Aviationstack

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds no behavioral details such as pagination behavior, response format, or how search/filter parameters work.

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

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

Extremely concise (one sentence), but this brevity sacrifices necessary detail. Not all sentences earn their place as more information is needed.

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 5 optional parameters and an output schema, the description lacks details on tool behavior, search semantics, and return structure. Incomplete for an AI agent to use effectively.

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

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

Schema coverage is 40% (only iata_code and icao_code have descriptions). The description 'Airline directory.' adds no semantic context for the five parameters, failing to compensate for the low 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?

Description 'Airline directory.' vaguely indicates the tool provides a list or reference of airlines, but lacks a specific verb like 'list' or 'search'. It distinguishes from siblings like 'airports' and 'flights' but does so minimally.

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

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

No guidance on when to use this tool versus alternatives (e.g., 'airports', 'flights', 'routes'). No context about prerequisites or filtering criteria.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context beyond listing output fields.

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

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

The description is a single sentence, front-loaded with the tool's purpose, and lists key data points without wasted words.

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

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

With an output schema present and good annotation coverage, the description is adequate but lacks details on search functionality or usage hints, though examples in the input schema partially compensate.

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 67%, with descriptions for search, iata_code, icao_code, and country_iso2. The tool description does not add extra meaning to parameters; baseline 3 is appropriate.

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

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

The description clearly states it's an 'Airport directory' listing specific fields (name, IATA/ICAO codes, country, GPS, timezone), distinguishing it from sibling tools like 'airlines', 'cities', or 'countries'.

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

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

The description implies usage for airport lookups but provides no explicit guidance on when to use this tool versus siblings, nor any exclusions or prerequisites.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description goes beyond by specifying the default model (Workers AI Llama-3.3-70b, free), explaining the need for an API key for Anthropic, and detailing the return format (per-model fields plus combined view). 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 compact: one sentence for core functionality, one for key details and return format, one for use cases. It is front-loaded with the most important information, and every sentence is informative without redundancy.

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

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

Given the tool has 4 parameters, no output schema, and rich annotations, the description covers the return structure, parameter dependencies, and use cases comprehensively. It provides enough information for an AI agent to select and invoke the tool correctly without additional documentation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the default model behavior, the conditional need for _apiKey, and the purpose of the context parameter for disambiguation. This extra information raises it above baseline.

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

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

The description clearly states the verb 'probe', the resource 'LLMs', and the specific outcome 'score visibility (0-100) per model'. It distinguishes itself from the sibling tool 'scan_competitor_ai_presence' by focusing on per-model scoring rather than a broader scan. The purpose is unambiguous and specific.

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

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

The description lists explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It provides context for when to use the tool. However, it does not explicitly state when not to use it or compare it to alternatives like 'scan_competitor_ai_presence', so it misses some 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?

Description adds significant context beyond annotations: routes to 3775 tools, fills arguments, returns structured answer with citation URIs. Annotations already mark readOnly, openWorld, idempotent. 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?

Well-structured and front-loaded with key recommendation. Slightly verbose with extensive domain list and examples, but each sentence adds value.

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

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

For a complex meta-tool, description covers purpose, usage, behavior, and return format (structured answer + citations). Lacks error handling or format details, but adequate given no output schema.

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

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

Schema coverage is 100% with aliases described. Description reinforces natural language usage and provides diverse examples, adding practical meaning beyond schema.

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

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

Description clearly states it answers factual questions using authoritative structured data, with specific verb 'ask' and resource 'Pipeworx'. It highlights preference over web search and differentiates from general search, though doesn't explicitly compare to sibling 'ask_pipeworx_grounded'. Still, purpose is unmistakable.

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

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

Explicitly advises to prefer over web search for a wide range of factual queries, provides example trigger phrases and concrete examples. Lacks explicit when-not-to-use or comparison to grounded sibling, but overall guidance is strong.

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

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

Beyond annotations (readOnly, openWorld, idempotent), the description reveals behavioral traits: it makes an extra LLM call, extracts answers only from tool results, and returns explicit refusals with specific reasons. 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 well-structured with clear sections for purpose, mechanism, return format, and guidance. While slightly long, every sentence contributes necessary information, earning a 4.

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 fully explains return formats (success and refusal) with detailed fields. It covers routing, cost, and usage context, making it complete for an agent to understand and use the tool correctly.

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

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

Schema description coverage is 100%; all parameter aliases are documented. The description does not add significant meaning 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 defines the tool as a hallucination-resistant answer mode for high-stakes reads. It distinguishes from the sibling 'ask_pipeworx' by specifying the extra cost and when to use each, making the purpose precise and unambiguous.

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

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

Explicit usage guidance is provided: 'Use whenever an answer will be quoted, cited, or acted on... prefer ask_pipeworx for casual lookups.' Also mentions cost trade-off, giving clear when-to-use and when-not-to-use instructions.

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 is exceptionally transparent, covering fan-out logic, response shapes, resolver contract (confidence levels, alternatives, suggestions), parent event extraction, safety mechanisms (low-confidence short-circuit, closed market handling), and resolution-rule risk. Annotations already declare readOnly, idempotent, non-destructive; description adds rich behavioral context beyond annotations.

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

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

The description is comprehensive but verbose (multiple long paragraphs). While well-structured with topical sections, it could be more concise. The first sentence clearly states purpose, but subsequent details about classifiers, fan-out examples, and response shapes add significant length.

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

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

Given the tool's complexity (3 parameters, no output schema, many edge cases), the description is thorough. It covers all aspects: input types, processing logic, output structure, safety checks, dead markets, wide spreads, cancellation rules, and fallback mechanisms. No critical gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining the 'market' parameter accepts slug, URL, or question text; clarifying 'depth' options (quick vs thorough) with source counts; and providing guidance on when to use 'include_raw' (true for full payloads, false for summaries).

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

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

The description explicitly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and outputs (evidence packet + comparison), and clearly distinguishes from siblings like 'polymarket_arbitrage'.

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

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

Usage guidance is explicit: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides detailed instructions on inspecting resolver confidence before trusting analysis, handling low-confidence matches, and checking cancellation rules.

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, open-world, idempotent, and non-destructive. The description adds no additional behavioral context (e.g., permissions, rate limits, or handling of missing parameters), but it does not contradict 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 concise (one sentence), but it is too vague. It front-loads the purpose but lacks enough detail to be useful, making it adequate but not optimal.

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

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

Given the presence of an output schema, return values need not be explained. However, the description omits any usage hints, examples, or guidance on the four optional parameters, making it incomplete for effective tool invocation.

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

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

Schema description coverage is only 25% (only the 'iata_code' parameter has a description). The tool description does not mention any parameters or their semantics, failing to compensate for the low coverage.

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

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

The description states it is a 'City + primary-airport database,' which vaguely indicates the tool provides city and airport data, but it lacks a specific verb (e.g., search, list) and does not differentiate from sibling tools like 'airports' or 'countries'.

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. Siblings include airports, countries, and routes, but the description offers no context for choosing this tool over others.

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 idempotentHint=true. The description adds context about data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, and result sorting. 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 thorough but slightly verbose with multiple example queries. However, it front-loads the core purpose and is well structured, making it easy to scan for key information.

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

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

Given the complexity and absence of output schema, the description covers all necessary aspects: what data is returned, how results are sorted, and why this tool replaces multiple lookups. It adequately differentiates from sibling tools like entity_profile.

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 description adds significant value: explains type options ('company' pulls financials, 'drug' pulls FAERS data), validates constraints (2-5 items), and provides concrete examples (e.g., tickers for company, names for drug).

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 side-by-side comparison of 2-5 entities with specific verb 'compare'. It distinguishes from siblings like entity_profile by emphasizing preference over sequential single-pack lookups, making it clear when to use this tool.

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

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

It explicitly says when to use (comparing entities) and to prefer over sequential lookups. It does not formally list alternatives but strongly implies when not to use (single entity lookup). Clear context is provided.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false, so the description's 'reference' aligns. However, it does not describe behavior like pagination or multiple results, relying on annotations for safety.

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

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

Single sentence, not verbose, but sacrifices informativeness for brevity. Could be more structured with parameter hints.

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

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

With 3 optional parameters and no parameter descriptions, the description is too minimal. Output schema exists, so return values need not be described, but usage and parameter roles are missing.

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

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

Schema has 0% description coverage. The description mentions 'capital city, currency' but does not explain how parameters like 'search' or 'country_iso2' work. No new meaning added beyond parameter names.

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

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

The description specifies 'Country reference with capital city, currency, etc.' indicating the resource and some output fields, but lacks a clear verb (e.g., 'retrieve' or 'list') and is vague with 'etc.' It distinguishes from siblings like 'cities' but not sharply.

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

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

No guidance on when to use this tool versus siblings like 'airports' or 'cities'. No explanation of when to use 'search' vs 'country_iso2' parameters.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavioral context: it never invents answers (explicit gaps[]), provides stable citations, decomposes questions, and expects 15-60s runtime. Also notes account and paid plan needs. 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 well-structured: starts with core purpose, then process, then use cases, then prerequisites and expectations. Every sentence adds distinct value, and it's front-loaded with the most critical information. Length is appropriate for 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?

Despite the tool's complexity (multi-source, parallel research), the description covers all key aspects: how it works, what it returns (findings packet with gaps, citations, confidence scores), prerequisites (account, paid plan for depth), and performance expectation (15-60s). No output schema needed for this description to be 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%, but the description significantly enhances meaning: for `depth` it explains the numeric facets per level (quick=3, etc.) and plan requirements, and for `question` it reassures that broad/multi-part questions are fine. This adds clear practical guidance beyond schema types and enums.

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 immediately states "Grounded multi-source research in ONE call" and explains the decomposition and parallel routing process. It clearly identifies the specific verb (research) and resource (multi-source findings), and distinguishes from sibling `ask_pipeworx`.

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

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

Explicitly states "Use for broad or multi-part questions" and contrasts with `ask_pipeworx` (single lookups). Also specifies account requirements and plan tiers for different depths, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is clear. The description adds behavioral context by explaining that the tool returns ready-to-call results with full schemas and examples, and that no second lookup is needed.

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 with a clear opening sentence stating purpose, followed by usage guidance and examples. It is somewhat lengthy but every sentence adds value. It front-loads the core purpose and actionable advice.

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

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

Given the tool's simplicity (1 required param, no output schema), the description is complete: it explains what the tool does, when to use it, what it returns, and how the output is structured (top-N tools with full schemas). No critical information is missing.

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 does not add significant new parameter semantics beyond what the schema already provides (aliases for query are already listed in schema). However, the description does mention 'limit' implicitly in its behavior but not specifically in the description text.

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

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

The description states 'Find tools by describing the data or task', which clearly specifies the verb (find) and resource (tools). It distinguishes discover_tools from sibling tools by framing it as a meta-tool for discovering other tools, rather than a specific data source tool.

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

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

The description explicitly advises when to use it: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set'. This provides clear usage context and prioritization 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 indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true. Description adds behavioral specifics: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), notes USPTO patents API sunset May 2025 with soft-fail behavior, and specifies it returns only US public companies. 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?

Description is front-loaded with usage examples and purpose, then details returns and constraints. Every sentence adds value, though it is slightly verbose due to listing all data sources and caveats. Clean structure but could be trimmed without losing meaning.

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

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

Despite no output schema, description enumerates all returned fields: CIK, company name, recent filings with URIs, fundamentals (10-K revenues/net income/cash), patents (with sunset note), news (with fallback), and LEI. Sufficient for an agent to understand output shape; lacks exact JSON structure but covers key data points.

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

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

Schema coverage is 100% with parameter descriptions already including the constraint that names are not supported and type only 'company'. The description repeats these but adds no new parameter-level information beyond examples. Baseline 3 is appropriate as schema already does heavy lifting.

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

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

Description clearly states it provides a full cross-source profile of a US public company, with specific examples of queries ('Tell me about X', 'research Acme'). It lists exactly what it returns (CIK, filings, fundamentals, etc.) and distinguishes from chaining simpler tools. Siblings like 'resolve_entity' are mentioned as prerequisites for names, clarifying scope.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving clear when-to-use guidance. Also states 'names not supported (use resolve_entity first if you only have a name)' for when-not-to-use, providing an alternative tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds that the tool provides 'real-time and scheduled' data, which aligns with annotations but does not disclose additional behavioral traits 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 a single sentence that front-loads the purpose and filtering capability without any unnecessary words. It is highly concise.

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

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

With 8 parameters, 0 required, and an output schema present, the description covers core functionality and filtering. It does not explain pagination or result structure, but output schema handles that. Reasonably complete for a query tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description mentions 'combine filters,' reinforcing the filtering nature, but adds no significant meaning beyond the schema's parameter descriptions.

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

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

The description clearly states that the tool retrieves real-time and scheduled flights, listing key fields like departure/arrival airport, time, gate, and status. It is distinct from sibling tools like 'airlines' or 'routes'.

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 lacks guidance on when to use this tool versus alternatives. It only mentions filtering capabilities without specifying appropriate contexts 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 indicate destructiveHint=true. Description adds context about clearing sensitive data and when to use, but does not detail error behavior for missing keys.

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

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

Two sentences, no filler. Action-oriented first sentence, usage guidance second. Optimal for quick parsing.

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?

Simple tool with one required parameter. Annotations and description together provide sufficient guidance for safe 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 a clear parameter description. Description adds no extra parameter details beyond what the schema provides.

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

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

Clearly states 'Delete a previously stored memory by key' – specific verb and resource. Distinguishes from siblings 'remember' and 'recall'.

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 lists use cases: stale context, task completion, clearing sensitive data. Advises pairing with sibling tools.

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

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

Annotations already indicate readOnly and idempotent behavior. The description adds context about fetching the page, extracting data, and producing standard format, which is consistent and provides additional behavioral insight.

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

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

Two well-structured sentences: one explaining the action and output, the other listing typical uses. No unnecessary words.

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

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

For a simple tool with 2 parameters and no output schema, the description covers the process, output nature, and use cases adequately.

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

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

Both parameters have full schema descriptions, so the description adds no new semantics beyond examples (e.g., 'https://example.com') and repeating default/max 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 clearly states the tool generates an llms.txt file for any URL, specifies the output format (standard markdown), and distinguishes from sibling tools like 'scan_competitor_ai_presence' and 'ai_visibility_check' by focusing on llms.txt generation.

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

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

The description lists concrete use cases (indexing client sites, drafting llms.txt, auditing competitors) but does not explicitly exclude cases where the tool should not be used.

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, destructiveHint. Description adds no new behavioral traits beyond listing output fields, which is acceptable given annotation coverage.

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

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

Two efficient sentences, front-loaded with action and output, no wasted words.

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

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

Given single optional parameter, no output schema, and rich annotations, description fully covers purpose, output fields, and usage guidance. Complete for its 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?

Single parameter 'include_inactive' is fully described in schema (100% coverage). Description does not add additional meaning beyond schema, so baseline score of 3.

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

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

Clearly states 'List the caller's active subscriptions' and specifies returned fields, distinguishing from sibling tools like subscribe/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?

Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', providing clear usage context. No explicit when-not or alternative tools mentioned, but context is strong.

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

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

The description discloses rate limits (5 per identifier per day) and that it doesn't count against query quota. Annotations are neutral (non-destructive, non-readonly), but the description adds valuable behavioral context beyond those.

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, well-structured paragraph that front-loads the purpose, then covers usage guidelines, tips, and constraints. Every sentence adds value; no redundancy.

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

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

Given the tool's simplicity (submitting feedback), the description covers all necessary aspects: types, context usage, message constraints, rate limits, and impact on quotas. No gaps remain.

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

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

All parameters have descriptions in the schema, and the tool description elaborates on the enum values (e.g., 'bug = something broke or returned wrong data'). It also clarifies that context is optional and how to use it.

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

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

The description clearly states the tool's purpose: to send feedback about bugs, missing features, data gaps, or praise. It differentiates itself from siblings by focusing on feedback, which no other sibling tool does.

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

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

The description explicitly tells when to use each feedback type (e.g., bug for wrong data, feature for missing tool) and provides a clear instruction not to paste end-user prompts. It also notes rate limits and quota exemption.

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 set readOnlyHint, idempotentHint, destructiveHint. Description adds valuable context: data source (CF analytics-engine), no PII, and caching behavior (5min-1h). No contradictions.

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

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

Two paragraphs, front-loaded with main output. Every sentence contributes value with no redundancy.

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

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

Given one optional parameter, rich annotations, and no output schema, the description fully covers purpose, use cases, data origin, caching, and parameter guidance. Complete for 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?

Only one parameter (window) with 100% schema coverage. Description adds decision-making context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds value beyond the enum definition.

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 returns top tools, top packs, and total call volume over recent windows. It is specific about the resource (Pipeworx trending data) and distinguishes from siblings like 'discover_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 lists three explicit use cases and provides guidance on window selection (shorter for hot, longer for steady-state). Lacks explicit 'when not to use' but the guidance is strong.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds algorithmic details (monotonicity, partition check, fill check), constraints (similarity threshold, placeholder filter), and response structure, going far beyond annotations.

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

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

Though lengthy, the description is information-dense and front-loaded with requirement and overview. All sentences add value, but some reorganization (e.g., bullet points) could improve readability.

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

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

Despite no output schema, the description details response fields (opportunities[], partition_check) and explains fill_check and custom sizing via polymarket_fill_risk. Covers constraints, thresholds, and edge cases, making it complete for the tool's complexity.

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

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

Schema descriptions cover both parameters. Description adds examples of valid inputs (slugs, URLs, seed questions) and explains the semantics of each mode, enriching the schema.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two modes (event vs topic) and differentiates from sibling tools like polymarket_edges.

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 required parameters: one of event or topic; recommends event for specific markets and topic for cross-event scanning. Provides examples and notes when cross-event is beneficial.

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. The description adds substantial behavioral context: caching details, edge calculation (edge_pp_net, kelly fractions, 24h-move warning), model specifics, and diagnostics for empty segments. This far exceeds 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 comprehensive but verbose. It front-loads the purpose and then dives into intricate model details, which may overwhelm some agents. It is structured with bold terms and sections, but could be tighter without losing essential information.

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

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

Given the complexity (9 parameters, 3 model families, no output schema), the description is thorough. It describes the response structure (by_segment, fed_candidates, _diagnostics), explains why Fed bets are excluded, includes caveats like the 24h-move warning, and details diagnostics for empty segments. It leaves little to ambiguity.

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 each parameter is described. The description adds extra context for several parameters (e.g., min_partition_leg_kelly, slippage_pp, min_liquidity) with usage examples and rationale. While baseline is 3 due to high schema coverage, the additional explanation 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 the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It targets 'what should I bet on today' agents and distinguishes itself from sibling tools like polymarket_arbitrage by focusing on Pipeworx disagreement and model families.

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 usage context, stating it is for discovering opportunities without paging hundreds of markets, and explains the three model families. It implicitly differentiates from siblings through its unique focus, though it lacks explicit exclusions for when not to use. The detail on Fed candidates and caching offers some 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?

The description adds significant context beyond the annotations (readOnlyHint, idempotentHint, etc.), such as the specific response fields (tracked, expired, snapshot_dates), computation details (decay on |edge_pp_net|), and limitations (60-day TTL, no intraday). No contradictions with annotations.

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

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

The description is well-structured with a clear purpose, argument listing, response format, and limitations. While comprehensive, it is somewhat verbose; a few phrases could be tightened without losing meaning. Nonetheless, each sentence adds value.

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

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

Given the lack of an output schema, the description thoroughly explains the return format (tracked[], expired[], snapshot_dates[]) and their fields (e.g., first_seen, trend, decay_pp_per_day). It also covers limitations like snapshot TTL and data gaps, providing complete guidance for agent usage.

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

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

Schema coverage is 100%, and the description adds essential semantics: default values (14 days, '1wk'), clamp ranges (2-30 days), and clarifies that 'window' refers to the snapshot family. This extra meaning goes well beyond the schema descriptions.

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

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

Description clearly states the tool's purpose as 'Edge persistence and decay telemetry' and answers the specific question 'how long has this edge existed and is it shrinking?'. It distinguishes itself from siblings like 'polymarket_edges' by focusing on time-series analysis of edges, not just current snapshots.

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

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

Provides good context on when to use the tool (comparing fresh vs. aged wide edges) and describes the response structure. However, it does not explicitly state when not to use it or mention alternative tools for similar tasks, leaving some room for ambiguity in 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, so the tool is safe. The description adds context: it walks the order book ladder, returns specific fields, and warns about forced directional risk and uncapturable theoretical overround on thin books. This provides meaningful behavioral insight beyond annotations.

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

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

The description is verbose (over 15 lines) and uses all-caps emphasis, but it is well-structured with clear sections for single-market and basket modes. While every sentence adds value, the length reduces conciseness. A slightly more compact version could improve readability.

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 (two modes, multiple parameters, no output schema), the description is very complete. It explains inputs, outputs (including specific fields like top_of_book, vwap_fill_price, capture_ratio, thin_legs), usage context, and failure modes (partial fills, directional risk). No significant gaps are apparent.

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

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

Schema description coverage is 100%, but the description adds significant meaning: it explains the purpose and default of each parameter in both modes, clarifies the auto-detection logic for side in basket mode, and specifies the clamp range for size_usd. This aids correct parameter selection 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 that the tool performs a realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes between single-market and basket modes, and explicitly references sibling tools (polymarket_arbitrage, polymarket_edges) to differentiate its 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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the consequences of not using it (partial basket fills convert an arb into an unhedged directional position), providing clear usage guidance.

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

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

Beyond the readOnlyHint annotation, the description details compatibility warnings, temporal alignment checks, and skipped cross types, fully disclosing behavior when bet shapes are non-equivalent or events are unrelated.

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

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

The description is relatively long but well-structured with sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded key info. Each sentence adds value, though some could be trimmed for conciseness.

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

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

Given the complexity (cross-venue spread, multiple modes, safety fields) and no output schema, the description thoroughly explains response fields, compatibility conditions, and temporal alignment, making it complete for agent invocation.

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

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

With 100% schema description coverage, the baseline is 3; the description adds value by explaining the topic shortcuts list and overriding behavior, though it mostly complements the schema without deep additional 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 clearly states it calculates a 'cross-venue spread between Kalshi and Polymarket for the same resolving question,' using a specific verb and resource, and distinguishes from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison.

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 outlines two modes (topic shortcuts vs explicit tickers) and warns that pre-mapped topics often return compatibility warnings, providing clear context on when to use each mode, though it lacks explicit alternatives to other 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 indicate readOnly and idempotent. The description adds context: omitting key lists all keys, scope is per identifier, and it is non-destructive. No contradictions.

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

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

The description is concise with two sentences, plus a scope/pairing sentence. It is front-loaded with the primary action. Every sentence adds value without redundancy.

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

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

Given one optional parameter, no output schema, and annotations covering safety, the description is complete. It explains both usage modes, scope, and sibling relationships. No missing critical information.

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

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

Schema coverage is 100% with description for 'key'. The description adds semantic value: explaining that omitting key lists all keys and the purpose of key retrieval. It also provides examples of what can be stored (ticker, address, notes).

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: retrieving a saved value or listing all keys. It uses specific verbs ('retrieve', 'list') and identifies the resource ('value previously saved via remember'). It also distinguishes from siblings 'remember' and 'forget'.

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 when to use the tool: to look up context stored earlier without re-deriving. It mentions scope (identifier-based) and pairing with remember/forget. It lacks explicit 'when not to use' but is clear enough.

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

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

Annotations already indicate safe, read-only operations. The description adds behavioral context like mark_read flag affecting subsequent calls and persistent feed details, exceeding annotation coverage.

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

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

Concise at four sentences, front-loaded with action and return content, then parameter details, then usage advice. No superfluous text.

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

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

Despite no output schema, the description explains key return fields (source, citation_uri, payload). It covers functionality and alternatives adequately, though more format details could be helpful.

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

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

With 100% schema coverage, baseline is 3. The description enhances understanding by explaining mark_read's effect on state and the purpose of type and since filters, adding value beyond 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 specifies the verb 'Pull' and resource 'fired events from your subscription feed', clearly stating the tool retrieves recent alerts. It differentiates from sibling tools like list_subscriptions by focusing on alert data.

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

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

The description explains polling suitability and provides an alternative access method (GET endpoint). It filters by type/since and mark_read behavior but lacks explicit when-not-to-use guidance.

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

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

Beyond annotations (readOnlyHint, etc.), the description details the fan-out to multiple APIs, fallback logic, and return structure (changes grouped by source, total_changes count, citation URIs). 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 detailed and well-structured, starting with usage examples, then mechanics, then alternative. While verbose, all sentences add value; could be slightly more concise but not wasteful.

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

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

Despite no output schema, the description explains the return format. All parameters are fully explained with examples and usage context. The description covers limitations, fallback, and alternative tool, making it complete for this complexity level.

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 descriptions cover 100%, but the description adds significant value: examples for 'since' (e.g., '30d', '3m', '1y'), recommendation to use '30d' or '1m', and clarification that 'value' accepts ticker or CIK.

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

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

The description clearly states the tool provides a change feed for a company over a time window, aggregating from multiple sources (SEC, GDELT/GNews, USPTO). It distinguishes itself from the sibling 'entity_profile' tool, which is 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?

Explicit example queries are given ('What's new with X', 'latest on Y'). The description advises when to use this tool versus 'entity_profile' (for static profiles). It also explains fallback behavior (GDELT→GNews) and soft-failures (USPTO).

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 read-only false, idempotent true, destructive false. Description adds scope (by identifier), persistence details (authenticated vs anonymous), and pairing with recall/forget, going beyond annotations.

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

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

Every sentence adds value: purpose, usage, pairing, storage behavior. Front-loaded with main action. No redundancy.

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

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

No output schema needed. Description covers when to use, what to store, scoping, persistence, pairing. Fully complete for a write tool.

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

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

Schema covers 100% of parameters with descriptions. Description adds examples but does not significantly extend meaning beyond the schema.

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

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

The description clearly specifies the action ('Save data'), the resource ('data the agent will need to reuse later'), and provides concrete examples (ticker, address, preference). It distinguishes from sibling tools like recall and forget.

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?

States when to use ('discover something worth carrying forward') and pairs with alternatives (recall, forget). Lacks explicit when-not-to-use, but provides sufficient context.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds that each call cascades through multiple lookup endpoints, replacing 2-3 manual lookups, which gives extra behavioral context without contradicting 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?

Concise yet comprehensive; every sentence adds value. Starts with example queries to immediately convey usage, then covers supported types and return details efficiently.

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 effectively covers return values and behavior. It mentions auto-disambiguation but could briefly address ambiguous input handling. Overall, complete for a lookup 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%, and the description significantly enriches parameter meaning by detailing the return payload for each type (ticker, CIK, RxCUI, etc.), citation URIs, and accepted input formats. This goes well beyond the schema's basic 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?

Description clearly states the tool resolves names to canonical identifiers, with specific examples and supported entity types. It distinguishes the tool's role as a prerequisite for other tools that require IDs, making its purpose unmistakable.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID,' providing a clear usage context. It does not explicitly list when not to use, but the directive is strong and sufficient for an AI agent.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds no new behavioral context, such as parameter defaults, pagination (implied by 'limit' parameter), or data freshness. Thus it provides minimal added value beyond annotations.

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

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

The description is only 6 words, which is overly terse. While concise, it omits essential information, sacrificing clarity for brevity. Each word is not sufficient to be independently valuable.

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

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

Given 5 parameters with no descriptions and an output schema that is not explained, the description is extremely incomplete. An agent cannot reliably use this tool without external knowledge or trial and error.

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 fails to explain any of the 5 parameters (limit, arr_iata, dep_iata, airline_iata, flight_number). This leaves the agent with no semantic guidance on how to fill in inputs.

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

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

The description states 'Scheduled routes between airports,' which clarifies the resource (routes) and domain (airports). However, it does not distinguish from sibling tools like 'flights' or 'airlines,' leaving ambiguity about what 'scheduled routes' specifically means.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'flights' or 'airlines.' There is no mention of typical use cases, prerequisites, or exclusions, so an agent lacks context for correct invocation.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds process detail (probes each entity with ai_visibility_check) and output format (ranked list with score, confidence, signal density). 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 three sentences, each earning its place. The first sentence delivers the core purpose, followed by an explanation of method and an example use case. No filler 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?

With no output schema, the description adequately explains the return format (ranked list with score, confidence, signal density). It covers the tool's purpose and key parameters. Slightly more detail on expected behavior (e.g., timing or error handling) would push it to 5.

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 schema already describes all parameters. The description adds limited extra meaning: it notes that the first entity is treated as the subject. Baseline score of 3 is appropriate as the description does not significantly enrich parameter understanding 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 uses specific verbs like 'compare', 'probe', 'ranks', and 'surfaces' and identifies the resource as 'AI visibility' across multiple entities. It clearly distinguishes from sibling tools like 'ai_visibility_check' (singular) and 'compare_entities' (generic).

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 a concrete use case ('competitive AI-marketing audits') and an example question. It implies when to use over single-entity checks, but lacks explicit guidance on when not to use or direct mention of alternative tools.

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

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

Discloses partial failure behavior (bundlephobia first measurement delay, graceful degradation with sources_failed). Annotations already cover safety; description adds operational 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?

Description is relatively long but efficiently packed with critical info. Well-structured: overview, usage hint, return details, limitations.

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, description fully explains return structure (summary block, advisories, links, alternatives) and handles complex multi-service behavior.

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

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

Schema coverage is 100%, so baseline 3. Description confirms scoped packages and default version, but adds little beyond 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?

Description clearly states the composite check across deps.dev and bundlephobia, with specific verb 'scan' and resource 'npm package'. Distinct from all 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?

Explicitly says when to use: 'is X safe/popular/small' or 'what does adding lodash cost me'. Also clarifies NPM-only in v1 and directs to deps.dev for other ecosystems.

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 provide readOnlyHint, idempotentHint etc., but the description adds significant behavioral detail: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and passage offsets for verification. 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?

Approximately 7 sentences, well-structured: starts with core action, then use case, then technical details. Every sentence adds value; no fluff.

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

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

Given complexity (3 params, no output schema, limited annotations), the description covers purpose, usage, behavioral details, parameter info, and pairing with sibling. It mentions return format (top-N passages with offsets and scores), which is sufficient for an agent to understand what the tool returns.

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

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

Input schema has 100% coverage for all 3 parameters. The description adds value by explaining the purpose of limit (top-N), giving query examples, and clarifying text size cap and truncation behavior, which goes beyond the schema descriptions.

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

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

The description clearly states it performs semantic search inside a fetched record, specifies the action and resource, and distinguishes from siblings by mentioning when to use it (record too large for prompt) and pairing with ask_pipeworx_grounded.

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 the record is too big to cram into the prompt'. Also mentions pairing with ask_pipeworx_grounded. Does not explicitly state when not to use, but the context is clear enough for an agent to decide.

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 idempotentHint=true and destructiveHint=false. The description adds valuable behavioral details not in annotations, such as the 10/day SMS cap, phone verification requirement, and webhook HMAC signing. There is 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 information-dense but well-structured, front-loading the main purpose. Every sentence adds value, though it could be slightly more concise without losing detail.

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

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

Given the tool has 3 parameters (one with nested objects) and no output schema, the description is remarkably complete. It covers auth requirements, subscription types, parameter examples, delivery options, limits, and webhook details.

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

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

Schema coverage is 100%, so parameters are documented. The description enhances understanding by providing concrete examples for each subscription type (e.g., items for sec_8k, topic for polymarket_edge) and specifying constraints like required fields for clinical_trial.

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 creates a proactive monitoring subscription to a live-data event stream and returns the new subscription ID. It lists supported subscription types with examples, distinguishing it from sibling tools like 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 provides clear usage context, including the requirement for a Pipeworx OAuth account and details on delivery channels. While it doesn't explicitly state when not to use the tool, the context is sufficient for correct invocation.

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

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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds value by detailing output behavior: returns example questions and tool shapes, and mentions the optional parameter effect. 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?

Front-loaded with common user queries and compact but informative. Every sentence adds value, though slightly longer than minimal.

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

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

No output schema, so description compensates by explaining the return structure: category-bucketed examples with tool+argument shapes. Adequate for a basic discovery 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 3. The description adds concrete examples for the topic parameter (e.g., 'finance', 'pharma'), which is not an enum in the schema, and explains that omission gives a cross-category spread.

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

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

The description specifies the tool's role as the onboarding entry point, returning category-bucketed example questions with tool+argument shapes. It distinguishes from siblings by advising to use it first when unfamiliar with Pipeworx, setting it apart from tools like discover_tools or ask_pipeworx.

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: 'first when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Provides clear invocation patterns: 'Call with no arguments for the full spread, or pass topic to focus'.

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 value beyond annotations by explaining the deactivation (not deletion) behavior and ownership enforcement. Annotations provide readOnlyHint=false, destructiveHint=false, idempotentHint=true, and the description aligns with these while adding concrete 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 extremely concise with two sentences, no wasted words, and the key action is front-loaded. Every sentence adds value.

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

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

For a simple one-parameter tool with no output schema and annotations present, the description covers usage, ownership, and side effect. It could mention error cases or idempotency effect but is otherwise complete.

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

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

Schema description coverage is 100%, and the parameter 'id' already has a clear description in the schema. The description does not add extra semantic information beyond what the schema provides, earning a baseline score of 3.

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

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

The description clearly states the action 'Cancel a subscription by id', using a specific verb ('Cancel') and resource ('subscription'). It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by being the inverse operation.

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

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

The description provides context: ownership is enforced (only cancel own subscriptions) and the row is deactivated, not deleted. It implicitly tells when to use it but does not explicitly mention alternatives or when not to use it.

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

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

Annotations already declare readonly, idempotent, non-destructive. Description adds: returns verdict categories, structured form, actual value with citation, percent delta. Informs that it replaces 4-6 sequential calls. 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?

Four sentences, efficient in conveying purpose, scope, output, and benefits. Could be slightly more concise, but no wasted words.

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

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

Given one parameter, no output schema, and moderate complexity (NLP + data lookup), description covers input format, scope, and output details (verdict, structure, citation). Adequate for 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 covers 'claim' parameter with description. Description adds natural-language examples and context that only company-financial claims for public US companies are supported, enriching understanding beyond schema.

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

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

Description uses specific verb 'validate' with resource 'claim', provides natural-language trigger phrases, and specifies domain (company-financial claims via SEC EDGAR). Clearly distinguishes from sibling tools which are mostly search, research, or entity 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?

States 'Use whenever the agent needs to check whether something a user said is factually correct.' Explicitly scoped to company-financial claims; no contradictory guidance. Lacks explicit when-not use cases but limitations are clear.

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