first-innings total from any mid-innings state', providing a specific verb and resource. It distinguishes from sibling cricket tools like chase-difficulty or win-probability by focusing on par score projection. No ambiguity.

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 over/under betting but does not explicitly state when to use this tool versus alternatives like chase-winprob. No when-not or alternative comparisons are provided, leaving the agent without guidance on tool selection among similar cricket 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?

The description notes the tool is 'media/broadcast-grade' and 'pay per call,' which hints at quality and cost. However, with no annotations, it fails to mention idempotency, rate limits, data freshness, or whether results are real-time. Some behavioral context is present but incomplete.

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 are used, with the first immediately stating the tool's core purpose. No redundant information. However, it could be slightly more compact by merging the second sentence into the first.

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 (producing a timeline over multiple overs) and absence of output schema, the description does not specify the output format, how many overs are included, or whether the data is historical/live. The 'broadcast worm' hint is insufficient for full understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%, with the 'target' parameter having an empty description. The tool description adds no additional meaning to the parameters beyond what the schema already provides. It should clarify the format of 'events' or constraints on 'target'.

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 provides an over-by-over win-probability timeline for a T20 chase, using the familiar 'broadcast worm' analogy. This specific verb-resource combination distinguishes it from sibling tools like hsh-cricket-chase-winprob.

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. It mentions 'media/broadcast-grade' and 'pay per call' but doesn't give selection criteria or exclusion conditions relative to other cricket 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 are provided, so the description carries the full burden. It discloses the three data layers, payment via x402 (USDC on Base), and the per-call cost model. It does not detail error handling or rate limits, but the behavioral description is substantial.

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 the main purpose and uses a numbered list for the three layers, making it well-structured. It is slightly long but 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 complexity of the tool (three layers) and the absence of an output schema, the description provides a good overview of the output (signal types, whale transactions, risk score). It lacks details on data freshness or pagination, but is sufficiently complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema's parameter descriptions; it restates that asset defaults to BTC, which is already in the schema. No additional semantic value is provided.

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 provides 'complete crypto intel for trading agents' with three specific layers (positioning, on-chain whale activity, liquidation risk). It uses a specific verb ('complete crypto intel') and resource, and the sibling tools are all non-crypto, making distinction easy.

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 targets 'trading agents' and lists supported assets, providing clear context for usage. However, it does not explicitly state when not to use this tool or suggest alternatives, though none are present among 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 discloses setup fee, per-record cost, and included services, but does not mention rate limits, auth, or potential side effects. Moderate disclosure.

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?

Description is reasonably concise, starting with core purpose then pricing. Minor redundancy (e.g., 'novel datasets that don't exist anywhere yet' is slightly verbose) but overall 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?

Without output schema, annotations, or detailed parameter help, the description covers pricing and tiering but lacks details on output format, error handling, or prerequisites. Could be more complete for a custom dataset creation 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 description coverage is 50%. The description provides context for schema_description (plain English) but adds little for other params like sources_hint or delivery_format beyond the schema's empty 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 it provides novel datasets that don't exist yet, with specific examples like cross-domain fusion and industry-specific schemas. It distinguishes from sibling tools by emphasizing custom creation.

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 custom dataset needs with pricing tiers, but lacks explicit when-not-to-use or alternative tool guidance. Context signals show many sibling data tools, but no direct comparison.

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 the process: receive price, scope, quote_ref, pay_url; pay via x402; delivery in 24h. It also mentions optional parameters (urgency, budget). It doesn't cover auth, rate limits, or failure scenarios, but for a quoting tool it provides effective 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 two sentences long. It is front-loaded with 'FREE.' Every sentence serves a purpose: stating what the tool does, what it returns, and how to proceed. 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?

With 3 parameters, no output schema, and no annotations, the description covers the tool's purpose, input parameters, and key outputs (price, scope, quote_ref, pay_url). It does not specify output structure, but the description lists the outputs clearly. For a simple quoting tool, it is largely 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%, so baseline is 3. The description adds context: 'need' is free-text, 'urgency' optional with no further detail (but enums are in schema), and 'budget_usd' is optional with the note 'we may accept it within our floor,' which adds meaning 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: 'Describe any data need in plain language and receive an instant firm quote.' It specifies the verb (describe), resource (HSH Intelligence Data-on-Demand), and outcome (price, scope, quote_ref, pay_url). It distinguishes from siblings like hsh_check_order and hsh_check_quote by noting it is for describing a need before purchasing custom 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?

The description explicitly says 'FREE' and 'Use this BEFORE purchasing custom data,' providing clear context for when to use this tool. It implies alternatives by naming sibling tools (check_order, check_quote), but does not explicitly state when not to use it. The guidance is sufficient for an AI agent.

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

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

No annotations are provided, so the description carries full burden. It discloses the data source (SEC 8-K + SD filings), the event types, ESG mapping, severity grading, and links to filings. It mentions pay-per-call pricing, which implies external authentication/billing, but does not detail rate limits or required authorization beyond that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph with no filler. It front-loades the core purpose, then efficiently lists data sources, event types, features, and filters. Every sentence contributes meaningful information, balancing brevity with completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description provides essential context: data origin, event categories, severity grading, and linking. It explains the optional parameters. However, it does not describe the output format or structure of returned events, which would help in planning further processing. Overall, it is sufficiently complete for an agent to understand and invoke the 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 100% coverage with descriptions for all four parameters. The description adds marginal value, reiterating that filters are optional and providing examples for ticker. Beyond that, it does not clarify parameter constraints like allowed pillar values (though schema says 'E, S, or G') or date range restrictions.

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 provides authoritative ESG material-event intel from SEC EDGAR filings for US-listed companies. It lists specific event types (governance changes, restatements, etc.) and distinguishes from sibling tools like hsh-esg-news, which likely covers news-based ESG 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?

The description gives context that it's for US-listed companies, sourced from SEC filings, and mentions optional filters. It implies when to use (for filing-based ESG events) but does not explicitly state when not to use or name alternatives. However, the sibling context provides implicit differentiation.

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 for behavioral disclosure. It clearly conveys key traits: real-time detection, enrichment, classification by pillar, severity grading, source linking, and payment via x402. It does not discuss rate limits or pagination, but covers the core behavior well for a query 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 approximately 80 words, efficiently structured in two paragraphs. The first sentence immediately states the core purpose, and subsequent sentences add value without redundancy. Every sentence earns its place, making it highly concise and well-organized.

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 4 optional parameters, no output schema, and no annotations, the description covers the input usage, processing (enrichment, classification), and output characteristics (pillar-classified, severity-graded, source-linked). It lacks a brief example of return fields, but is otherwise fairly complete for a data 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?

All four parameters have descriptions in the input schema (100% coverage), so the description adds limited new meaning beyond reinforcing the 'region' parameter with an example. The baseline is 3, and the description does not provide additional syntax or format details, but it does clarify the purpose of the 'region' filter, which adds minor value.

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 provides 'EU & global ESG controversy intel for compliance and trading agents', detailing detection from news and enrichment with LEI and sanctions screening. It distinguishes itself from the sibling hsh-esg-events by noting it is 'News-derived breadth (complements the authoritative SEC-filing ESG product hsh-esg-events)', making the purpose specific and clearly differentiated.

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 for use, such as 'Pass region='EU' to filter to EU-domiciled companies' and hints at alternatives by mentioning hsh-esg-events as a complement. However, it stops short of explicitly stating when not to use this tool, which would elevate it to a 5.

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

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

With no annotations, the description carries the full burden. It details behavioral aspects: delivers a verified, deduplicated dataset with train/val/test split in HuggingFace format. It mentions verification methods and pricing, but does not cover turnaround time or side effects like 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 a single paragraph that is informative but somewhat verbose, including pricing details that may not be critical for an AI agent. It is front-loaded with the core purpose and structured well, but could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the lack of an output schema, the description thoroughly explains the return format: a clean, HuggingFace-ready dataset in Alpaca schema with splits and deduplication. It also covers verification and target platforms, making it complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already documented. The description adds context about the overall process and default schema, but does not significantly enhance parameter meaning beyond what the schema 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 produces 'Made-to-order, answer-verified datasets for LLM fine-tuning' and provides specific examples like 'step-by-step math reasoning', making the purpose explicit. The name and title align, and it distinguishes from sibling tools by emphasizing verification and target platforms.

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 explains when to use the tool ('Describe the task...') and lists target platforms, but does not explicitly state when not to use it or mention alternatives like the sibling 'hsh-custom-dataset'. The guidance is clear but lacks exclusion criteria.

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

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

With no annotations provided, the description carries the full burden. It discloses the six signals are 'free signals,' mentions payment via x402, and notes these are 'operational/behavioral signals that precede price moves.' However, it does not describe the output format or what happens if optional parameters are omitted, leaving some behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that front-loads the main purpose and efficiently lists six signals with parenthetical details. Every sentence adds value, but breaking the list into bullet points could improve readability slightly.

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 complexity of 9 parameters and no output schema, the description covers the purpose, all input signals, and payment method comprehensively. The only notable gap is the lack of any information about the output format or expected return structure, which would aid 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 description coverage is 100%, but the description adds significant meaning beyond the schema by explaining each parameter in the context of its signal (e.g., 'Greenhouse board token' and 'HIRING posture categories'). This enriches the agent's understanding of how each input contributes to the overall intelligence.

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 is 'multi-signal alt-data intel for investment research' and lists six specific signals (hiring posture, insider activity, GitHub velocity, etc.), distinguishing it from sibling tools like hsh-crypto-intel or hsh-esg-events. The verb 'combines' and resource 'signals' are specific and 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 says 'Pass whichever identifiers you have,' implying flexibility but does not explicitly state when to use this tool versus alternatives among the sibling tools. No exclusions or when-not-to-use guidance are provided, only implied usage 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 provided, so the description must cover behavioral traits. It mentions real-time, structured, agent-ready, and classification with sentiment/impact, but lacks details on freshness, rate limits, default behavior, 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, front-loaded with purpose, followed by features and payment. No wasted words, but could be structured more clearly (e.g., grouping features vs. billing). Good conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should explain return values. It mentions classification and tags but omits full field list, pagination, default limit, and error handling. Adequate for a simple tool but with notable gaps.

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 clear descriptions for both parameters (limit and symbol). The description adds only the billing note and restates the symbol filter as optional, providing no deeper semantics beyond the schema. Baseline 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 provides 'Live NSE corporate-announcement intel for Indian listed companies', specifying the source, type of announcements, and classification by event type, sentiment, and impact summary. It is specific and distinguishable from 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?

No guidance on when to use this tool versus alternatives. The only usage-related information is 'Pay per call via x402', which is billing, not usage guidance. No when-not or alternative comparison.

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?

Although no annotations are provided, the description fully discloses the returned data components, units (INR crore), and the self-computed nature of ratios. It does not mention update frequency or caching, but the read-only behavior is clear.

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 densely informative but well-structured with natural breaks. It front-loads the core purpose and efficiently lists components. Minor redundancy could be trimmed, but overall it earns its length.

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 complexity and absence of output schema, the description covers most key aspects: data source, frequency split, specific financial items, ratios, and shareholding. It lacks historical depth and update timing but is sufficient for basic fundamental analysis.

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 significant meaning beyond the schema: it explains that 'period' implies quarterly vs annual data output, gives example symbols, and details the data organization. This compensates for the lack of output 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 provides Indian listed-company fundamentals from NSE official XBRL filings. It specifies the data types (quarterly P&L, annual balance sheet, ratios, shareholding) and distinguishes from third-party data sources, 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 mentions passing an NSE symbol and pay-per-call pricing but does not explicitly contrast with sibling tools like hsh-company-intelligence or hsh-india-announcements. Usage context is implied but not clearly delineated.

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 the full burden of behavioral disclosure. It only mentions 'FREE,' implying no cost, but does not describe rate limits, authentication needs, or whether the operation is read-only or destructive. The lack of detail leaves the agent uncertain about behavioral expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently conveys the core purpose and a key advantage ('FREE'). It front-loads important information and contains no unnecessary words. Every part serves a 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 tool has no parameters, no output schema, and no annotations, the description is somewhat adequate. It explains what the tool lists and includes a call to action for custom orders. However, it does not describe the output format or any limitations, which could aid the agent. For such a simple tool, it is marginally 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 (empty object), and schema description coverage is 100%. According to guidelines, 0 parameters yields a baseline of 4. The description does not need to add parameter semantics since none exist; it appropriately omits extraneous detail.

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 lists 'HSH Intelligence's catalogued data capabilities with live pricing, plus how to order custom data.' The verb 'list' is specific, and the resource is well-defined. This distinguishes it from sibling tools like hsh_check_order or hsh_describe_data_need, which have different 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 explicit guidance is given on when to use this tool versus alternatives. The description implies it is for viewing capabilities and pricing, but it does not mention when to use other sibling tools like hsh_check_order or hsh_describe_data_need.

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 fully covers behavioral aspects: it details the data sources, limitations (license-walled financials), pricing, and the specific inclusion of Tadawul data for Saudi. It is honest about what the tool delivers and does not overpromise, providing a clear understanding of its capabilities.

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 dense but well-structured, starting with purpose, then listing what it combines, adding a note on limitations, and ending with usage instructions. While somewhat long, every sentence contributes useful information, and it is front-loaded with the key 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 tool's complexity (multiple data types per country), the description covers the main components comprehensively. It does not detail exact return format or error handling, but the lack of an output schema is partially compensated by describing what the tool provides. The note on limitations adds 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 adds value by providing examples of tickers (e.g., 2222.SR for Aramco) and clarifying that country codes are for Gulf countries. This helps users correctly fill in parameters beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as providing MENA (Gulf) market intelligence for trading agents, listing specific signals: oil, sovereign macro, USD-peg stability, live equity pricing, and Tadawul data for Saudi. It distinguishes itself from sibling tools by its geographic and market focus, making it 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 instructs users to pass a country code and optional ticker, and mentions payment via x402 on Base. It implies use for Gulf market intelligence but does not explicitly state when to use it versus alternative tools or mention conditions to avoid. Context from sibling tools provides differentiation.

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 pricing, frequencies, and webhook alerts but omits behavioral traits like authentication needs, rate limits, data retention, or whether changes are destructive.

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 efficiently cover core purpose, features, and pricing without extraneous words. Each 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?

For a monitoring tool with multiple configurable parameters and pricing, the description covers high-level features but lacks detail on return format, error handling, or integration with other tools. Missing output schema exacerbates this.

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 40%, low. The description adds context for frequencies (daily, hourly, real-time) and pricing related to duration_months, but lacks explanations for webhook_url and alert_threshold beyond the enum. It does not fully compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs 'Continuous URL/page monitoring with change detection', specifying key features like snapshots, webhook alerts, and screenshot archive. This distinguishes it from sibling 'hsh-web-scrape', which likely offers one-time scraping.

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 ongoing monitoring but does not explicitly state when to use this tool versus alternatives (e.g., for one-time scraping vs. persistent monitoring), nor does it provide exclusions or prerequisites.

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 fully carries the burden. It discloses the return format (verdict with failing test and ratios) and mentions payment via x402 (USDC on Base), adding behavioral context beyond the basic screen. It does not contradict any annotations as none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise: three sentences covering purpose, process, and output/cost. It is well-structured with no redundant words, earning its place with efficient, front-loaded 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?

Despite no output schema, the description fully explains the return values (verdict with failing test and ratios). It covers the screening criteria comprehensively, making the tool's behavior complete for a single-parameter, read-only screening 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 coverage is 100% with one required parameter (ticker) already described. The description adds value by explaining the screening methodology and output, but does not provide additional parameter-level details beyond what the schema offers. 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 the tool's purpose: a rule-based Shariah compliance screen for US-listed companies. It specifies the verb 'screen' and the resource 'Shariah compliance for US-listed companies', distinguishing it from sibling tools like hsh-company-intelligence and hsh-esg-events by its unique domain.

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 detailed context on when to use the tool: for Shariah screening of US-listed companies with specific AAOIFI-style criteria. It does not explicitly state when not to use or suggest alternatives, but the specificity makes its use case 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 provides key behavioral traits: it is free to register, it is a standing subscription, it watches events and sends quotes on match, and payment is per matched delivery. It lacks details on cancellation or default behavior but covers the main aspects.

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 only three sentences, front-loading the main purpose and example, then adding transition and payment details. 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?

Given moderate complexity (6 optional parameters, no output schema), the description covers the main concept but leaves ambiguity about what happens when no parameters are provided (e.g., subscribing to all events) and lacks information on managing subscriptions. It is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 6 parameters have descriptions in the schema (100% coverage). The tool description adds overall context but does not provide additional meaning beyond what is already in the schema for each parameter. Thus it meets but does not exceed the 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's purpose: register a standing subscription to watch for events and send a quote on match. It provides a specific example (SEC filings) and distinguishes it from one-off data buying, making the purpose very 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?

The description implies when to use this tool (for continuous monitoring instead of one-off purchases) and mentions the pay-per-delivery model. However, it does not explicitly state when not to use it or provide alternatives among siblings, but given no other subscription tool, it 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?

With no annotations provided, the description must fully disclose behavioral traits. It mentions pricing and complexity multiplier but omits critical details like rate limits, authentication requirements, data freshness, error handling, or what happens when sites block scraping.

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 efficient, with purpose stated upfront. Pricing details add length but are relevant. Minor waste could be trimmed by separating pricing into annotations.

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?

Lacking output schema and annotations, the description should explain return format, pagination handling, and error states. It does not cover these, leaving the agent with insufficient information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds pricing context but does not clarify parameter formats or expected values (e.g., how 'fields' are specified, meaning of 'all' for quantity).

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 is for custom web scraping of structured data from public sites, specifying it handles static HTML, JS-rendered, paginated, and anti-bot scenarios. However, it does not explicitly differentiate itself from sibling tools like hsh-custom-dataset or hsh_describe_data_need.

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 pricing tiers but offers no guidance on when to use this tool versus alternatives (e.g., hsh-b2b-contact, hsh-company-intelligence). There is no mention of prerequisites, use cases, or exclusion criteria.

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