Hackertarget

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses cost implications ('BYO key — you pay Anthropic directly for those calls') and the exact return payload ('per-model {score, confidence, signals, raw_response} + a combined view'). This adds valuable context not present in 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?

Three sentences efficiently cover the core action, configuration, output, and use cases. No repeated or unnecessary content; each clause contributes to selecting and invoking the tool correctly.

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 compensates by stating the return structure and typical uses. It could elaborate on the semantics of 'signals' or the 'combined view', but for a read-only probe with clear annotations and schema, this is sufficient.

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 the input schema already fully describes all four parameters. The description adds only minor flavor (e.g., default model name, cost nuance) that is not essential to parameter understanding, meeting the baseline for high schema coverage.

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

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

The description opens with a specific action: 'Probe one or more LLMs for what they know about a business / brand / product / topic' and defines a measurable output ('score visibility (0-100) per model'). This clearly differentiates it from sibling tools that ask questions or perform research.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and configuration guidance (default model, optional _apiKey for Anthropic). However, it does not name any sibling alternatives or state when not to use, so it falls short of full exclusion 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 readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context beyond annotations: it routes to a large toolset, fills arguments automatically, returns stable pipeworx:// citation URIs, and works on all tiers. 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 well-structured: leading directive ('PREFER OVER WEB SEARCH'), purpose statement, usage triggers, examples, and escalation paths. It is slightly verbose with redundant examples and marketing phrases like 'works on every tier, one fast call', but every major section earns its place and is front-loaded.

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

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

Given no output schema, the description adequately covers return behavior ('structured answer with stable pipeworx:// citation URIs'), the breadth of domains, and when to escalate to alternatives. It does not detail error handling or edge cases, but for a complex, open-world tool, the description is substantial enough.

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% and clearly documents the 'question' parameter plus aliases. The description provides illustrative examples but does not add semantic detail beyond the schema. Since the schema already handles parameter semantics fully, a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Routes the question to the right one of 5,358 tools across 1395 verified sources' and returns structured answers with citations. It distinguishes itself from siblings by explicitly referencing ask_pipeworx_grounded and deep_research, and framing itself as the default 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?

Provides explicit when-to-use guidance: 'PREFER OVER WEB SEARCH', 'Use whenever the user asks...', and 'START HERE for most questions'. It also gives clear exclusions and alternatives: 'Step up only when needed: for ... use ask_pipeworx_grounded; for broad/multi-part ... use deep_research'.

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, openWorld, idempotent hints, and the description adds valuable context: experimental routing improvements may be live, currently none are active so it matches ask_pipeworx exactly, and it has no fallback because it is a full router. This goes beyond the annotations without 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?

The description is moderately sized but every sentence contributes: it defines the tool, states current status, gives usage direction, and clarifies expectations. It is front-loaded with the identity and beta nature, and avoids 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 explains the response shape is identical to ask_pipeworx, covers the current state (no active candidates), and notes the experimental nature. It could have elaborated on what happens when a candidate is active, but it's sufficiently complete for 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?

Schema coverage is 100% with each parameter described. The description only says 'same arguments' which adds no new detail beyond the schema. Baseline of 3 is appropriate since the schema handles parameter semantics fully.

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

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

The description clearly states the tool is a beta version of ask_pipeworx, an identical universal router with 5,358 tools. It distinguishes itself from the stable sibling by noting it is the experimental edge with candidate routing improvements. The verb 'use' and resource 'ask_pipeworx_beta' are explicit.

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 guidance: use it exactly like ask_pipeworx when you want the newest routing, notes that results are compared against the stable router, and clarifies it is a full working router. However, it doesn't explicitly state when NOT to use it or mention alternative siblings like ask_pipeworx_grounded.

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, but the description adds valuable behavioral context: it performs an extra LLM call, returns verbatim evidence, and gives a structured refusal list with specific reasons. This goes far beyond the annotations and helps the agent understand failure modes.

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?

Although the description is dense, every sentence contributes critical information: purpose, routing, return format, refusal reasons, use cases, and cost comparison. It is front-loaded with the core distinction and remains efficient 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?

Despite lacking an output schema, the description explicitly defines the success and refusal response shapes, including possible refusal reasons. Combined with the usage guidance and behavioral details, it fully equips an agent to decide and invoke 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?

The schema covers all parameters (100% coverage) with the 'question' field described as 'Your question in natural language' and aliases listed. The tool description does not add new parameter-specific semantics beyond that, so the baseline of 3 is appropriate.

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

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

The description opens with a specific verb and resource: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly distinguishes itself from the sibling tool ask_pipeworx by explaining that it extracts answers using only the tool result and returns explicit refusals, making the purpose unambiguous.

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

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

The description explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also notes the cost trade-off (one extra LLM call), providing clear decision criteria against 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 this as read-only, open-world, idempotent, and non-destructive, so the safety profile is covered. However, the description adds no behavioral context about data sources, rate limits, result structure, or any quirks. It merely repeats the tool name, adding little 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 it is under-specified rather than concise. It contains no usable information beyond restating the tool name and source. It does not front-load any meaningful guidance, so the brevity is not a virtue.

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 lookup with one parameter and annotations covering safety, one might expect a minimal description to suffice. However, the description is so sparse that it fails to explain the tool's purpose, parameter semantics, or return value. It is not complete enough for an agent to select and invoke 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?

The input schema defines a single 'target' string parameter with zero description. The description does not compensate for this 0% schema coverage; it never explains what 'target' refers to (e.g., IP, domain, ASN, or something else). This leaves the agent unable to infer correct parameter values.

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 essentially a restatement of the tool name and source. It does not specify what kind of lookup is performed (e.g., autonomous system lookup), what the target means, or what information is returned. It fails to distinguish itself from sibling lookup tools like dns_lookup or reverse_ip.

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. The description does not explain when to use this tool versus alternative lookup/OSINT tools in the sibling list, nor does it mention any prerequisites or contexts where this lookup is appropriate.

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 discloses the resolver contract (market_match_confidence, alternatives, suggestions), blocking status routes (low_confidence_match, market_closed_or_inactive), wide-spread illiquidity, and resolution-rule risk. This goes well beyond the readOnly/idempotent annotations with agent-relevant behavioral details.

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 long but well-organized with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, RESOLVER CONTRACT, SAFETY). While verbose, content is dense and useful; not every sentence is essential, but structure makes it navigable.

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 carries the full burden of explaining return values. It does so thoroughly: result.market fields, analysis fields, evidence keys, parent_event extractor, news fallback fields, and critical safety/status paths. The tool's complexity is fully documented.

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

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

With 100% schema coverage, the baseline is 3. The description adds value by explaining market input variants (slug, URL, question text) and demonstrating category-specific fan-out with concrete examples. It also clarifies depth semantics through the schema and description's response-size implications for include_raw.

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 opens with a specific verb and resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly distinguishes the tool from siblings by describing market resolution, classification, fan-out, and evidence packet delivery.

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 usage scenarios are given: "Use for 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'." It provides classifiers and fan-out examples, but does not explicitly name when to prefer alternatives like polymarket_edges.

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 and idempotentHint, but the description adds rich context: data sources (SEC EDGAR/XBRL, FAERS), correct handling of off-calendar fiscal years, sorting by primary metric, and return of citation URIs. 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?

The description is dense but every clause adds value, from example queries to data source specifics to fiscal-year handling. It is somewhat long but well-structured and front-loaded with usage examples.

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 data sources, edge cases (off-calendar fiscal years), output characteristics (sorted, paired data plus citation URIs), and replaces 8–15 sequential lookups. For a 2-parameter tool without output schema, this is thorough.

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

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

Though the schema covers both parameters, the description adds concrete examples (AAPL, MSFT for company; ozempic, mounjaro for drug), clarifies that values can be tickers/CIKs or drug names, and explains how type affects the data pulled. This exceeds the baseline.

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

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

The description clearly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call, with examples of user intents. It distinguishes itself from sequential single-pack lookups, making its purpose unambiguous.

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

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

Explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides example queries. It also differentiates between company and drug types, giving clear when-to-use guidance.

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

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

Despite strong annotations (readOnly, idempotent, non-destructive), the description adds substantial behavioral context: account requirements, paid gating, parallel dispatch, second-hop gap recovery, contradictions[] behavior, semantic excerpting, never-invented gaps, and expected latency. None of this contradicts the annotations; openWorldHint is consistent with accessing external structured 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 long but every sentence carries actionable information—no filler or repetition of schema/annotations. It is front-loaded with the critical account/sign-in constraint, then logically progresses through function, alternatives, depth semantics, and output characteristics. Slightly dense, but justified by tool 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?

For a high-complexity tool with no output schema, the description fully compensates: it details the return packet (verbatim evidence, confidence, source, fetched_at, citations, gaps[], contradictions[]), latency expectations, edge cases for unsupported topics, and exact behavior of each depth setting. No obvious missing information for an agent to correctly select and invoke this 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 already 100% with meaningful descriptions, but the tool description adds significant extra semantics: quick/standard/thorough facet counts, hop behaviors, gap recovery, contradictions scan, and pricing implications. It also clarifies that the question param is for broad/multi-part natural-language queries.

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

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

The description names a specific verb ('research'), a distinct resource (Pipeworx's 1395 structured data sources), and a unique mechanism (decomposition, parallel routing to 5,358 tools). It explicitly positions itself as NOT open-web search and differentiates from sibling tools like ask_pipeworx and bet_research.

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

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

Provides explicit when-to-use and when-not-to-use guidance: use for broad/multi-part structured-data questions; prefer ask_pipeworx for single lookups, breaking/current-news topics, or when not signed in. It also maps depth levels to use cases and clarifies paid-account limits for 'thorough'.

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 read-only, idempotent, and non-destructive. The description adds that it returns top-N tools with full input schemas and curated examples, ready to call directly with no second lookup. This explains the output format and convenience, but doesn't disclose rate limits or edge cases, so a 4 is appropriate.

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

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

Three sentences, each serving a distinct role: purpose/timing, output/benefit, and strategic positioning. Front-loaded with the core verb phrase. No fluff or 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?

Although there is no output schema, the description explicitly describes the return value (top-N tools with names, descriptions, full input schemas, curated examples) and the direct-call benefit. For a low-complexity discovery tool with a single real parameter (query), this is complete.

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

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

Schema coverage is 100% and includes aliases and examples, so description doesn't need to repeat parameter details. The description enriches the query parameter by listing domain keywords (SEC, FDA, FRED, Census, BLS, ATTOM, etc.), giving the agent a vocabulary of valid query subjects. This adds value beyond the schema's generic 'natural language 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 opens with a specific verb-object pair ('Find tools by describing the data or task') and enumerates many concrete domains (SEC filings, FDA drugs, FRED, Census, BLS, etc.), making the tool's purpose unmistakable. It also differentiates from siblings by positioning itself as the meta-tool for discovering 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?

Explicitly states when to use ('when you need to browse, search, look up, or discover what tools exist') and gives a positional rule ('Call this FIRST when you have many tools available and want to see the option set'). It also implies not for single-answer tasks by saying 'not just one answer'.

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) indicate a safe read-only operation, but the description itself adds no behavioral context. It does not explain how the lookup behaves, what data is returned, or any 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 one short sentence, but it is under-specified rather than concise. It omits essential meaning and does not communicate the tool's purpose or usage effectively.

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

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

For a tool with a single parameter and no output schema, this description is far too minimal. It gives the agent no understanding of what input to provide or what output to expect, making correct invocation nearly impossible.

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

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

The schema defines a single 'target' string with no description, and the tool description provides zero explanation of what 'target' should be (e.g., a domain name, IP address, or hostname). With 0% schema coverage, the description fails to compensate.

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_host_search lookup' essentially restates the tool name with a generic verb. It fails to specify what is actually searched or what the tool returns, and it does not differentiate it from sibling DNS 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 is provided about when to use this tool versus alternative lookup tools. There is no mention of use cases, prerequisites, or exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. However, the description adds no behavioral context—no rate limits, input format expectations, or return structure. It only names a third-party service, which is not behavior.

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

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

The description is extremely short, but it is under-specification rather than conciseness. It says nothing beyond repeating the tool name, so the brevity does not serve the agent's needs.

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 one-parameter tool with no output schema, the description should at least state the tool's purpose and expected input. This description provides virtually no context, making it inadequate for the agent to correctly select or invoke the tool, especially amid many DNS-related siblings.

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

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

The only parameter 'target' has no schema description, and the tool description provides zero information about what format or values it expects. With schema description coverage at 0%, the description completely fails to compensate, leaving the agent unable to know if 'target' should be a domain, IP, or something else.

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 essentially a tautology, repeating the tool name without specifying what kind of DNS lookup is performed (e.g., forward, reverse, record types). It does not distinguish from sibling tools like reverse_dns or dns_host_search.

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?

There is no guidance on when to use this tool versus alternatives. No mention of target types (domain, IP), expected use cases, or exclusions. The agent is left without any context to choose between this and 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, establishing a safe read-only profile. The description adds substantial behavioral context beyond that: it mentions parallel fan-out across sources, specific return structure (filings with URIs, fundamentals sorted by period_end DESC), and important caveats like 'USPTO PatentsView API sunset May 2025 — soft-fails until reactivated' and 'GDELT→GNews fallback.' This is rich behavioral transparency.

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

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

The description is long but information-dense, front-loaded with example queries and the core purpose statement before detailing return fields and sources. Every sentence adds value, though there is some redundancy with the schema (e.g., 'names not supported') that could be trimmed. Still, for a tool with this complexity, the length is justified and well-structured.

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

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

Even though there is no output schema, the description fully explains the return values and behaviors: it lists every field (cik, company_name, recent_filings, fundamentals, patents, news, LEI), specifies the maximum number of filings, gives URI format, notes sorting order, and flags known limitations (patent API sunset, news fallback). It also defines input constraints and the alternative tool, making it complete for the 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 description coverage is 100%, so both parameters (type and value) are already documented in the schema, including the ticker/CIK format and the instruction that names are not supported. The description largely repeats this information ('Pass ticker "AAPL" or zero-padded CIK "0000320193"') without adding new semantic meaning. Baseline 3 is appropriate since the schema handles the parameter documentation.

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: 'full cross-source profile of a US public company in ONE parallel call.' It provides multiple example user queries ('Tell me about X', 'research Acme') and explicitly distinguishes itself from sibling tools by saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups.' This is a specific verb+resource+scope with clear differentiation.

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 explicit usage guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also names an alternative: 'names not supported (use resolve_entity first if you only have a name).' This clearly states when to use the tool and when not to.

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 no behavioral context beyond the annotations. While annotations (readOnlyHint, idempotentHint, etc.) already indicate a safe read operation, the description does not mention input formats, rate limits, or any operational details that would help the agent anticipate behavior.

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

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

The description is extremely short, but it is under-specified rather than concise. A single phrase that merely echoes the tool name does not earn high marks for structure; it lacks substantive content while still occupying space.

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 sparse schema and absence of an output schema, the description needed to explain the tool's purpose and parameters. It fails to do so, making the tool effectively unusable without external context about HackerTarget or shared DNS. The description is incomplete for even a simple one-parameter tool.

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

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

The input schema has one parameter 'target' with no description, and the schema description coverage is 0%. The description does not explain what 'target' represents (e.g., a domain, IP, or hostname) or how it should be formatted, leaving the agent without essential parameter semantics.

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 find_shared_dns lookup' mostly restates the tool name, adding only the vendor 'HackerTarget' and the generic verb 'lookup'. It does not specify what 'shared DNS' means or what action is performed, so it fails to clearly distinguish from sibling tools like dns_lookup or dns_host_search.

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 mention of when to use this tool versus alternatives such as dns_lookup, reverse_dns, or as_lookup, nor any indication of prerequisites or appropriate contexts.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds context about memory being previously saved and that it clears sensitive data, but doesn't go far beyond the annotation-provided safety profile. No contradiction found.

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

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

Three concise sentences: the action, use cases, and related tools. Every sentence contributes, and the description is front-loaded with the primary verb. No waste or redundancy.

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

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

For a simple one-parameter destructive but idempotent tool with no output schema, the description covers purpose, usage timing, and relationship to sibling tools. It's nearly complete, though it could optionally mention what happens if the key doesn't exist, but the idempotent hint mitigates that gap.

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%, with the key parameter already described as 'Memory key to delete'. The tool description references 'by key' but adds no new semantics or format details. Baseline 3 is appropriate since the schema handles parameter documentation.

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, using a specific verb ('Delete') and resource ('memory'). It distinguishes itself from sibling tools like 'remember' and 'recall' by defining the delete operation for memory.

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 conditions: when context is stale, task is done, or clearing sensitive data. It also mentions pairing with remember and recall, offering alternatives. However, it lacks explicit when-not-to-use conditions, so it doesn't fully reach a 5.

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

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

Annotations already cover read-only, idempotent, open-world, and non-destructive hints. The description adds behavioral details: it fetches the page, extracts title/description/key links, and outputs the standard llms.txt markdown format. This enriches the annotations without contradiction, though it does not mention error handling 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?

Three sentences, front-loaded with the main purpose, then process, then use cases. No fluff, every sentence provides value, and the structure is easy to scan.

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

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

Given the tool's simplicity, the description is reasonably complete: it explains the output format ('single text blob'), the process, and use cases. Without an output schema, it does not detail edge cases like unreachable URLs, but for a two-parameter read-only tool, it covers the essential context.

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

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

Schema coverage is 100%, with both url and max_links having clear descriptions. The tool description does not add additional parameter semantics beyond the schema, which is acceptable given the high coverage, so baseline 3 applies.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, with a specific verb ('Generate') and resource ('llms.txt file'). It also outlines the process (fetches page, extracts content, emits markdown) and gives use cases, distinguishing it from sibling tools that focus on analysis or lookups.

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

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

The description provides clear contexts for when to use the tool via 'Useful for:' scenarios (client sites, own projects, auditing competitors) without explicitly naming alternatives or exclusions. It does not state when not to use it, but the use cases offer solid 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 communicate read-only, open-world, and idempotent behavior. The description adds no extra context such as response format, data source, or any limitations. Since there is no output schema, the description should disclose what a lookup returns (e.g., geolocation fields), but it does not.

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 at only four words, which is structurally simple and front-loaded. However, it sacrifices necessary information for brevity, making it less effective than a slightly longer description could be.

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 a single parameter and no output schema, the description carries the full burden of explaining what the tool does. It only states 'HackerTarget geoip lookup,' which is insufficient for an agent to know how to set the target or what to expect in response. Given the tool's simplicity, a more complete description is expected.

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

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

The schema defines a single required string 'target' with 0% description coverage. The phrase 'geoip lookup' implies the target is an IP address or hostname, but this is not explicitly stated. The description fails to adequately compensate for the lack of parameter documentation.

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 identifies the tool as a GeoIP lookup service powered by HackerTarget. It distinguishes itself from siblings like dns_lookup and whois by focusing on geographic IP data. However, it lacks details on input format and output, preventing a top score.

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 offers no guidance on when to use this tool versus alternatives. It does not explain whether it is for IP addresses, domains, or how it complements other recon tools. Given the large sibling set, explicit context is essential but missing.

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, which covers safety. The description adds no additional behavioral context such as external API dependencies, rate limits, or output format, providing no 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 brief, but this is under-specification rather than conciseness. It is not a full sentence and lacks any informative structure, offering no useful content beyond the tool 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?

Even for a simple tool, the description should at least clarify what target refers to and what the output will be. The lack of an output schema increases this need, but the description provides neither, making it incomplete 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 only parameter 'target' is a required string, but the description does not explain what it means (e.g., a domain, URL, or IP). With schema description coverage at 0%, the description fails to compensate, leaving the parameter's semantics completely unexplained.

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 http_headers lookup' essentially restates the tool name with the verb 'lookup' and the provider, but does not explain what http_headers does (e.g., retrieving HTTP headers for a given target). It does not distinguish from sibling tools beyond the name, making it a near-tautology.

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?

There is no guidance about when to use this tool versus alternatives like dns_lookup or whois. The description provides no context, prerequisites, or examples of appropriate 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?

The annotations already establish this as a safe, read-only, idempotent operation, so the description does not need to disclose that. It adds value by specifying the response scope ('the caller's active subscriptions') and the exact return fields, which are useful behavioral details beyond the annotations. 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 two sentences: the first states the purpose and the return values, the second gives usage guidance. Every word earns its place, and the key information is front-loaded. This is a model of conciseness.

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

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

For a simple list tool with one optional parameter and no output schema, the description fully covers the purpose, scope, return fields, and common use cases. It is complete and leaves no important gaps for an agent to invoke it correctly.

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

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

The only parameter (include_inactive) is fully described in the schema with a clear description, and schema coverage is 100%. The tool description does not add any extra parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('List') and the resource ('the caller's active subscriptions'), and it enumerates the return fields (id, type, params, etc.), which distinguishes it from sibling tools like subscribe and unsubscribe. This is a specific and unambiguous statement of what the 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?

The second sentence provides explicit context for when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It does not mention when not to use it or name alternatives, but the context is clear enough given the sibling tools (subscribe/unsubscribe). This earns a 4 rather than a 5 because it lacks explicit when-not/exclusion 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 readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. However, the description adds no behavioral context beyond the annotations—it does not mention external service dependencies, rate limits, or any side effects. It is consistent with annotations but adds no 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 short but under-specified rather than concise. A single phrase like 'HackerTarget mtr lookup' does not earn its place because it provides no meaningful information. This is closer to a placeholder than a well-structured 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?

With a single parameter, no output schema, and minimal annotations, the description still fails to provide essential context such as what mtr does, what kind of output to expect, or how it differs from the sibling 'traceroute' tool. It is minimally viable but leaves major gaps for an agent trying to decide when to invoke it.

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

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

Schema description coverage is 0%, so the description must compensate for the undocumented 'target' parameter. The description only says 'HackerTarget mtr lookup' and fails to explain what the 'target' should be (e.g., IP address, hostname, domain). This is completely inadequate for an agent to understand how to fill 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 'HackerTarget mtr lookup.' is essentially a tautology, restating the tool name 'mtr' and adding 'lookup' without explaining what mtr does. It does not distinguish this tool from siblings like 'traceroute' or 'dns_lookup'. The verb 'lookup' is present but the resource 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?

The description provides no guidance on when to use this tool versus alternatives such as 'traceroute' or 'ping'. There is no mention of context, prerequisites, or exclusions. The agent is left without any information to select this tool correctly.

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

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds no behavioral context beyond stating the external source 'HackerTarget'. It does not explain what the tool does operationally (e.g., sends ICMP packets, checks host reachability, returns latency metrics) or any limitations. There is no contradiction with annotations, but the description contributes no meaningful transparency.

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

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

The description is extremely short and front-loaded, but it is under-specified rather than concise. It consists of only three words, which is not enough to convey meaning. This is similar to the calibration example where under-specification receives a score of 2, not 5.

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

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

Given a single parameter with no schema description, no output schema, and no sibling differentiation, the description is completely inadequate. It does not explain what the tool does, what input to provide, or what output to expect. Even for a simple tool, this is not enough information for an agent to select and invoke it correctly.

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

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

Schema description coverage is 0% and the only parameter 'target' is undefined in both the schema and the description. The description does not clarify whether 'target' is a hostname, IP address, or URL, nor does it explain the expected format or possible values. The description completely fails to compensate for the lack of schema documentation.

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.' is essentially a tautology: it restates the tool name 'nping' and adds the generic verb 'lookup' without specifying what nping does, what resource it targets, or what kind of lookup (e.g., host availability, latency, packet loss). It does not distinguish this tool from network diagnostic siblings like mtr, traceroute, 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?

There is no guidance on when to use this tool versus alternatives. The description provides no context about typical use cases, prerequisites, or exclusions. Sibling tools such as mtr and traceroute exist, but the description gives no basis for choosing nping over them.

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 annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint safely, so the safety bar is lowered. However, the description adds no behavioral context beyond what annotations provide—no mention of return format, data source specifics, or API limitations. It adds zero value on top of 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 this is under-specification rather than effective conciseness. It consists of a single phrase that repeats the tool name without earning its place or conveying operational 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?

With a single param, no output schema, and a one-line description, the tool is severely under-documented. The agent cannot infer what input format to use or what results to expect. It is completely inadequate 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 required string with no description in the schema (coverage 0%). The tool description also fails to explain what 'target' means (e.g., domain, IP, URL). The agent has no information to construct a valid input.

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' essentially restates the tool name with the generic verb 'lookup'. It does not explain what page_links returns or how it differs from sibling tools like dns_lookup or reverse_dns. This is closer to a tautology than a clear statement of 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?

There is no guidance on when to use this tool or when to prefer an alternative. The description provides no context about typical use cases, prerequisites, or exclusions. The agent is left without any decision-making support.

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 discloses rate limiting and quota exemption: 'Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.' This is behavioral context not visible in annotations, which are all false. No contradiction with annotations is present.

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?

Each sentence earns its place: purpose, usage scenarios, message formatting rule, impact, and rate limit. It is front-loaded with the core action and maintains a compact, structured format.

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 feedback nature, the description covers all essential aspects: purpose, when to use, parameter semantics, and operational constraints. No output schema is present, but the description and schema fully prepare the agent to invoke 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?

The schema already provides 100% parameter coverage, including enum descriptions and message specifications. The description adds meaningful guidance on message composition—'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt'—beyond the schema's generic 'Be specific.' This enhances parameter usage, so a 4 is warranted.

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 opens with a clear verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback categories (bug, feature, data_gap, praise) that map to the enum, making the tool's purpose unmistakable and distinct from sibling tools.

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

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

The description explicitly states when to use the tool: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also provides negative guidance: 'don't paste the end-user's prompt' and 'Describe the issue in terms of Pipeworx tools/packs.'

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 (read-only, idempotent), the description discloses that the data is self-aggregating from CF analytics-engine, contains no PII, consists only of (pack, tool, count) tuples, and is cached 5min-1h. This provides valuable context about data freshness and privacy.

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 compact and well-structured: it states the core purpose, lists return contents, gives a bulleted list of use cases, and ends with data-source and caching notes. 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?

For a simple read-only tool with one optional parameter and no output schema, the description provides sufficient detail: what is returned, why it's useful, data provenance, and caching behavior. It gives the agent enough context to decide when to call it and what to expect.

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

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

The schema already provides a detailed description of the single 'window' parameter, including default and trade-offs between short and long windows. The tool description merely echoes the window options without adding new information, so it meets the baseline for high schema coverage.

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

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

The opening phrase 'What other AI agents are calling on Pipeworx right now' clearly states the tool's purpose. It further specifies it returns top tools, top packs, and total call volume over selectable windows, distinguishing it from sibling discovery 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 explicitly lists three use cases (discovering hot data sources, confirming canonical tool choice, and aligning with agent needs), providing clear when-to-use guidance. The window parameter's semantics are also explained both in the description and schema.

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 and non-destructive behavior, but the description adds substantial behavioral context: the partition filter drops placeholder slugs, the semantic anchor requires ≥0.30 Jaccard similarity, and the fill check explicitly advises 'do not trade it' when realizable_edge_pp ≤ 0. This goes far beyond the annotations without contradicting 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 long, but it is front-loaded with the core purpose and uses capitalized section headers (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) to organize dense information. Every sentence adds value, though the length could overwhelm an agent that just needs a quick overview.

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 fully accounts for return values ('Response: opportunities[]...'), edge cases (realizable_edge_pp ≤ 0), and mode-specific behavior. It even mentions tracking fields like skipped_low_similarity and placeholders_filtered. This is complete for an arbitrage-scanning 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?

While both parameters have schema descriptions, the main description clarifies what each parameter does, provides concrete examples, and distinguishes the no-args mode. It adds meaning by explaining how event mode 'walks child markets' and topic mode 'searches related events across the platform,' which is absent from 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 opens with a clear verb+resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It further distinguishes between three modes (trending_scan, event, topic) and references the sibling tool polymarket_fill_risk for custom sizing. This separates it from other Polymarket tools like polymarket_edges 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?

Explicit guidance is given for when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', and 'topic (for cross-event scanning)'. It also names an alternative: 'For custom sizing use polymarket_fill_risk.' This satisfies the when/alternative guidance clearly.

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, and non-destructive. The description adds extensive behavioral details: cache behavior, diagnostics for empty segments, edge calculation after slippage, Kelly caps, 24h-move warnings, and filtering knobs. This goes well beyond the 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 long but densely packed with useful information, organized into segments and knobs. It front-loads the purpose and then details behaviors. No filler sentences, though length might be slightly intimidating. It earns its length given the tool's 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?

No output schema exists, so the description fully explains the response structure (by_segment, fed_candidates, _diagnostics) and what callers can expect. It covers edge metrics, warnings, and why segments might be empty. This is a complete description for a complex 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%, so baseline is 3. The description adds extra context for key knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly) explaining how they filter tradeable edges and why min_kelly doesn't filter partitions. This enriches the parameter semantics 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 that the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It specifies the three model families and the intended use case ('what should I bet on today'), making it distinct from sibling tools like polymarket_arbitrage or polymarket_edge_tracker.

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 a clear use case: agents discover opportunities without paging hundreds of markets. However, it does not explicitly compare to alternatives or state when not to use this tool, though the detailed segment explanations imply a discovery-oriented purpose.

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 readOnlyHint annotations, the description adds significant behavioral details: snapshot TTL of 60 days, cache-miss write behavior causing gaps, decay computed on daily closes net of default slippage, the meaning of negative edge_pp_net (SELL YES), and the full response shape (tracked, expired, snapshot_dates). No contradiction 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 long but well-structured with 'Args:', 'RESPONSE:', and 'LIMITS:' labels, front-loaded with the core purpose. Every sentence provides unique information such as the trade interpretation, response fields, and limitations. It is not maximally concise, but the density is justified for a tool with no output schema and complex telemetry semantics.

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 absence of an output schema, the description is remarkably complete. It fully specifies response fields (tracked, expired, snapshot_dates), explains data freshness limits (TTL, cache-miss gaps), and clarifies computational methodology (daily closes, net of slippage, trend classification). It also provides the competition-clock heuristic, leaving little ambiguity for the agent.

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

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

The input schema already covers both parameters with descriptions (days: default 14, clamp 2-30; window: which family with allowed values and default). The description's 'Args:' line is mostly redundant, adding only 'max 30' which is already present in the schema as 'clamp 2-30'. Thus the description adds marginal value beyond the 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 opens with a specific verb and resource: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It directly answers 'how long has this edge existed and is it shrinking?' which distinguishes it from siblings like polymarket_edges (current edge) by focusing on time-series and decay. The distinction is further emphasized with the trade example.

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 strong contextual guidance: 'a fresh wide edge and a 3-week-old wide edge are different trades' explains when the tool is valuable, and the 'LIMITS' section clarifies data availability (TTL, cache-miss gaps). However, it does not explicitly state when to prefer this over sibling tools like polymarket_arbitrage or polymarket_fill_risk, so it is clear but not fully explicit.

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

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

Annotations declare readOnly, idempotent, and non-destructive, and the description does not contradict them. Beyond annotations, it discloses behavior: walks the order-book ladder, returns fill analysis fields, identifies thin legs and forced directional risk. It also clarifies that partial basket fills can convert an arb into an unhedged directional position, which is crucial 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 long but structured with SINGLE-MARKET and BASKET sections, and each sentence contributes operational detail. It is front-loaded with the core definition and ends with actionable usage guidance. However, it could be slightly more scannable with bullet points, so it loses one point for density without sacrificing 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?

No output schema is present, so the description carries the full burden of explaining return values. It enumerates key output fields for both modes (e.g., top_of_book, vwap_fill_price, capture_ratio, profit_usd, thin_legs, max_clean_notional_usd) and explains the risk rationale. This is comprehensive for a tool of this complexity.

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

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

Schema coverage is 100%, but the description adds meaning beyond the schema by explaining how each parameter behaves in each mode. For example, it explains that 'side' defaults auto in basket mode based on partition sum, and that 'size_usd' is interpreted as settlement notional (shares per leg) in basket mode. This enriches 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 opens with a specific verb+resource: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It clearly distinguishes two modes (single-market and basket) and differentiates from siblings by explicitly naming polymarket_arbitrage and polymarket_edges as tools to precede.

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 usage guidance is given: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround not capturable, partial basket fills create directional risk) and what to expect in return, offering clear when-to-use and inherent exclusion logic.

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 and destructiveHint=false, but the description goes far beyond this. It discloses compatibility_warning conditions, temporal_alignment meaning, skipped_cross_type/subtype counters, and the interpretation of matched_pairs:0. This rich behavioral context is exactly what an agent needs to correctly interpret 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 long but densely structured: it front-loads the purpose, then modes, response, and safety fields. Each section earns its place, though the ending repeats 'pre-mapped ≠ tradeable' and the compatibility_warning explanation could be slightly tightened. Overall, the length is justified by the tool's 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?

With no output schema, the description carries full responsibility for explaining return values, and it does so thoroughly: leg-by-leg prices, spread values, compatibility_warning cases, temporal_alignment, and skipped counters. It also addresses edge cases (matched_pairs:0) and real-world expectations (rarity of tradeable spreads). This is complete for a complex cross-venue 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%, so the parameters are already documented. The description adds value by explaining how topic auto-fetches matching events, how kalshi_event_ticker and polymarket_event_slug override the mapped side, and what 'custom pairings' means. It also gives concrete examples (fed, btc), making parameter usage concrete.

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 opens with a specific verb+resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly distinguishes from sibling tools like polymarket_edges (single-venue) and polymarket_arbitrage (intra-venue) by emphasizing cross-venue comparison. The two modes and tradeability caveat further sharpen the 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?

The description explicitly explains two usage modes (topic shortcuts vs explicit pairings) and provides critical guidance that most pre-mapped topics return compatibility_warning and are not tradeable. It does not explicitly name alternative tools or provide a when-not-to-use section, but the context is strong and actionable.

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 valuable behavioral context beyond this: scoping to an identifier (anonymous IP, BYO key hash, or account ID) and the two distinct operational modes (retrieve a value vs. list all keys). This enriches the agent's understanding without being verbose.

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, each serving a distinct purpose: function, use case, and scope/pairing. It is front-loaded with the verb and resource, contains no filler, and every sentence contributes valuable information.

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

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

Given the tool's simplicity (1 optional parameter, no output schema) and informative annotations, the description fully covers what, when, why, and how it relates to other tools. It also clarifies privacy/scope. No significant 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?

The schema covers the single 'key' parameter fully ('Memory key to retrieve (omit to list all keys)'). The description adds semantic depth by explaining what kind of values are stored (user's target ticker, address, research notes) and linking the key to the 'remember' tool. This goes beyond the schema's 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 opens with a specific verb and resource ('Retrieve a value previously saved via remember') and clearly explains the second mode ('list all saved keys'). It distinguishes itself from siblings by explicitly referencing remember and forget, and provides concrete examples of stored context.

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

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

The description explicitly states when to use the tool ('Use to look up context the agent stored earlier...') and why it is beneficial ('without re-deriving it from scratch'). It also clarifies the working relationship with sibling tools by instructing to 'Pair with remember to save, forget to delete.'

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 directly contradicts the annotations. Annotations include 'readOnlyHint': true, but the description says 'Set mark_read:true to flag returned events read so the next call only shows newer ones' — a clear side effect that modifies state. This is an annotation contradiction, so the score must be 1. Even though the description adds useful behavioral context elsewhere, the contradiction overrides.

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

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

The description is only three sentences, front-loaded with the main purpose. Every sentence provides distinct information: purpose/return payload, filtering and mark_read behavior, polling and alternative endpoint. There is no fluff or redundancy.

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

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

Despite having no output schema, the description compensates by explaining the return payload contents. It covers the mark_read side effect, filtering options, polling suitability, and external access. Given the 5 optional parameters and no output schema, the description is very complete for an agent to decide and invoke the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by giving a concrete type example ("sec_8k") and explaining the consequence of mark_read ('so the next call only shows newer ones'), which is not in the schema. This extra semantic detail justifies 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?

The description clearly states what the tool does: 'Pull fired events from your subscription feed' and specifies it returns alerts from the evaluator's persisted feed. It distinguishes itself from siblings by mentioning the feed source, the return payload elements, and even provides an external endpoint for the same 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?

It gives clear usage context: filtering by type and since, mark_read behavior, and polling suitability. It also points out an alternative external access method ('the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards'). However, it does not explicitly compare against sibling tools or 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?

Beyond the readOnly/idempotent annotations, the description reveals multi-source fan-out (SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail), fallback triggers (rate-limited or 5xx), and the return structure (changes[] grouped by source).

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?

A single dense paragraph front-loaded with example queries. Every sentence adds functional value: sources, fallback behavior, parameter formats, and output shape. No unnecessary words.

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

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

Given no output schema, the description fully explains the returned structure (changes[], total_changes, pipeworx:// URIs). It also covers error/fallback behavior and input constraints, making it complete for an agent to select and call the tool.

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

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

Schema already provides 100% coverage with examples for `since` and descriptions for `value`/`type`. The description adds a practical recommendation ('Use 30d or 1m for typical monitoring') and reiterates accepted formats, providing extra guidance beyond the schema 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 it is a 'change feed for a company in the last N days/weeks/months' with specific example queries. It explicitly distinguishes itself from entity_profile by noting the alternative for static profiles.

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 names an alternative tool ('Use entity_profile instead when you want the static profile...') and describes when this tool is appropriate for recent changes. Also provides typical use cases and the scope of the fan-out.

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 idempotentHint=true and destructiveHint=false, and the description adds valuable context: storage as key-value pairs scoped by identifier, 24-hour retention for anonymous sessions, and pairing with recall/forget. It does not contradict annotations, and the added behavioral details go beyond the structured metadata.

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 long, front-loaded with the primary purpose, and every sentence contributes useful information (purpose, usage triggers, persistence, and related tools). It is concise without being under-specified.

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 key-value write tool with no output schema, the description covers all essential aspects: what to store, when to store it, how long it persists, scoping, and how to retrieve/delete. The annotations handle safety, and the description fully supports invocation without requiring additional context.

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

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

Schema coverage is 100% with clear descriptions for both 'key' and 'value' parameters, including examples. The tool description does not add significant param-specific details beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's verb and resource: 'Save data the agent will need to reuse later.' It distinguishes itself from related tools by explicitly pairing with 'recall to retrieve later, forget to delete,' making its role unique among siblings.

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

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

The description provides explicit guidance on when to use the tool ('Use when you discover something worth carrying forward') with concrete examples (resolved ticker, target address, user preference). It also mentions persistence differences between authenticated and anonymous sessions. However, it does not explicitly state when not to use it, though this is implied by 'worth carrying forward.'

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, openWorld, idempotent, and non-destructive behavior. The description adds meaningful context beyond that: it cascades through multiple endpoints, auto-disambiguates inputs, and returns citation URIs. It doesn't cover potential caveats like ambiguity failure modes or rate limits, but given the annotations, this is a solid disclosure.

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

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

The description is well-structured: opening examples, core statement, 'Use FIRST' guidance, then clearly labeled 'SUPPORTED TYPES'. It is somewhat lengthy but every sentence carries necessary information, and the formatting improves scannability. It could be trimmed slightly, but it's not bloated.

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 there's no output schema, the description takes full responsibility for explaining return values, and it does so thoroughly for both entity types: precise outputs (ticker, CIK, RxCUI, ingredient, brand) plus citation URIs. It also communicates the internal cascading behavior and the benefit (replacing multiple lookups), making it complete for a tool of this complexity.

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

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

Schema coverage is 100%, so a baseline of 3 applies. The description adds value by clarifying accepted input formats (ticker, CIK, name for companies; brand/generic for drugs) and specifying exactly what each type returns (e.g., 'returns ticker + 10-digit CIK + company_name'). This goes beyond the schema's generic field 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 opens with concrete usage examples ('What's the ticker for…'), then states the core function: resolving a user-spoken name to the canonical identifier other tools require. This is a specific verb+resource with clear scope, and it distinguishes itself from sibling tools like entity_profile (which likely returns profiles, not identifiers) and as_lookup (which may be AS-number 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?

It explicitly says 'Use FIRST whenever you have a name but need an ID', giving a clear condition for when to invoke. It also lists supported entity types and mentions that it replaces 2-3 manual lookups, implying a preference over chaining other tools. However, it doesn't explicitly state when NOT to use it or name alternative tools for cases like needing full entity details rather than identifiers.

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=true, destructiveHint=false, and idempotentHint=true, but the description adds no additional behavioral context beyond repeating the lookup nature. It does not disclose potential network dependencies, rate limits, or that it queries an external service (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 short, but this is under-specification rather than conciseness. It is a fragment that lacks structure or explanatory sentences. Every word is essential but crucial information is omitted.

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 fails to convey even the basic input requirement or expected result. It does not state what the input represents, what the output looks like, or any limitations. The overall context is drastically incomplete.

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 the 'target' parameter at all. The user is left without knowing whether target is an IP address, domain, or something else, and no format or examples are given.

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' clearly indicates the tool performs a reverse DNS lookup, distinguishing it from siblings like dns_lookup (forward DNS) and reverse_ip (IP to domains). However, it is merely a noun phrase rather than a full statement of action, and relies on the tool name for clarity.

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. It does not mention prerequisites, appropriate input types (e.g., IP addresses), or when reverse DNS is preferable to other lookups. No alternative tools are referenced.

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, idempotentHint=true, and destructiveHint=false, so safety is covered. However, the description adds no behavioral context beyond naming the HackerTarget source. It does not mention potential rate limits, external API dependencies, or response format, which are not disclosed by 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 short but under-specified. It restates the tool name and fails to convey necessary details, making it more of a placeholder than a concise, informative description. Every word does not earn its place because the sentence lacks substantive 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's simplicity (one parameter, no output schema) and the available annotations, the description is incomplete. It does not clarify what input the tool accepts or what output it produces, making it difficult for an agent to select and invoke it correctly without prior knowledge.

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

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

The schema has one required parameter 'target' with no description, and schema description coverage is 0%. The tool description also fails to explain what 'target' should contain (e.g., an IP address) or its format. This leaves the agent without essential parameter semantics.

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.' is essentially a restatement of the tool name, with only the addition of the data source (HackerTarget). It does not explain what a reverse IP lookup does, what it returns, or how it differs from similar 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 usage guidance is provided. The description does not state when to use this tool, what input is expected, or how it compares to alternatives such as reverse_dns, dns_lookup, or as_lookup. The agent must infer usage entirely from the tool name.

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 and idempotent, so safety is covered. The description adds operational detail by noting it 'Probes each entity with ai_visibility_check, ranks by score' and returns a 'ranked list with score, confidence, signal density per entity,' which goes 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?

Three sentences, front-loaded with the core action, followed by how it works, a use case, and the return format. Every sentence adds value with no redundancy or fluff.

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

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

With no output schema, the description properly explains the return value ('ranked list with score, confidence, signal density'). It covers purpose, usage context, and operational mechanism. Minor omissions like external API dependencies are inferred from the schema, not the description, but overall it is complete enough for a tool of this complexity.

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

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

Schema coverage is 100% with full parameter descriptions, so the baseline is 3. The description doesn't add meaning beyond the schema; for instance, the note about first entity as subject is already present in the schema's entities 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 uses a specific verb+resource phrase: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes from the sibling ai_visibility_check by focusing on multi-entity comparison and ranking, making the 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?

It provides concrete context with 'Useful for competitive AI-marketing audits' and an example question, signaling when to use it. While it doesn't explicitly exclude single-entity use or name alternatives, the context is sufficiently clear to guide selection.

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 discloses composite behavior across two external services, partial failure degradation ('sources_failed will list it if it times out'), latency expectations ('5-30s' for first bundlephobia measurement), and a detailed list of return fields. This goes well beyond the readOnlyHint and idempotentHint annotations, providing actionable context for agents.

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

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

The description is front-loaded with the core purpose, and every sentence adds value: data sources, usage triggers, return structure, ecosystem scope, and failure handling. It is appropriately sized for the tool's complexity 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 no output schema and a moderate complexity (composite call, partial failures), the description is remarkably complete. It details return fields, failure modes, ecosystem limitations, latency, and alternative routes, ensuring the agent has a full operational picture.

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?

Since schema description coverage is 100%, baseline is 3. The description does not add meaning beyond the schema for the 'package' and 'version' parameters; the schema already covers the defaults and scoped package acceptance. No additional parameter semantics are provided.

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

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

The description clearly identifies a composite npm package evaluation tool, explicitly stating the question it answers ('should I add this npm package to my project') and the data sources consulted (deps.dev, bundlephobia). It is distinct from sibling tools focused on DNS, research, or network analysis, establishing a unique role.

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 ('whenever an agent asks "is X safe / popular / small"') and provides an alternative for non-npm ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This gives clear usage triggers and 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?

Beyond the read-only/idempotent annotations, the description reveals concrete behavior: every passage carries a character offset for verification, embeddings use BGE-base-en with cosine over 500-char overlapping windows, and inputs over 200K chars are truncated and flagged. These are valuable operational details that an agent cannot infer from annotations alone.

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 information-dense but not verbose. Each sentence contributes: core purpose, usage trigger, pairing guidance, and technical internals. It is slightly long but well organized, making it easy to scan. It could be tightened by removing the embedding model details, but they are relevant for understanding truncation and scoring behavior.

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 there is no output schema, the description adequately explains what the agent receives: 'top-N passages with character offsets and similarity scores.' It also covers edge behavior (truncation flag) and integration with a sibling tool. For a search-within-record tool with 3 parameters and no output schema, this is a complete picture.

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 the baseline is 3. The description adds value by giving concrete examples for the 'query' parameter (e.g., 'supply-chain risk', 'fiscal year 2024 revenue') and explaining the relationship between 'text' and 'query' (pass the already-pulled text and ask a natural-language query). It also clarifies the output concept of top-N passages, though it doesn't describe the 'limit' parameter beyond the schema's explicit documentation.

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 begins with a clear, specific action: 'Semantic search INSIDE a fetched record.' It distinguishes this tool from broader search/retrieval tools by scoping it to already-fetched text, and the 'pairs with ask_pipeworx_grounded' note further differentiates its role. The verb+resource combination is 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 when-to-use guidance is provided: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' It also names an alternative/complementary tool (ask_pipeworx_grounded) and explains how to combine them. This clearly directs the agent to the right context for selecting this tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description carries little additional burden. The only added context is the source 'HackerTarget,' which hints at an external service but does not disclose behavior such as rate limits or error handling.

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 useful way; it is a tautological repetition of the name. It provides zero informative content and fails to earn 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?

For a tool with one parameter and no output schema, the description should explain what a subnet lookup does and what it returns. It does neither, making it entirely inadequate for an agent to select or invoke 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?

The schema has one required 'target' parameter with no description. Schema coverage is 0%, and the description does not clarify what 'target' means or how to format it, leaving the agent guessing.

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 is a tautology: 'HackerTarget subnet_lookup lookup' repeats the tool name without explaining what the tool does. No specific verb or resource is described, making it impossible to distinguish from other 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?

There is no guidance on when to use this tool versus alternatives like dns_lookup, reverse_ip, or geoip. No use cases, prerequisites, or exclusions are provided.

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

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

The description adds substantial behavioral details beyond annotations: requires a Pipeworx OAuth account, phone verification for SMS, a 10/day SMS cap, webhook signing secret returned once, and auto-disable after 10 consecutive failures. No contradiction with annotations (readOnlyHint=false, idempotentHint=true).

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, with clear sections for supported types and delivery channels. Every sentence adds value, though it is somewhat lengthy given the complexity. The main purpose is front-loaded.

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

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

The description covers many aspects (account requirements, return value, delivery details) but claims to list supported types and omits patent_grant and clinical_trial, which appear in the schema. This incomplete enumeration is a significant gap given the tool's complexity and lack of an 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?

Despite 100% schema coverage, the description enriches parameter semantics with concrete examples (sec_8k items, polymarket_edge topic, fred_series series_id), delivery channel constraints (E.164 phone, email pattern, webhook HMAC signing), and operational limits (10/day cap, auto-disable). This goes well 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 identifies the tool as creating a proactive monitoring subscription for live-data event streams and notes it returns the new subscription id. This distinguishes it from sibling tools such as list_subscriptions and unsubscribe.

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 the purpose and requirements (e.g., OAuth account) but does not explicitly contrast with alternative tools like list_subscriptions or recent_alerts. The mention of pulling alerts via recent_alerts is about the feed channel, not an alternative for management. Guidance is implied rather than explicit.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that the response is category-bucketed, drawn from a live catalog of thousands of tools, and that calling with no arguments returns the full spread while a topic focuses the output. This additional behavioral context is useful and consistent with 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 a single long run-on sentence with many slash-separated example questions and categories. It contains all necessary information but could be better structured with clear sentences or bullets. While front-loaded with examples, the dense format makes it less scannable than the ideal.

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 one-parameter tool with no output schema, the description covers the purpose, output format, parameter behavior, and a usage trigger. It is rich enough for an agent to know when and how to invoke it, and what to expect. Minor gaps include not mentioning whether the output is truncated or formatted, but given the tool's simplicity, it is nearly complete.

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

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

The schema already documents the topic parameter with all valid values and the omit behavior ('Omit for a cross-category spread'). The description adds only the phrase 'to focus' and a few examples, providing marginal added meaning. With 100% schema coverage, the description does not significantly enhance parameter understanding beyond what the schema already provides.

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

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

The description clearly states that this tool returns category-bucketed example questions with exact tool and argument shapes, serving as an onboarding entry point for understanding Pipeworx. It specifies the resource and scope (what questions to ask) but does not explicitly differentiate from the sibling discover_tools, though it positions 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 provides an explicit when-to-use trigger: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' This gives clear guidance on the appropriate context and even mentions meta-tools as a related learning target, though it does not name specific alternative tools to avoid or contrast.

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, and destructiveHint=false, so the safety profile is covered. The description adds minimal context by naming HackerTarget as the data source, which suggests an external service, but it does not disclose rate limits, failure modes, or other behavioral traits.

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 phrase, 'HackerTarget traceroute lookup.', which is efficient and front-loaded. It wastes no words, though it is slightly under-specified.

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

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

For a tool with no output schema and no parameter description, the description is too sparse. It lacks usage context, return value expectations, and any caveats, making it incomplete for an agent to fully understand the tool's behavior beyond its name.

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 compensate. The parameter 'target' is not explained at all—there is no mention of it being a hostname or IP address. The name itself gives a hint, but the description adds no value beyond the schema.

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

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

The description clearly states it performs a HackerTarget traceroute lookup, using a specific verb ('lookup') and resource ('traceroute'). However, it does not differentiate from similar sibling tools like 'mtr' or 'nping', so it stops short of full distinction.

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. There is no mention of scenarios, prerequisites, or exclusions, leaving the agent without context for tool selection.

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, the description explains ownership enforcement, that the row is deactivated (not deleted), and that historical events remain available via recent_alerts. This adds meaningful behavioral nuance absent from just 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?

Two sentences, front-loaded with the primary action and followed by essential caveats. Every word earns its place, with no redundancy or filler.

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

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

For a single-parameter tool with no output schema, the description covers the action, ownership constraints, the deactivation behavior, and the impact on historical data. It is fully sufficient for an agent to know how and when to invoke it.

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

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

The schema already documents the id parameter fully (including that it comes from subscribe). The description merely says 'by id' without adding semantics beyond the schema, so it meets the baseline for full schema coverage.

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

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

The description clearly states the action ('Cancel a subscription by id') with a specific verb and resource, and it naturally distinguishes itself from siblings like subscribe and list_subscriptions. The purpose is unambiguous and immediately understandable.

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 sets a clear usage condition ('you can only cancel your own subscriptions') and implies the id comes from subscribe, but it doesn't explicitly name alternatives or non-use cases. The context is sufficient given there is only one unsubscribe 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 indicate read-only, open-world, idempotent behavior. The description adds substantial context: it discloses the SEC EDGAR + XBRL fast path, the fall-through grounded pipeline, verbatim evidence gathering, and the specific verdict types returned. It also explains that tolerance_pct overrides claim wording, going 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?

The description is longer than average but information-dense. It front-loads example user phrasings, then states when to use, describes the two routing paths, and lists return values. Each sentence earns its place, though the opening list of example utterances could be slightly trimmed without losing 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 fully describes the return value (verdict options, actual value with citation, reasoning). It also explains the two distinct processing paths for different claim types, which is essential for a tool with high complexity. The description is complete enough for an agent to select and invoke 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% and the schema already describes both parameters with examples, defaults, and constraints. The description does not add meaningful parameter semantics beyond the schema, so the baseline score of 3 is appropriate. The routing details in the description indirectly clarify how the 'claim' parameter is interpreted, but no extra syntax or non-obvious meaning is added.

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: natural-language claim verification against authoritative sources. It uses specific verbs ('validate', 'verify', 'confirm/refute') and resource ('claim'), and differentiates from siblings by noting it replaces 4–6 sequential calls. The two-path routing (SEC EDGAR vs. grounded pipeline) further clarifies its unique role.

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 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear when-to-use guidance. It also explains the automatic routing for company-financial vs. other claims, but does not name alternative tools or explicitly state when not to use it, so it stops short of full alternative differentiation.

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

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

Annotations clearly indicate a read-only, idempotent, non-destructive operation, but the description adds no behavioral context beyond the name. It does not mention the external HackerTarget service, rate limits, or any unusual behavior. The description provides zero value beyond the annotations, so the score is below the baseline 3.

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 this is under-specification rather than effective conciseness. It contains only three words and does not 'earn its place' – it adds no information beyond what is already known from the tool name and title.

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, a single undocumented parameter, and no behavioral context, the description is severely incomplete. The agent cannot tell what the tool does, what input to provide, or what to expect in the response. This is as inadequate as a one-word description.

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

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

The schema has 0% description coverage for the required 'target' parameter, and the description gives no hint about what 'target' should be (e.g., domain, IP, or URL). The agent has no information about parameter format or semantics, making this parameter effectively undocumented.

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 whois lookup' essentially restates the tool name and title. It provides no specific detail about what the lookup entails, what data is returned, or how it differs from sibling tools like dns_lookup or reverse_dns. This is a tautology rather than a clear purpose statement.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any exclusions, prerequisites, or scenarios where this tool is preferred. The agent is left without context for selecting this tool among many similar network-lookup tools.

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