flights

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

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds cost implications, API key handling, and return structure, 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?

Five sentences that front-load the core purpose, include key details, and avoid fluff. 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?

No output schema, but description outlines per-model and combined return fields. All 4 parameters are explained with examples. Lacks error handling or rate limit info, but acceptable given the tool's scope.

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

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

Schema coverage is 100% so baseline is 3. Description adds concrete examples for entity, clarifies models options, API key requirement, and context usage, providing clear semantics beyond the schema.

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

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

Clearly states the tool probes multiple LLMs for AI visibility of an entity and returns a score. Distinct from siblings like scan_competitor_ai_presence which scans competitor presence.

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

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

Explicitly mentions use cases like AI-marketing audits and pre-launch brand checks. Provides details on default vs paid models but lacks explicit when-not-to-use guidance compared to siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: Pipeworx picks the right tool and fills arguments automatically, and it returns results. However, it lacks details on limitations, such as rate limits, error handling, or what happens if no data source is found, which is important for a tool with no 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 appropriately sized and front-loaded, starting with the core purpose. Every sentence adds value: explaining the tool's function, its automation benefits, and providing concrete examples. There is no wasted text, making it efficient and easy to understand.

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 (natural language querying with automated tool selection) and lack of annotations and output schema, the description is moderately complete. It covers the purpose and usage well with examples, but does not fully address behavioral aspects like error cases or output format, which could be important for an AI agent to use it correctly.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter 'question' as 'Your question or request in natural language.' The description adds value by providing examples (e.g., 'What is the US trade deficit with China?'), which illustrate the parameter's use, but does not add significant semantic 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?

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes from siblings by emphasizing natural language input without needing to browse tools or learn schemas.

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

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

The description provides clear context on when to use this tool: for asking questions in plain English to get answers from data sources, without needing to browse other tools. However, it does not explicitly state when not to use it or name specific alternatives among siblings, though it implies it's for general queries versus more specialized tools like 'get_flights_in_area'.

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 and non-destructive behavior. The description adds behavioral context: it resolves the market, classifies the bet type, fans out to appropriate data packs, and returns an evidence packet with a market-vs-model comparison. This goes beyond what annotations alone 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 two sentences, front-loaded with the main purpose. The second sentence is somewhat verbose with marketing language, but overall it is efficiently structured for an agent to quickly grasp the tool's function.

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

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

The description explains the tool's workflow (resolve, classify, fan out, return comparison) and output type. However, it lacks details on the exact format of the evidence packet or the comparison, which would help an agent parse the response without an output schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces parameter semantics by explaining the 'depth' default and acceptable 'market' formats, but does not add substantial new meaning beyond the schema.

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

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

The description clearly identifies the tool as researching Polymarket bets by pulling Pipeworx data. It specifies the input formats (slug, URL, or question text) and states the core use case, distinguishing it from generic data query tools like ask_pipeworx by emphasizing that it classifies the bet and fans out to relevant data packs.

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

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

The description provides explicit usage scenarios: 'should I bet on X?', 'what does the data say about this Polymarket market?', and 'is there edge in this bet?'. It does not explicitly mention when NOT to use it or list alternative tools, but the context implies this is the primary tool for bet research.

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

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

No annotations are provided, so the description carries full behavioral disclosure burden. It mentions the return includes paired data and resource URIs, and lists fields for each type. It does not cover potential errors, rate limits, or authentication needs, but the read-only nature is implied.

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

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

The description is two sentences plus structured type-specific details. It is front-loaded with the core purpose and contains no redundant or irrelevant 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 absence of an output schema, the description explains return fields for each type and mentions paired data and URIs. It lacks details on error handling or invalid inputs, but covers the essential context for using the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description reiterates parameter meanings already in the schema (e.g., tickers/CIKs for company, names for drug) but adds no new semantic nuance beyond the schema context.

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

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

The description clearly states the tool compares 2-5 entities side by side, specifies the two entity types (company and drug) with distinct data fields, and explicitly mentions it replaces 8-15 sequential calls. This distinguishes it from sibling tools like resolve_entity 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?

The description implies usage for cross-entity comparison when efficiency matters, stating it replaces multiple calls. However, it does not explicitly state when not to use it or mention alternatives like sequential individual lookups.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search function that returns relevant tools with names and descriptions, and it specifies the 'FIRST' call recommendation. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be helpful for a search tool.

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

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

The description is perfectly concise and front-loaded. The first sentence states the core functionality, the second explains the return value, and the third provides crucial usage guidance. Every sentence earns its place with no wasted words or redundant information.

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

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

Given the tool's moderate complexity (search function with 2 parameters) and the absence of both annotations and output schema, the description does a good job of explaining what the tool does and when to use it. However, it doesn't describe the format or structure of the returned tool information, which would be helpful since there's no output schema. The 'FIRST' call guidance is particularly valuable context.

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

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

With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add any additional parameter semantics beyond what's in the schema - it mentions the query concept generally but doesn't provide format details or usage examples beyond what the schema already contains. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from sibling tools by mentioning it's for when you have '500+ tools available' - unlike the sibling tools which are all flight-related. It provides a clear action and scope.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and distinguishes it from alternatives by positioning it as a discovery tool rather than a direct data retrieval tool like the sibling flight tools. It gives clear context for usage.

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

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

No annotations provided, so description carries full burden. It explains the tool returns pipeworx:// citation URIs and mentions performance (too slow to bundle for federal contracts). Could be more explicit about read-only nature, but overall clear.

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

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

Concise, with purpose upfront and bullet-like details. Could be slightly more structured, but 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 tool with two parameters, no output schema, and no annotations, the description adequately covers inputs, outputs, and use cases. It could detail return format more, but it's sufficient.

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

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

Schema coverage is 100% with descriptions. Description adds value by clarifying that only 'company' is supported, providing examples for value (ticker or CIK), and directing to resolve_entity for 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 clearly states the tool's purpose: 'Full profile of an entity across every relevant Pipeworx pack in one call.' It lists specific data sources (SEC, XBRL, patents, news, LEI) and contrasts with sibling tools by noting it replaces multiple sequential calls and directs federal contracts to usa_recipient_profile.

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

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

Explicitly provides when to use (comprehensive profile) and when not to (federal contracts: use usa_recipient_profile). Also advises using resolve_entity first for names, making adoption easy.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Delete' implies a destructive mutation, but it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting other tools). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for a simple tool.

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 destructive tool with no annotations and no output schema, the description is minimally adequate. It states the purpose and parameter, but lacks crucial context like deletion consequences, error handling, or return values. Given the complexity (mutation) and lack of structured data, it should provide more behavioral guidance 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 description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format, examples, or constraints. Baseline 3 is appropriate when the schema already fully describes parameters.

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 ('Delete') and target resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories), missing the opportunity to clarify its specific role in the memory management system.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or relationships with sibling tools like 'recall' or 'remember', leaving the agent to infer usage context independently.

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

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

The description discloses the tool's behavior (fetches page, extracts title/description/key links, emits markdown) beyond the annotations (readOnlyHint, idempotentHint, etc.). It does not contradict any annotation, and added context enhances transparency.

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

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

The description is concise with three sentences that front-load the main purpose, then detail behavior and use cases. Every sentence provides 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 the tool's low complexity (2 params, no output schema), the description covers purpose, behavior, and use cases adequately. It does not explain error handling or edge cases, but these are not critical for a read-only, idempotent tool.

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

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

Input schema has 100% coverage, so baseline 3 is appropriate. The description does not add new parameter details beyond what the schema provides, but it implicitly mentions 'URL' and 'max_links' in context. No significant added value.

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

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

The description clearly states the tool generates an llms.txt file for AI crawlers, specifying the verb (generate), resource (llms.txt), and targeted users. It distinguishes itself from sibling tools like ai_visibility_check or scan_competitor_ai_presence by its specific output format and purpose.

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

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

The description lists three explicit use cases (getting client site indexed, drafting own project, auditing competitor), which provides clear context. However, it does not mention when not to use this tool or suggest alternative sibling tools, which would have earned a 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return data (current position, velocity, altitude, heading), which is useful, but lacks details on error handling, rate limits, or data freshness, leaving gaps for a tool with no annotation coverage.

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

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

The description is appropriately sized and front-loaded, consisting of two efficient sentences that directly state the tool's purpose and return values without any wasted words, making it easy to understand quickly.

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 annotations and no output schema, the description is moderately complete: it covers the purpose and return data but lacks details on behavioral aspects like errors or limitations. For a simple query tool with one parameter, it's adequate but could be more comprehensive.

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

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

Schema description coverage is 100%, so the schema already fully documents the single parameter (icao24). The description adds minimal value by mentioning the parameter in context but does not provide additional syntax or format details beyond what the schema specifies, meeting the baseline for high coverage.

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

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

The description clearly states the specific action ('Track') and resource ('a specific aircraft by its ICAO24 transponder address'), distinguishing it from sibling tools like get_arrivals, get_departures, and get_flights_in_area which focus on different scopes (arrivals, departures, area-based flights).

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

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

The description provides clear context for when to use this tool (to track a specific aircraft by ICAO24 address), but does not explicitly state when not to use it or name alternatives among the sibling tools, such as using get_flights_in_area for broader queries instead.

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

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

No annotations are provided, so the description carries the full burden. It mentions the requirement for ICAO airport code and Unix timestamps, which is useful. However, it doesn't disclose behavioral traits like rate limits, authentication needs, pagination, error conditions, or what the return format looks like (especially important since there's no output schema). For a read operation with no annotation coverage, this is a significant gap.

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, efficient sentence that front-loads the core purpose ('Get flights that arrived at an airport within a time range') and follows with essential constraints. Every word earns its place with no redundancy or fluff.

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

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

Given the tool's complexity (3 required parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the return values look like (e.g., list of flights with details), error handling, or other behavioral aspects needed for effective use. The description alone is insufficient for an agent to fully understand how to interpret results.

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

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

Schema description coverage is 100%, so the schema already fully documents all three parameters (airport, begin, end). The description adds minimal value beyond the schema by mentioning that airport requires an ICAO code and timestamps are Unix-based, but doesn't provide additional syntax or format details. This meets the baseline of 3 when schema does the 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?

The description clearly states the specific action ('Get flights that arrived'), the resource ('at an airport'), and scope ('within a time range'). It distinguishes from sibling tools like 'get_departures' by specifying arrivals only, and from 'get_flights_in_area' by focusing on a specific airport rather than a geographic area.

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

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

The description provides clear context for when to use this tool (for arrivals at a specific airport within a time range) and implicitly distinguishes it from siblings like 'get_departures' (which handles departures) and 'get_flights_in_area' (which handles geographic areas). However, it doesn't explicitly state when NOT to use it or name alternatives, keeping it at a 4.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the required inputs and time constraints, but does not cover aspects like rate limits, authentication needs, error handling, or the format of returned data. It adequately describes the core operation but lacks deeper behavioral context.

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

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

The description is a single, efficient sentence that front-loads the purpose and key requirements without any wasted words. It is appropriately sized for a tool with three well-documented parameters and no complex behavioral traits to explain.

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 moderate complexity (three required parameters, no output schema, and no annotations), the description is minimally complete. It covers the purpose and inputs but does not address output format, error cases, or limitations (e.g., data availability, max time range). It meets basic needs but leaves gaps for an agent to infer 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?

The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by reiterating the need for an ICAO airport code and Unix timestamps, but does not provide additional syntax, format details, or usage nuances. Baseline 3 is appropriate when the schema does the 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?

The description clearly states the specific action ('Get flights that departed'), the resource ('from an airport'), and the scope ('within a time range'). It distinguishes from siblings like 'get_arrivals' (departures vs arrivals) and 'get_flights_in_area' (airport-specific vs area-based).

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

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

The description provides clear context by specifying the required inputs (ICAO airport code and Unix timestamps) and the time-bound nature of the query. However, it does not explicitly state when to use this tool versus alternatives like 'get_arrivals' or 'get_flights_in_area', nor does it mention any exclusions or prerequisites beyond the 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?

No annotations are provided, so the description carries the full burden. It discloses the return data fields (e.g., icao24, position) but omits critical behavioral traits such as data freshness (real-time vs. delayed), rate limits, authentication needs, error handling, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

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

The description is front-loaded with the core purpose in the first sentence and efficiently lists return fields in the second. Every sentence earns its place by providing essential information without redundancy, making it appropriately sized and well-structured.

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

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

Given no annotations, no output schema, and 100% schema coverage, the description is moderately complete: it clearly states the purpose and return data. However, it lacks details on behavioral aspects (e.g., real-time updates, errors) and output structure, which are important for a tool querying dynamic data like aircraft positions, leaving room for improvement in context.

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

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

Schema description coverage is 100%, with all parameters (lamin, lomin, lamax, lomax) clearly documented in the schema as bounding box coordinates. The description adds no additional parameter semantics beyond implying geographic filtering, so it meets the baseline of 3 where the schema does the heavy lifting without extra value from the description.

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

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

The description clearly states the specific action ('Get all aircraft currently in a geographic bounding box') and resource ('aircraft'), distinguishing it from siblings like get_aircraft, get_arrivals, and get_departures by specifying geographic filtering rather than general, arrival-specific, or departure-specific queries.

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 retrieving aircraft within a bounding box, but provides no explicit guidance on when to use this tool versus alternatives like get_aircraft (which might not filter geographically) or get_arrivals/departures. It lacks clear exclusions or prerequisites, leaving usage context inferred rather than stated.

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

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

No annotations provided, so description bears full burden. Discloses rate limit, the free nature, and the restriction against including user's prompt verbatim. Does not explicitly state that feedback is one-way or that no response is guaranteed, but it is adequate for this simple tool.

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

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

Two sentences plus a rate-limit note. Front-loaded key action and use cases. No unnecessary words; every sentence adds value.

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

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

For a simple feedback tool, the description covers purpose, use cases, input guidance, and constraints. No output schema needed, but could mention that no immediate response is expected. Overall sufficient for agent to use this tool correctly.

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

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

Schema has 100% coverage with good descriptions. The description adds extra guidance: 'Describe what you tried in terms of Pipeworx tools/data' and 'do not include the end-user's prompt verbatim,' which improves 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?

Clearly states the tool sends feedback to the Pipeworx team and lists specific use cases (bug reports, feature requests, missing data, praise). Distinguishes from sibling tools like ask_pipeworx and discover_tools by being about feedback, not queries or discovery.

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 (for feedback) and what to include (describe in terms of Pipeworx tools/data, exclude end-user prompt). Mentions rate limit (5 per day). Does not explicitly contrast with siblings, but the purpose is distinct 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?

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description adds crucial behavioral details: the data is derived from 'CF analytics-engine', contains no PII, and is cached for 5min-1h. This provides the agent with a clear understanding of data freshness and privacy.

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

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

The description is concise, well-structured, and front-loaded with the core purpose. Each sentence adds value, and the use case bullets are clear and actionable.

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 low complexity (1 optional param, no output schema), the description is complete: it explains the data source, cache, aggregation method, and provides three concrete use cases. No gaps in 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?

The schema already covers the parameter with 100% description coverage, but the description adds semantic value by explaining that shorter windows surface 'what's hot right now' and longer windows show 'steady-state demand', aiding agent decision.

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

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

The description clearly states the verb ('returns'), the resource ('top tools, top packs, and total call volume'), and the scope ('over a recent window'), making the tool's purpose immediately understandable. It also distinguishes from siblings like get_aircraft or get_arrivals by focusing on trending data across all AI agents.

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 three explicit use cases (discovering hot data sources, confirming canonical choice, seeing alignment) and mentions the cache and time windows. However, it does not explicitly state when not to use this tool or compare it to alternatives, though the context implies it's for trending insights.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context: monotonicity checking, mode-specific logic (walking child markets vs. searching separate events), and return of ranked opportunities with reasoning. No contradiction.

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

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

The description is somewhat long but every sentence serves a purpose: overall purpose, two modes with examples, a real-world scenario, and return value. It could be slightly trimmed but remains well-structured and informative.

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

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

With no output schema, the description states what the tool returns: 'ranked opportunities with suggested trade direction + reasoning.' It covers parameters, modes, behavior, and output, making it complete for a read-only tool with clear annotations.

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 adds value by explaining the modes and giving examples (e.g., event slug 'when-will-bitcoin-hit-150k', topic 'Strait of Hormuz traffic returns to normal') 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 'finds arbitrage opportunities on Polymarket by checking monotonicity violations' and explicitly distinguishes two modes: event and topic. The verb is specific, the resource is clear, and it 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?

The description provides explicit when-to-use guidance for both modes: 'event' for a single event slug, and 'topic' for cross-event searches. It explains why cross-event mode catches cases single-event misses, offering clear usage context.

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

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

Discloses detailed behavior: scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge, and returns top N with suggested trade direction. Annotations (readOnlyHint, openWorldHint, destructiveHint) are consistent and enhanced by this 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?

Well-structured and front-loaded with the main purpose. Every sentence adds value, though slightly verbose; could be trimmed slightly without losing clarity.

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

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

No output schema, but the description explains return structure (top N by edge magnitude with suggested trade direction) and model briefly. Complete enough for an agent to understand inputs and outputs.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add significant new meaning beyond what the schema provides; it reiterates parameter purposes without deeper syntax or format details.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifically for crypto-price bets. It specifies the model and data sources, distinguishing it 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?

Explicitly says it's built for the 'what should I bet on today' question, providing clear when-to-use guidance. Does not explicitly state when not to use or mention alternatives, but context is sufficient.

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

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

Annotations already declare readOnlyHint=true, so the agent knows it's safe. The description adds valuable context: the spread exists due to different participant pools and can be 2-25pp. It also explains output format. However, it doesn't discuss edge cases (e.g., missing events), so not a full 5.

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 (about 120 words) and well-structured: core purpose, two modes, output spec. No redundant sentences. Perfectly sized for an agent.

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

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

The description covers the concept, modes, parameter interaction, and output format. With no output schema, it sufficiently describes return values. A minor gap: behavior if no parameters are provided (though optional, may default? Not stated). Overall, very 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 adds meaning: it explains that topic is a shortcut and explicit parameters override the topic-mapped side. This clarifies the relationship between parameters beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: computing cross-venue spread between Kalshi and Polymarket. It explicitly mentions the verb (spread) and resource (same resolving question), and distinguishes from sibling tools like polymarket_arbitrage by focusing on this specific pair and providing two usage modes.

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

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

The description explains two usage modes (topic shortcuts vs explicit pairing) and when each applies. It gives clear context but doesn't explicitly address when not to use this tool or compare with alternatives like polymarket_arbitrage. However, the guidance is sufficient for an agent to decide between modes.

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

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

With no annotations provided, the description carries the full burden. It discloses that it retrieves or lists memories stored earlier, implying read-only behavior without destructive effects. However, it lacks details on error handling (e.g., if key doesn't exist), return format, or persistence scope beyond 'session or previous sessions,' leaving some behavioral gaps.

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

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

Two sentences front-load the core functionality (retrieve/list) and usage context, with zero wasted words. Each sentence earns its place by providing essential information 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's low complexity (1 optional parameter, no output schema), the description is mostly complete. It covers purpose, usage, and parameter semantics adequately. However, without annotations or output schema, it could benefit from more behavioral details like return format or error cases.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the semantics: 'omit key' to list all keys, which clarifies the optional parameter's effect beyond the schema's technical description. This enhances understanding but doesn't fully detail edge cases.

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 specific verb ('retrieve' or 'list') and resource ('previously stored memory'), distinguishing it from siblings like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving by key or listing all, making the purpose unambiguous.

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

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

It provides explicit guidance on when to use this tool vs. alternatives: 'Use this to retrieve context you saved earlier in the session or in previous sessions,' linking it to the 'remember' tool for storage. It also specifies when to omit the key to list all memories, offering clear usage 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?

With no annotations, the description carries full burden. It explains parallel fan-out to SEC EDGAR, GDELT, USPTO, and return structure (structured changes + total_changes count + URIs). It does not mention auth, rate limits, or error handling, but it is transparent about core behavior.

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

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

The description is concise, front-loaded with the tool's purpose, and each sentence adds value. No redundant or extraneous words.

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

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

Given no output schema, the description explains return structure. It covers inputs well for a 3-param tool. Could mention error handling or rate limits, but overall it is sufficiently complete for an agent to use correctly.

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

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

Schema coverage is 100%, and the description adds meaning by specifying 'type' only accepts 'company', 'since' accepts ISO date and relative formats, and 'value' examples (ticker or CIK). This 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 the tool shows what is new about an entity since a point in time, with a specific verb 'fans out' and details on parallel sources. It distinguishes itself from siblings like entity_profile (likely static) and compare_entities (comparison) by focusing on change monitoring.

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

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

The description explicitly says 'Use for "brief me on what happened with X" or change-monitoring workflows' and provides guidance on 'since' parameter formats (ISO date or relative). It lacks explicit when-not-to-use statements, but the context implies limitations to company entities.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool stores data in session memory, specifies persistence differences (authenticated users get persistent memory, anonymous sessions last 24 hours), and implies it's a write operation. It does not cover rate limits, error conditions, or response format, but provides sufficient context for basic use.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add valuable context without redundancy. Every sentence earns its place by clarifying usage and behavioral details concisely.

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 moderate complexity (write operation with persistence nuances), no annotations, and no output schema, the description is fairly complete. It covers purpose, usage context, and key behavioral traits like persistence. However, it lacks details on return values or error handling, which would be helpful for a tool with 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?

The input schema has 100% description coverage, so the schema already documents both parameters (key and value) with examples. The description does not add any parameter-specific details beyond what the schema provides, such as constraints or usage tips, but the baseline is 3 when schema coverage is high.

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 with specific verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes it from sibling tools like 'recall' (which likely retrieves) and 'forget' (which likely deletes). It specifies what types of data can be saved (intermediate findings, user preferences, context across tool calls).

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

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

The description provides clear context on when to use this tool (to save data across tool calls) and mentions different persistence behaviors for authenticated vs. anonymous users. However, it does not explicitly state when NOT to use it or name specific alternatives among siblings, though the distinction from 'recall' and 'forget' is implied.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return values (ticker, CIK, name, URIs) and the version constraint. However, it does not mention behavioral traits like idempotency, side effects, or error handling. The description is adequate but lacks depth for a tool with no annotations.

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

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

The description is extremely concise: two sentences. The first sentence states the core purpose, and the second provides details, examples, and a benefit. No superfluous information; every word earns its place.

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

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

For a simple tool with 2 parameters and no output schema, the description is quite complete. It explains what is returned (ticker, CIK, name, URIs), the version limitation, and the types of input. It could be improved by mentioning what happens if the entity is not found, but it's largely sufficient.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining the 'type' parameter (v1 supports 'company') and the 'value' parameter with examples ('AAPL', '0000320193', 'Apple'), clarifying how to use them beyond the schema's enum and type descriptions.

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

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

The description clearly states the tool's purpose: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies the action (resolve) and resource (entity), and distinguishes itself by claiming it 'Replaces 2–3 lookup calls,' which differentiates it from potential sibling lookup tools.

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

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

The description tells when to use the tool: for resolving entities like companies in a single call instead of multiple lookups. It provides examples of accepted inputs (ticker, CIK, name) and notes the v1 limitation (type='company'). However, it does not explicitly state when not to use it or alternatives among siblings.

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

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

Discloses internal dependency on ai_visibility_check, explains how probes work per entity, and describes output fields (score, confidence, signal density). Annotations already indicate idempotent, read-only behavior, so description adds valuable context beyond that.

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

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

Two sentences plus brief example—every sentence adds value. Purpose first, then use case and return structure. 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?

Covers purpose, usage, parameters, output structure, and internal mechanism. Lacks error handling or rate limit notes but is sufficient given annotations and schema completeness.

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

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

Adds meaning beyond schema: clarifies first entity as 'subject' for narrative, explains purpose of context and models, and notes apiKey requirement when using anthropic. Schema coverage is 100%, but description still enriches parameter understanding.

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

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

Clearly states the tool compares AI visibility across multiple entities side-by-side, using a specific verb and resource. Differentiates from sibling ai_visibility_check by emphasizing the comparative, multi-entity nature.

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 a concrete use case ('competitive AI-marketing audits') and implies the alternative (single entity check via ai_visibility_check). Does not explicitly list when not to use, but context is clear.

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

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

No annotations are provided, so the description bears the full burden. It lists the return values (verdict, structured form, actual value with citation, percent delta) and the scope. It does not disclose potential errors, authentication needs, or other behavioral traits, but the described behavior is fairly comprehensive for a fact-checking tool.

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

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

The description is concise (three sentences) with no unnecessary words. It efficiently covers purpose, domain, output, and value proposition, making it easy for an agent to parse quickly.

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

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

For a single-parameter tool with no output schema, the description provides complete context: what it does, what claims it handles, what it returns, and why it is valuable. No gaps remain for typical usage.

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

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

The input schema has 100% coverage with a clear description and examples. The tool description adds context by specifying the claim format (natural-language, company-financial), which enhances usability beyond the schema alone.

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

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

The description clearly states the tool's function: fact-checking natural-language claims against authoritative sources. It specifies the supported domain (company-financial for public US companies) and the method (SEC EDGAR + XBRL). The mention of replacing 4-6 sequential agent calls differentiates it from sibling tools.

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

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

The description explicitly limits usage to company-financial claims (revenue/net income/cash) for public US companies, guiding when to use it. It also highlights efficiency gains by replacing multiple agent calls. However, it does not mention alternatives or when not to use it beyond the stated domain.

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