Hackertarget

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's behavioral disclosure is supplementary. It adds value by explaining the free default model, the BYO key mechanism for Anthropic, and the return structure (score, confidence, signals, raw_response). No contradictions, but it could mention rate limits or data handling practices.

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

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

The description is three sentences: core function, optional setup with API key, and return details plus use cases. Every sentence earns its place, and critical info is front-loaded. No repetition of schema content.

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

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

Given 4 parameters, full schema coverage, and no output schema, the description covers purpose, parameter behaviors, return format, and usage scenarios. It is sufficient for an agent to select and invoke the tool correctly without needing additional explanation.

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 enriches semantics by explaining the default model for 'models', the API key usage for '_apiKey', and the purpose of 'context' for disambiguation. This adds meaningful information beyond the schema descriptions.

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

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

The description clearly states the tool's function: probing LLMs about a business/brand/product/topic and scoring visibility (0-100) per model. It specifies the default model and optional Anthropic integration, distinguishing it from siblings like 'scan_competitor_ai_presence' which focuses on competitor scanning rather than general visibility scoring.

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 lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also notes prerequisites (e.g., needing an API key for Anthropic models). However, it does not explicitly compare to alternative tools or state when not to use this tool, so it lacks exclusions.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, etc. Description adds that it returns structured answers with citations but doesn't disclose other behaviors like rate limits. Aligns with annotations; no contradiction.

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?

Front-loaded with key guidance, then provides examples. Every sentence adds value, though slightly verbose. No wasted words, but could be shorter without losing clarity.

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 routing tool with good annotations and thorough schema, description adequately covers when and how to use it. No output schema expected. Lacks mention of limitations or error handling, but otherwise 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?

Input schema has 100% coverage, describing all 6 parameters and aliases in detail. Description reinforces that the question is natural language. No additional semantics beyond schema, so 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 routes factual questions to thousands of sources, specifying exact domains (SEC filings, FDA data, etc.) and distinguishing itself from general web search. It gives concrete examples, making purpose unmistakable.

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

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

Explicitly prefers this tool over web search for structured factual queries, lists trigger phrases ('what is', 'look up'), and provides examples. Does not compare with siblings like 'ask_pipeworx_grounded', but the guidance is strong.

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?

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, destructiveHint. Description adds the specific return format (answer, evidence, confidence, etc.), refusal reasons, and cost implication, going well beyond annotations.

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 front-loaded with key purpose and usage, then details. It is slightly long but each sentence contributes value, so it remains efficient.

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

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

Given the tool's complexity (routing, extraction) and no output schema, the description covers purpose, usage, behavior, output structure, and refusal reasons comprehensively.

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 all parameters described as aliases for 'question.' Description does not add substantive meaning beyond the schema, meeting the baseline but not exceeding it.

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

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

The description clearly identifies the tool as a hallucination-resistant answer mode for high-stakes reads, specifying it extracts answers solely from tool results. It distinguishes from sibling 'ask_pipeworx' by noting the extra LLM call and use case for critical answers.

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?

Explicit guidance is provided: use when answers will be quoted/cited/acted on and facts must not be invented, with prefer 'ask_pipeworx' for casual lookups. Refusal reasons are listed, aiding agent decision-making.

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?

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) clearly indicate safe, idempotent behavior. However, the description adds no behavioral context beyond the annotations, such as external API dependencies or rate limits.

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

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

While short, the description is under-specified and does not earn its place. It provides no actionable information, making it ineffective rather than 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?

With no output schema and minimal description, the agent cannot infer what the tool returns (e.g., AS number, organization, network range). The definition is incomplete for informed 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 has one required parameter 'target' with no description. The tool description (0% schema coverage) fails to explain what 'target' represents (e.g., IP, domain, ASN), leaving the agent without necessary context.

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

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

The description 'HackerTarget as_lookup lookup' is a tautology that restates the tool name without specifying what 'as_lookup' does or what it looks up (e.g., Autonomous System information). It does not differentiate 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 like dns_lookup, reverse_dns, or whois. No context on prerequisites or appropriate scenarios.

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

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

The description far exceeds annotations by detailing behavioral traits: resolution process, fan-out examples, response shapes, resolver contract, parent event extraction, news fields, safety measures (low-confidence short-circuit, closed market handling), wide-spread market handling, and resolution-rule risk. Annotations already indicate read-only, idempotent, and non-destructive nature, and the description adds no contradictions.

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 very long and dense, containing extensive details across multiple sections. While it is well-structured with headings (CLASSIFIERS, FAN-OUT EXAMPLES, etc.), it sacrifices conciseness and may overwhelm an agent. It could be more concise while retaining key information.

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

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

Given the tool's complexity and lack of output schema, the description is remarkably comprehensive. It covers input types, process, response fields, safety mechanisms, edge cases (closed markets, low-confidence matches, wide spreads), and resolution-rule risks. An agent would have sufficient context 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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by providing examples of market_input, explaining the default depth ('Default thorough'), and elaborating on the include_raw parameter's effect on response size. This extra context justifies a score above 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: researching Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet + comparison). The verb 'research' and resource 'bet' are specific, and the description distinguishes from sibling tools like polymarket_edges through its detailed explanation of the fan-out process and evidence packet generation.

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

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

The description explicitly states when to use the tool: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides guidance on interpreting results, such as inspecting market_match_confidence before trusting analysis. However, it does not explicitly state when not to use it or mention alternatives among sibling tools, missing some exclusivity 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, indicating safe, non-destructive, idempotent behavior. The description adds substantial behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs. No contradictions.

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 example queries and imperative guidance. Every sentence adds value, but it could be slightly more structured (e.g., bullet points for company vs drug details). However, it remains concise and informative without unnecessary verbosity.

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

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

Given the simple input schema (2 params), rich annotations, and absence of output schema, the description is remarkably complete. It covers input types, data sources, sorting behavior, output format (paired data + URIs), and performance motivation (replaces 8–15 lookups). Nothing critical is missing for an AI agent to select and invoke this tool correctly.

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

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

Schema coverage is 100%, providing baseline 3. The description adds meaning beyond the schema by explaining the enum values: 'company' pulls latest 10-K financials, 'drug' pulls adverse events/approvals/trials. For 'values', it clarifies format (tickers/CIKs for company, names for drug) and quantity range (2–5), adding significant value for correct parameter usage.

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 does side-by-side comparison of 2–5 companies or drugs in one parallel call, with example queries like 'compare X and Y' and 'rank these companies'. It distinguishes itself from sequential lookups, making the purpose very 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 explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. It delineates data sources for company vs drug types but does not explicitly state when not to use (e.g., for single entity lookup), though the context of sibling tools like 'entity_profile' implies alternatives.

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 adds substantial behavioral context beyond annotations: parallel execution, extraction of evidence with confidence/source/citation, explicit gaps for unanswerable facets, no invention, and execution time expectations (15-60s). No contradictions with readOnlyHint (it reads data) or idempotentHint (idempotent queries).

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, front-loading the core purpose and key behavioral traits. While it could be slightly tightened, every sentence serves a purpose, covering usage, output, and requirements without unnecessary repetition.

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 fully explains the return value (structured findings packet with evidence, confidence, source, citation, gaps). It also covers prerequisites (Pipeworx account, paid plan for thorough), execution time, and the parallel decomposition mechanism, leaving no critical 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% (both params described in schema). The description adds value by noting that `depth:'thorough'` requires a paid plan, which is not in the schema. It does not repeat schema details, so it moderately enhances parameter 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 specifies the tool does 'grounded multi-source research in ONE call', decomposes questions into sub-questions, and routes to thousands of tools in parallel. It clearly distinguishes from sibling `ask_pipeworx` for single lookups, making the purpose and scope 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?

Explicit guidance: use for broad/multi-part questions ('compare X and Y', 'regulatory + financial + market'), and explicitly suggests `ask_pipeworx` for single lookups. Also notes account requirements and depth limitations, ensuring correct invocation.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context that the tool returns directly callable results with schemas and examples, and no second schema lookup is needed. This explains the output behavior beyond what annotations provide.

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

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

Two sentences, front-loaded with purpose and examples, no wasted words. Every sentence adds value.

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

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

Explains return format (top-N tools with names, descriptions, schemas with examples) and recommends calling it first. For a 6-param tool with no output schema, this covers essential context. Could mention pagination but not critical.

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 parameters are described in the schema. The description does not add significant new meaning beyond mentioning 'natural language description' and aliases, which are already in the schema. 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 verb 'Find tools' and the resource 'tools' accessed by describing data/task. It lists specific domains (SEC filings, FDA, etc.) and distinguishes itself from sibling tools by being a discovery/listing tool, not a specific data tool.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set'. Provides clear when-to-use guidance and implies it's a starting point.

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?

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already convey the safety profile. The description adds no further behavioral details such as rate limits, return format, or scope of results.

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 under-specified; it is too short to be useful. Conciseness should not sacrifice essential 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?

With no output schema and a single parameter, the description should at minimum explain the tool's function and parameter format. It fails completely, leaving the agent with no actionable information.

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

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

Schema coverage is 0% and the description does not clarify the 'target' parameter. The agent is left guessing whether it expects a domain name, IP address, or hostname, making correct invocation unlikely.

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

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

Description is 'HackerTarget dns_host_search lookup' which essentially restates the tool name without specifying the exact resource or action. It fails to distinguish this tool from sibling tools like dns_lookup or reverse_dns.

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 such as dns_lookup, find_shared_dns, or reverse_dns. The description does not provide any context about typical 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no behavioral context beyond what annotations provide, such as rate limits, output format, or data source limitations. Score 3 is baseline given annotation coverage.

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

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

The description is extremely concise (three words), but it sacrifices critical information. Conciseness without clarity is not effective; it should be longer to provide necessary details while remaining focused.

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 minimal description, the tool is incomplete for effective use. An agent would not know what result format to expect or how to interpret the output, making it nearly impossible to invoke successfully.

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

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

Schema description coverage is 0%, and the description does not clarify the 'target' parameter. It fails to specify whether the input should be a domain name, IP address, or other format. The parameter is completely undocumented, leaving the agent without essential usage information.

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

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

The description 'HackerTarget dns_lookup lookup' is vague and almost tautological. It does not specify what DNS records are queried (e.g., A, MX, TXT) and fails to distinguish from sibling tools like reverse_dns or dns_host_search. The mention of 'HackerTarget' hints at a data source but lacks actionable specificity.

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. There is no mention of context, prerequisites, or exclusions. An AI agent cannot determine if this is the appropriate tool for a given DNS query without additional 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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context: the tool fans out across multiple sources, patent lookup via USPTO is sunset (soft-fall), and names are not supported. No contradictions with annotations.

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 fairly long but front-loaded with example queries. Every sentence adds value, though the patent soft-fail detail could be condensed. Overall, it is well-structured and efficient for its complexity.

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 sources, no output schema), the description is remarkably complete. It explains all major output components, their sources, and limitations (e.g., patent sunset, name resolution). No critical 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%, but the description adds meaning beyond the schema: for 'type', it says only 'company' is supported; for 'value', it specifies ticker or zero-padded CIK, and crucially notes that names are not supported. This helps the agent understand parameter constraints.

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

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

The description clearly states the tool provides a full cross-source profile for US public companies. It lists specific data sources (SEC, XBRL, USPTO, news, GLEIF) and outputs (cik, filings, fundamentals, patents, news, LEI). Action verbs like 'Tell me about', 'research', and 'brief me' make the purpose obvious. It also distinguishes from sibling tools like resolve_entity.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also advises using resolve_entity first if only a name is available, and warns that patents may soft-fail. This provides clear when-to-use, when-not-to-use, and alternative guidance.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds no new behavioral context beyond what annotations provide, such as details about API queries or result format. A 3 is appropriate as it does not contradict annotations but adds minimal value.

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 short (one sentence), but it lacks essential information. Conciseness is achieved at the expense of completeness, earning a middle score.

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 no output schema and a single parameter missing description, the tool is poorly documented. The description does not explain what the lookup returns or any usage context, leaving the agent undersupported.

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

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

Schema description coverage is 0%, yet the description does not explain the 'target' parameter. It merely restates the tool name without clarifying what kind of value (e.g., domain, IP) is expected. This fails to add 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 includes a specific verb ('find') and resource ('shared dns'), indicating it performs a lookup for shared DNS records. However, it does not differentiate from sibling tools such as 'dns_lookup' or 'reverse_dns', leaving ambiguity about its unique 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 guidance is provided on when to use this tool versus alternatives. The description lacks context about the appropriate use case or any exclusions, making it hard for an agent to decide when to invoke this tool over similar DNS 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 aligns with annotations (destructiveHint, idempotentHint) and adds context about clearing sensitive data. No contradictions. It sufficiently discloses the destructive nature of deletion.

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 effectively convey purpose, usage, and relationships. No fluff, front-loaded with action.

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

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

For a simple single-parameter tool with no output schema, the description fully covers purpose, usage, and sibling context. Complete for agent decision-making.

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

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

Schema coverage is 100% for the single parameter key. The description only repeats 'by key' without adding new 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 deletes a previously stored memory by key, distinguishing it from remember and recall which are explicitly mentioned as sibling tools.

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

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

The description provides explicit use cases: stale context, completed tasks, or clearing sensitive data. While it doesn't specify when not to use, the positive guidance is sufficient.

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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that the tool fetches the page, extracts title/description/key links, and emits standard format. This goes beyond annotations by describing the process and output, though no additional constraints like rate limits are mentioned.

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 four sentences, front-loaded with the core purpose, and each sentence adds necessary detail (target, process, output location, use cases). No superfluous text.

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

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

Given no output schema, the description explains the output is a single text blob for site-root. It covers input (URL), process (fetch/extract), and output format. Missing error handling or edge cases, but adequate for a simple 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%: both parameters (url, max_links) have descriptive schema entries including defaults. The description does not add new semantic information beyond what the schema provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, targeting AI crawlers. It specifies the action (generate), the resource (llms.txt), and the use context. This distinguishes it from sibling tools like page_links or scan_competitor_ai_presence.

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 lists three use cases: indexing a client's site, drafting for own project, or auditing a competitor. While it does not mention when not to use it or alternatives, the stated scenarios provide clear guidance for appropriate usage.

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

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

Annotations already indicate readOnlyHint, idempotentHint, openWorldHint, and non-destructive behavior. The description adds no extra behavioral context such as rate limits, dependencies, or scope of data.

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, consisting of only three words. While not verbose, it lacks structure and does not front-load the most critical details.

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 absence of an output schema and parameter descriptions, the description should explain return values and input format. It fails to do so, making it incomplete for effective 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 single parameter 'target' is typed as string with no schema description. The description 'geoip lookup' implies the target is likely an IP address or domain but does not explicitly specify format or constraints. Important semantic information is missing.

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 'HackerTarget geoip lookup' clearly indicates a geo-IP lookup operation using HackerTarget service. It states the verb and resource but does not differentiate from sibling tools like whois or dns_lookup.

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 usage guidelines are provided. The description does not specify when to use this tool versus alternatives or mention any prerequisites or 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?

Annotations already provide readOnly, openWorld, idempotent hints; description adds no behavioral context beyond 'lookup'. Does not disclose what happens during the lookup or any side effects.

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?

Extremely short but not concise; it is under-specified. One sentence that adds little value, lacking structure.

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?

Completely inadequate for a tool with one required parameter, no output schema, and no parameter descriptions. Does not explain return values or parameter semantics.

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 required parameter 'target' has no description in schema (0% coverage) and the description does not explain its expected format (e.g., domain, IP). No guidance 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?

Description is minimally informative, essentially restating the tool name. 'HackerTarget http_headers lookup' does not provide a clear verb or scope, and fails to distinguish from similar lookup 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 or alternatives. Lacks any context about prerequisites or when not to use it.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context by listing return fields and implying it returns active subscriptions by default, which is consistent with annotations.

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

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

Two sentences, front-loaded with purpose, 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 no output schema, the description lists return fields. Parameter count is low and schema covers it. Context signals indicate read operation. Description is sufficient for using 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 coverage is 100% with a single optional boolean parameter described. The description mentions 'active subscriptions' but does not add semantics beyond what the schema already provides for the parameter.

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

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

The description clearly states the verb 'List', resource 'subscriptions', and scope 'caller's active'. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' which are write operations.

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

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

Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', providing clear context for when to use the tool. Does not explicitly mention alternatives but implies it's for reading current subscriptions.

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?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds no behavioral details such as output format, rate limits, or data returned. With no contradiction to annotations, but minimal additional value.

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?

Extremely short, but under-specified. While concise in word count, it lacks informative content. Every sentence should earn its place; here it does not.

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 should clearly state what the tool does. The current description is too vague to distinguish from similar tools or fully inform usage.

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

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

Schema description coverage is 0%, and the description does not explain what 'target' should be (e.g., hostname or IP). No added meaning beyond the bare schema.

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

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

Description states 'HackerTarget mtr lookup' which identifies the tool as performing an mtr lookup via HackerTarget. However, it does not explain what mtr is (a network diagnostic combining traceroute and ping) and only vaguely distinguishes from siblings like 'traceroute'.

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

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

No guidance on when to use this tool versus alternatives like 'traceroute' or 'nping'. The description lacks any context about suitability or limitations.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds no behavioral context beyond 'lookup,' omitting details like rate limits, data source, or return format.

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, which is brief, but it lacks necessary information. It prioritizes brevity over completeness, making it under-specified rather than concisely informative.

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

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

Given minimal schema (1 param, no output schema), the description should provide significant context. It fails to explain input format, output nature, or use case, resulting in severe incompleteness.

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 single required parameter 'target' has a schema description coverage of 0%. The description does not explain what 'target' should be (e.g., IP address, hostname), leaving the agent without semantic guidance.

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 'HackerTarget nping lookup' identifies the tool as a lookup using HackerTarget's nping service, but it does not explain what nping does (e.g., network ping, port scan). Compared to siblings like dns_lookup, traceroute, and whois, the purpose is vague and not 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?

No guidance is provided on when to use this tool versus alternatives like traceroute, mtr, or dns_lookup. The description lacks context for appropriate use cases or exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds no behavioral details beyond annotations.

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?

Extremely short but under-specified. A single sentence does not provide meaningful information about the tool's functionality.

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 low complexity, the description fails to explain what the tool does, making it incomplete for effective usage.

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 parameter 'target' has no description in schema or description. Schema coverage is 0%, and description does not explain what target should represent.

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 'HackerTarget page_links lookup' is vague. It does not specify what page_links returns or how it differs from sibling tools like dns_lookup or whois.

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

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

No guidance on when to use this tool versus alternatives. The description lacks context for appropriate usage.

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

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

Discloses important behaviors: rate limit (5 per identifier per day), that it's free and doesn't count against quota, and that feedback is read daily and influences roadmap. These details go beyond annotations, which only provide hints.

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

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

Four sentences, front-loaded with purpose, and no unnecessary information. Every sentence adds value, maintaining brevity while being informative.

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

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

Given the tool's simple purpose (submitting feedback), the description covers all necessary context: input parameters, usage scenarios, rate limits, and behavioral notes. No output schema needed; return is straightforward.

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

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

Input schema has 100% description coverage, so baseline is 3. The description adds value by advising to describe issues in terms of Pipeworx tools/packs, which is not in the schema. This extra guidance earns a 4.

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

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

Description clearly states the tool sends feedback to the Pipeworx team about bugs, features, data gaps, or praise. It distinguishes itself from sibling tools by its unique purpose of submitting feedback rather than querying 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?

Explicitly specifies when to use each feedback type (bug, feature, data_gap, praise) and provides guidance on what not to do (avoid pasting end-user prompts). This helps the agent choose the appropriate scenario.

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?

Beyond annotations (readOnly, openWorld, idempotent), the description adds key behavioral details: data source (CF analytics-engine), no PII, caching window (5min-1h), and aggregate nature.

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

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

Front-loaded main purpose, efficient paragraph, but listing three use cases adds slight verbosity; still every sentence is valuable.

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 single parameter, no output schema, the description fully covers behavior, return content, data source, and caching, making it complete for this simple 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 enum and description, and the description adds semantic context (shorter windows for hot trends, longer for steady-state), justifying a score above baseline 3.

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

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

The description clearly states the verb ('returns') and the resource ('top tools, top packs, and total call volume'), and the specific use cases differentiate it from sibling tools like 'ask_pipeworx' and 'as_lookup'.

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 lists three use cases (discovering hot data sources, confirming canonical tool, checking alignment), but doesn't mention when not to use or provide explicit alternatives.

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?

Annotations indicate readOnly and idempotent. The description adds extensive behavioral details: walking child markets, checking ordering, computing partition_check, filtering placeholders, and the fill_check logic with live depth and realizable edge. No contradictions.

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 detailed but front-loaded with the requirement and purpose. Some sentences could be slightly trimmed, but overall every sentence earns its place with specific 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 complex tool with no output schema, the description thoroughly explains the response structure (opportunities, partition_check, fill_check) and references a sibling tool for custom sizing. It covers all necessary aspects 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?

Schema coverage is 100% with descriptions. The description adds valuable usage context (e.g., event slug vs URL accepted, topic as seed question) and explains the modes, but the schema already handles parameter meaning adequately.

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 finds arbitrage opportunities via monotonicity violations and partition-sum checks. It clearly distinguishes two modes (event and topic) with specific examples, differentiating it from siblings like polymarket_edges or polymarket_fill_risk.

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 requires one of `event` or `topic`, warns that no args fails, provides recommendations for each mode, and mentions when to use the sibling tool polymarket_fill_risk for custom sizing. It gives clear context for choosing between modes.

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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint, already covering safety. Description adds caching behavior (1h KV cache), response structure (by_segment, diagnostics), and explains exclusion of Fed bets. Provides rich behavioral context beyond annotations.

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 verbose (~600 words) but well-structured with clear sections and headings. Every sentence adds specific detail, though could be more concise for quick agent scanning. Structure helps navigation.

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 all aspects: model families, response segments, diagnostics, caching, edge calculation, filtering knobs, and rationale for exclusions. Given 9 parameters, no output schema, and complex logic, description is thorough and leaves no obvious 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 detailed descriptions. Tool description adds context beyond schema: defaults, interactions (e.g., min_partition_leg_kelly explanation), and usage advice (e.g., slippage bumps). Adds significant value despite high schema coverage.

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

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

Clearly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price'. Specifies verb (scan, return) and resource (Polymarket markets), and unique value prop (disagreement with Pipeworx). Distinguishes from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread.

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

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

Explicitly states 'Built for what should I bet on today — agents discover opportunities without paging hundreds of markets'. Provides clear context for when to use. Does not explicitly state when not to use or compare with alternatives, but the use case is 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false), the description details output structure (tracked[], expired[], snapshot_dates[] with fields), data limits, and computation basis (daily closes, not intraday). No contradictions with annotations.

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 purpose but is lengthy, including detailed response structure and limits. It could be more concise while retaining key guidance.

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 format (tracked with fields, expired with lifespan, snapshot_dates) and covers limits and data sources. Adequate for the tool's complexity.

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

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

Schema coverage is 100%; description adds context: days default 14, max 30; window default '1wk', explains window as 'snapshot family'. This adds value 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 states the tool provides edge persistence and decay telemetry, answering 'how long has this edge existed and is it shrinking?' It distinguishes from the sibling polymarket_edges by focusing on time-series and decay rather than current snapshots.

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 clear context: use when evaluating edge age and decay, noting that a 3-week-old wide edge differs from a fresh one. It discusses limits (60-day TTL) but does not explicitly state when not to use or name alternative 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?

Annotations already declare readOnly=true, destructive=false, and idempotent=true. The description adds significant behavioral detail: walking the ladder, returning specific fields (top_of_book, vwap_fill_price, slippage_pp, verdict), and warning about thin books and forced directional risk, exceeding what annotations convey.

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-organized with uppercase mode labels and bullet-like enumeration. Each sentence serves a purpose, though it could be slightly more concise; however, it avoids fluff and front-loads key information.

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

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

Despite lacking an output schema, the description thoroughly explains return values for both modes (top_of_book, vwap_fill_price, slippage_pp, verdict for single-market; theoretical_sum, realizable_sum, capture_ratio, thin_legs, etc. for basket). It also covers edge cases like partial fills and directional risk, making the tool fully understandable.

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 descriptions for all 4 parameters. The description adds value by explaining mode-specific interpretation (e.g., size_usd as 'settlement notional' in basket mode) and defaults, enhancing the schema's meaning without redundancy.

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 checks 'realizable-vs-theoretical edge against live CLOB order-book depth,' with clear differentiation into single-market and basket modes. Among siblings like polymarket_arbitrage and polymarket_edges, this tool's risk-checking purpose is distinct.

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?

Directly instructs: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500,' and explains why (theoretical overround not capturable, partial fill risks). Provides clear context for when to use and why alternatives are insufficient.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant context: response structure, safety fields (compatibility_warning, temporal_alignment, skipped counters), and the nuance that pre-mapped topics may not yield tradeable spreads. This goes well beyond the annotations.

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 long but well-structured with clear sections (modes, response, safety fields). Every sentence provides information, and it front-loads the key purpose. There is no superfluous content.

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

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

Given the tool has no output schema, the description compensates by detailing return fields (leg prices, matched spreads, compatibility warning, temporal alignment, skip counters). Parameters are fully covered. It could explicitly mention the exact response format, but the description is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% and each parameter has a description. The description adds examples and explains how the two modes work, including the interaction between topic and explicit tickers. This adds value beyond the schema, though the schema already captures the basics.

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 calculates cross-venue spread between Kalshi and Polymarket, distinguishing two modes (topic shortcuts and explicit tickers). It provides a specific verb ('calculates spread') and resource ('same resolving question'), and differentiates from sibling tools like polymarket_arbitrage.

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 (comparing same outcome across venues) and when not (via compatibility warnings). It details two modes and safety fields. While it does not explicitly name alternatives, the context is clear and the instructions are sufficient for proper use.

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds scoping detail (by identifier) and clarifies behavior with key present vs omitted. No contradictions.

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

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

Two sentences with no wasted words. First sentence gives primary purpose, second provides usage examples and pairing. Front-loaded and 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?

For a simple retrieval tool with good annotations and no output schema, the description fully covers purpose, behavior, usage, scoping, and partner tools. No 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%; the description reinforces the optional key parameter and explains the difference in behavior when omitted. Adds value beyond schema description.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys when omitted. It distinguishes itself from sibling tools by naming remember and forget as partners.

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

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

Explicitly states when to use: to look up context stored earlier without re-deriving. Provides examples of use cases. Could be improved by stating when not to use, but context is 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?

Annotations indicate readOnlyHint=true and destructiveHint=false, implying no state changes. However, the description describes a mark_read parameter that flags events as read, which mutates state. This contradiction undermines transparency; the description should either align with annotations or annotations should be updated.

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 three sentences that follow a logical flow: purpose, returned fields, filtering/mark_read, and alternative access. Every sentence adds value, and there is 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 no output schema, the description covers key aspects: what is returned, filtering options, mark_read behavior, and alternative access. It is mostly complete, though some details about the event payload structure are left vague.

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 value by giving examples (e.g., type 'sec_8k') and explaining the effect of mark_read. This extra context enhances understanding beyond the schema alone.

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 pulls fired events from the subscription feed and returns recent alerts. It specifies the resource and action, but does not explicitly distinguish from sibling tools like 'recent_changes' or 'list_subscriptions', leaving some differentiation implicit.

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 polling works fine and provides an alternative HTTP endpoint, giving some context. However, it does not explicitly state when to use this tool versus siblings or when not to use it, leaving usage guidance mostly implied.

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?

Annotations indicate safe read-only behavior. Description adds fallback logic, error handling, and output structure, exceeding annotation coverage.

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 well-structured with purpose, details, and alternative, but slightly verbose; still 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?

Covers all aspects: sources, fallback, input formats, output structure, and alternative tool, despite no output schema.

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

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

Schema coverage is 100%, but description adds practical examples for since (ISO date, relative shorthand), ticker/CIK for value, and clarifies type restriction, significantly aiding parameter 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 explicitly states the tool provides a change feed for a company in a given window, with example queries and a clear distinction from sibling tool entity_profile.

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

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

It provides explicit when-to-use examples ('What's new with X') and when-not-to-use (use entity_profile for static profile), including an alternative 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?

The description adds behavioral details beyond annotations, such as scope by identifier, persistence for authenticated users, and 24-hour retention for anonymous sessions. Annotations indicate idempotentHint=true and destructiveHint=false, which align with the description. No contradictions.

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 four sentences, with no fluff. It is front-loaded with the main purpose, then use case, then behavioral details. Every sentence adds value.

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

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

Given the tool is a simple save operation with no output schema, the description covers purpose, usage, parameters, and behavioral context. It doesn't explain return values, but that is less critical for a write operation. It is sufficiently complete for an agent to use 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?

The input schema has 100% coverage with descriptions for both key and value. The description adds context by calling it a 'key-value pair' and giving examples, but it doesn't provide significant additional meaning beyond the schema. According to guidelines, with high coverage, baseline is 3.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later' and provides specific examples. The title 'Remember' is brief, but the description makes it unambiguous. It distinguishes the tool from siblings like 'recall' and 'forget' by explaining the pairing.

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 when to use the tool: 'Use when you discover something worth carrying forward' and mentions pairing with recall/forget. However, it doesn't explicitly state when not to use it or list alternatives, but the context is clear enough.

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

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

Annotations already indicate readOnly, openWorld, idempotent. Description adds details on internal cascading through multiple endpoints, return format including citation URIs, and auto-disambiguation for companies.

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 front-loaded with example queries, then a clear usage directive, followed by detailed type breakdown. Every sentence adds value.

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

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

With no output schema, the description thoroughly explains returns for each type, including citation URIs and internal behavior. Covers both supported entity types completely.

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%, but description adds significant context: explains what values work for each type (ticker, CIK, name for company; brand/generic for drug) and mentions auto-disambiguation.

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 resolves names to official identifiers, provides example queries, and covers supported entity types. It distinguishes itself from siblings by focusing on ID resolution needed by other 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?

Explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains it replaces multiple manual lookups and lists supported types with accepted input formats.

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?

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are already comprehensive and cover the behavioral traits. The description adds no value beyond these annotations, but does not contradict them.

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 short but not concise in a helpful way—it is under-specified. It lacks any structure beyond a single phrase and does not front-load key information.

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

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

Given the lack of output schema and the minimal description, the tool definition is incomplete. It does not explain what reverse DNS lookup returns (e.g., hostnames) or how to interpret results.

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 0% schema description coverage, the description must compensate but does not. It says nothing about the 'target' parameter's format, expected input type (e.g., IP, domain), or any constraints, leaving the agent with no guidance.

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 'HackerTarget reverse_dns lookup' is essentially a tautology, restating the tool name without specifying what it does (e.g., returns PTR records). It fails to differentiate from sibling tools like dns_lookup.

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

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

The description provides no guidance on when to use this tool vs alternatives, no context on prerequisites or limitations. It offers no help for an agent to decide between reverse_dns and dns_lookup.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing a strong behavioral profile. The description adds no further transparency, such as rate limits or specific API dependencies beyond mentioning 'HackerTarget'.

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 (0 sentences, just a phrase) but lacks structure. While brevity is positive, the content is too minimal to be effective.

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, 0% parameter coverage, and no explanation of return values or usage context, the description fails to provide a complete understanding of the tool. It is insufficient for an agent to use effectively.

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

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

The only parameter 'target' is a required string with no description in the schema (0% coverage). The description does not clarify what format 'target' should take (e.g., IPv4, domain), leaving the agent without critical information.

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

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

The description 'HackerTarget reverse_ip lookup' indicates the tool performs a reverse IP lookup, which is specific enough to convey the basic action. However, it does not elaborate on what the lookup returns (e.g., domains) or distinguish it from similar sibling tools like 'reverse_dns'.

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 such as 'reverse_dns', 'dns_lookup', or 'whois'. The description lacks any contextual or comparative 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?

Annotations already declare readOnly, idempotent, and non-destructive. The description adds that it probes each entity with ai_visibility_check and returns a ranked list, but does not disclose rate limits, failure behavior, or auth needs beyond the schema's _apiKey note.

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 very concise with three sentences, front-loading the purpose and key output. 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 annotations present and no output schema, the description covers the return format (ranked list with score, confidence, signal density) but omits details like error handling, concurrency, or computation method. Adequate but not comprehensive.

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 tool description does not add additional meaning beyond what the parameter descriptions in the schema already provide (e.g., first entity as subject).

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 'Compare AI visibility across multiple entities side-by-side,' which is a specific verb+resource. It differentiates from sibling ai_visibility_check (single entity) by focusing on comparison and ranking.

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 clear context for when to use the tool: 'Useful for competitive AI-marketing audits.' It implies alternatives (single entity via ai_visibility_check) but does not explicitly state when not to use it.

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

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

Annotations already mark it as read-only, idempotent, non-destructive. The description adds crucial behavioral detail: composite call fans across multiple services, degrades gracefully, and a specific service (bundlephobia) may be slow on first use. This goes well beyond the structured annotation hints.

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 somewhat lengthy (several sentences) but each sentence adds distinct value: purpose, usage context, return structure, error handling, and ecosystem limits. It is front-loaded with the primary function. Could be slightly more structured with bullets but still efficient.

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

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

Given no output schema, the description thoroughly explains return fields: summary block, advisory details, links, alternative versions. It also covers failure modes and partial success. This is complete for a tool with moderate complexity (2 parameters, multiple data sources).

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%. The description adds meaning: for package, it accepts scoped packages (e.g. '@types/node'); for version, it defaults to latest. This clarifies expected input beyond the schema's basic type and required status.

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 starts with a clear composite verb ('should I add this npm package') and specifies the resource (npm dependency). It distinguishes itself from siblings by limiting to npm ecosystem and referencing deps.dev and bundlephobia, which no other sibling tool does.

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

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

Explicitly states when to use: when agent asks about safety, popularity, size. Also notes exclusions (PyPI, Maven, etc. go to deps.dev:version directly). Provides context on partial failures and timing (bundlephobia first measurement can take 5-30s).

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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses internal details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flags, and output structure (offsets, similarity scores). This fully informs the agent about behavior and limitations.

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 and well-structured: opens with a clear purpose, then usage guidance, pairing information, and technical specifics. Every sentence adds value without 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 and lack of output schema, the description covers all necessary aspects: purpose, usage context, technical constraints, output format, and integration with another tool. It is complete for safe and correct agent usage.

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

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

The input schema provides 100% coverage of parameter descriptions. The description reinforces the purpose (e.g., 'text' is already-fetched record) but does not add significant new semantic detail beyond the schema. 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 function as 'Semantic search INSIDE a fetched record,' using a specific verb and resource. It distinguishes from siblings by emphasizing that it works on already-fetched text and pairs with ask_pipeworx_grounded, making its unique role 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 provides explicit guidance: use when the record is too large for the prompt, and it pairs with ask_pipeworx_grounded. It lacks explicit when-not-to-use or alternative tool mentions, but the context is clear enough.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it is safe and idempotent. However, the description adds no behavioral context beyond that. It does not mention what the tool returns, any rate limits, data source specifics, or potential side effects (e.g., network requests). Given the annotations carry the safety profile, the description fails to provide additional behavioral insight.

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 short (4 words), but this is not effective conciseness. It lacks critical information that would help an agent select and use the tool. Every sentence should earn its place; here, the only sentence is redundant with the name.

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 a single parameter and no output schema, the description must compensate by explaining what the tool does and what it returns. It fails to do so. There is no information about output format, typical use cases, or limitations. The description is completely inadequate for an agent to determine if this tool is appropriate.

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

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

The input schema has one required parameter 'target' of type string with 0% description coverage. The description does not clarify what 'target' represents (e.g., IP address, CIDR notation, domain name). There is no hint about expected format or examples. The description adds no meaning beyond the bare 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 'HackerTarget subnet_lookup lookup' is essentially a tautology. It repeats the name and adds only 'HackerTarget', which is a service provider but gives no clue about what the tool actually does. It does not specify whether it performs subnet calculations, looks up subnet information by IP, or something else. Sibling tools include dns_lookup, whois, etc., so the tool's unique purpose is unclear.

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 usage guidance is provided. There is no indication of when to use this tool versus alternatives like dns_lookup or reverse_dns. The description does not mention any prerequisites, typical use cases, or exclusions.

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

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

Annotations indicate non-read-only, non-destructive, and idempotent. The description adds substantial behavioral details: authentication requirement, type-specific parameters, delivery channel options (feed always on, optional email/sms/webhook), SMS cap (10/day), and webhook signing mechanism. No contradictions with annotations.

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 well-structured, front-loading the core action and return value. Each sentence adds value, though it is somewhat lengthy due to examples and delivery details. Could be slightly more concise, but complexity justifies the 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 (3 parameters, nested objects, no output schema), the description is fairly complete. It explains parameter semantics, delivery options, authentication constraints, and hints at return (subscription id). It could mention error scenarios or pagination but is adequate for usage.

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%, but the description adds significant meaning: examples for each subscription type, constraints on params (e.g., sponsor or condition required for clinical_trial), and delivery validation rules (E.164 format, phone verification, webhook restrictions). This goes beyond the schema's generic 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 'Create a proactive monitoring subscription to a live-data event stream' and specifies the return value 'Returns the new subscription id'. It distinguishes from sibling tools like unsubscribe, list_subscriptions, and recent_alerts by focusing on creation of subscriptions.

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 when to use (for live monitoring), account prerequisites (Pipeworx OAuth), supported types with examples, and delivery channel constraints. It implicitly warns against using anonymous/BYO accounts but does not explicitly list when not to use the 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?

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context about the output (category-bucketed questions, exact tool call shapes) and its role as an onboarding tool, going beyond annotations.

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 somewhat lengthy but every sentence adds value. It is front-loaded with common user queries and structured effectively. Minor reduction could improve conciseness without losing clarity.

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 optional param, no output schema), the description covers its purpose, usage context, parameter behavior, and expected output format comprehensively. No gaps are evident.

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

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

Schema coverage is 100% with a single optional parameter. The description adds meaning by explaining the effect of omitting vs passing topic, and providing example values, which goes beyond the schema's simple list of categories.

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: returning category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from sibling tools like ask_pipeworx and discover_tools by explicitly positioning itself as the onboarding entry point.

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 tells when to use ('Use this FIRST when you do not yet know what Pipeworx can do for you') and how to invoke (no args for full spread, or topic parameter for focus). It also references alternatives like ask_pipeworx, providing excellent guidance.

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

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

Annotations (readOnlyHint, idempotentHint) already indicate a safe, idempotent operation. The description adds no additional behavioral details such as output format, rate limits, or dependencies, so it provides minimal value beyond the annotations.

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 short but lacks essential information, making it a tautology rather than a concise, informative statement. It does not earn its place by adding value.

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

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

Given no output schema and a single undocumented parameter, the description is highly incomplete. It omits any details about return values, prerequisites, or expected behavior, which is insufficient for a tool with these characteristics.

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

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

The description does not explain the 'target' parameter at all. With schema coverage at 0%, the description fails to add meaning or guidance on parameter format, validation, or expected input (e.g., IP vs hostname).

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 'HackerTarget traceroute lookup' clearly indicates the tool performs a traceroute operation to a target. However, it does not differentiate from sibling tools like mtr or nping, which may offer similar functionality.

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 such as mtr, nping, or dns_lookup. The description lacks context for appropriate usage scenarios.

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

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

Adds value beyond annotations by explaining that the row is deactivated (not deleted) and historical events remain. No contradiction with annotations (destructiveHint=false matches soft delete).

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

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

Two sentences, no fluff. First sentence states the core action, second adds critical behavioral context. Efficient and front-loaded.

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

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

Completely covers the tool's behavior for a simple 1-parameter, no-output-schema tool. Explains ownership, deactivation, and history preservation.

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

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

Schema has 100% coverage with a clear description for the 'id' parameter. Description does not add additional meaning beyond the schema, meeting baseline but not exceeding.

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 action ('Cancel a subscription') and the resource ('by id'). Distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' with specific verb and 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?

Explicitly mentions ownership enforcement, indicating when to use (own subscriptions only). Does not explicitly mention when not to use or alternatives, but the context is sufficient for an 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description details the return verdict types, structured form, citation, and percent delta, adding valuable behavioral context.

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 somewhat lengthy but well-structured, front-loading the core purpose and then providing necessary details without 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 single parameter and lack of output schema, the description sufficiently covers domain, sources, and return types, making it complete for agent usage.

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 expands on the single parameter by defining it as a natural-language factual claim with concrete examples, adding 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 as natural-language claim verification against authoritative sources, provides example queries, and distinguishes it from siblings by noting it replaces multiple sequential calls.

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 when to use (check factual correctness) and the supported domain (company-financial claims for public US companies), but does not explicitly state when not to use or provide alternatives.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no extra behavioral context, such as rate limits or dependency on an external API, so it provides minimal additional value.

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 short sentence, which is concise but lacks necessary detail. It does not waste words, but it is arguably under-specified for a standalone description.

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 one parameter, no output schema, and minimal complexity, the description is still incomplete. It does not explain what kind of whois data is returned or any additional context, leaving the agent with insufficient information 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?

The only parameter 'target' is a string, but the description does not clarify what it expects (e.g., domain name, IP address). Since schema description coverage is 0%, the description should compensate but fails to provide meaning beyond the schema's type constraint.

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

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

The description clearly states it performs a 'whois lookup' using 'HackerTarget', specifying the service and action. However, it does not distinguish from sibling tools like 'as_lookup' or 'dns_lookup' which are also network lookups, but the purpose is still 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 provides no guidance on when to use this tool versus alternatives such as 'as_lookup' or 'dns_lookup'. There is no mention of prerequisites or exclusions, leaving the agent to infer context from the tool name alone.

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