geo

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

Annotations already indicate read-only and idempotent behavior. The description adds important context: cost implications for Anthropic, default free model, and return structure. There is 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 three concise sentences. It front-loads the core action, then provides details, and ends with use cases. 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?

The description covers purpose, usage, and output format. It does not detail error handling or limitations, but given the simple 4-param schema and no output schema, it is largely complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the default model, how models parameter works, and the purpose of the _apiKey and context 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 tool's purpose: probing LLMs for knowledge about an entity and scoring visibility per model. It distinguishes from siblings like 'entity_profile' and 'scan_competitor_ai_presence' by focusing on multi-model visibility scoring.

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

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

The description explicitly mentions use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and notes the default model and optional Anthropic integration. However, it does not compare with siblings or specify when not to use.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool interprets natural language questions, selects appropriate data sources, and returns results. However, it lacks details on limitations (e.g., response time, error handling) or authentication needs, though this is partially mitigated by the simple single-parameter design.

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 functionality, followed by supporting details and examples. Every sentence adds value: the first explains the tool's purpose, the second describes its automation benefits, and the third provides concrete examples. 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 complexity (natural language processing and tool selection) and lack of output schema, the description is mostly complete. It explains what the tool does and how to use it, but doesn't detail return formats or potential errors. For a tool with no annotations and simple parameters, this is adequate though not exhaustive.

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 baseline is 3. The description adds value by explaining the parameter's purpose in context: 'Your question or request in natural language' and providing examples like 'What is the US trade deficit with China?' This clarifies the expected format and scope beyond the schema's basic description.

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

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

The description clearly states the tool'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'), and mechanism ('Pipeworx picks the right tool'), distinguishing it from sibling tools like 'discover_tools' or 'geocode' which have different functions.

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

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

The description provides explicit guidance on when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by implying this tool handles tool selection internally, unlike other tools that require specific parameters or knowledge. Examples further clarify appropriate use cases.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds that it resolves market, classifies bet, fans out to packs, and returns evidence packet plus comparison. This is additive without contradicting annotations, though lacks details on error handling or rate limits.

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

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

Three sentences each serve a distinct purpose: what, how, and when/why. No redundant or filler content. Efficiently packed with 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?

No output schema, but description details return: evidence packet plus market-vs-model comparison. Covers resolution, classification, fan-out logic. Adequate for an AI agent to understand what it gets back and how it supports decision-making.

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

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

Schema covers both parameters with descriptions. The description adds real-world semantics: 'market' can be slug, URL, or text; 'depth' has explicit behavior ('quick = 2-3 evidence sources, thorough = full fan-out') and default. This adds value beyond the enum 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?

Description clearly states verb 'Research' and resource 'Polymarket bet' with specific action: pulling Pipeworx data. It uniquely differentiates from siblings like 'ask_pipeworx' and 'validate_claim' by focusing on betting edge on Polymarket.

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?

Description explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It positions this as the core demo product, implying agents should prefer it over discovering packs individually. No explicit exclusions, 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?

Discloses data sources (SEC EDGAR for companies, FDA for drugs) and return format (paired data + pipeworx:// URIs). With no annotations, this is good coverage; could add rate limits or auth needs.

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

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

Two sentences with no fluff. Front-loads core purpose and type-specific data. 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 2-param tool with no output schema, description covers purpose, data sources, and types. Lacks error examples or exact formatting, but sufficient for an agent.

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

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

Schema covers all params (100% coverage) with descriptions. Description reinforces but adds little new meaning; baseline 3 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?

Description clearly specifies comparing 2-5 entities side by side, with distinct data for 'company' and 'drug' types. It also highlights replacing 8-15 sequential calls, distinguishing 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?

States when to use (comparing multiple entities) and efficiency gain over sequential calls. Does not explicitly exclude scenarios, but context is clear enough.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation (implied read-only), returns ranked results ('most relevant'), and has a specific use case (initial discovery). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.

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 well-structured in two sentences. The first sentence explains the core functionality, and the second provides crucial usage guidance. Every word earns its place with no redundancy or unnecessary elaboration.

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 operation with 2 parameters) and 100% schema coverage, the description provides good contextual completeness. It explains the purpose, when to use it, and the expected output format. The main gap is the lack of output schema, but the description partially compensates by mentioning what gets returned ('tools with names and descriptions').

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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain query formatting or limit implications). This meets the baseline expectation 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 verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes itself from sibling tools by focusing on tool discovery rather than geographic or time-related functions like geocode or get_timezone.

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 guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use it (large tool catalog, initial discovery phase) and implicitly suggests alternatives (directly using specific tools once identified).

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 what data is returned (SEC filings, XBRL, patents, news, LEI, and citation URIs) but does not explicitly state if the tool has side effects (e.g., read-only). Given that annotations are absent and the tool appears to be a read operation, the description is fairly transparent, though it could benefit from mentioning idempotency or read-only 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: three sentences that front-load the main purpose, list key data sources, and provide usage tips. Every sentence adds value, and there is no unnecessary wording.

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 is largely complete given the tool's complexity. It lists the data sources returned, mentions the return format (pipeworx:// URIs), and notes missing features (person/place coming soon). Without an output schema, the description adequately informs the agent about the output. A small gap is the lack of mention of potential size or pagination, but this is minor.

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

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

The input schema already provides detailed descriptions for both parameters (type with enum and example, value with ticker/CIK format and guidance). The description adds minimal extra value (e.g., 'zero-padded CIK') but does not significantly expand the meaning beyond the schema. With 100% schema coverage, 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 that the tool returns a full profile of an entity across relevant packs, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from the sibling tool resolve_entity by noting that names are not supported and that resolve_entity should be used first. This provides a specific verb and resource with clear 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 explicitly states when to use the tool (for company profiles needing consolidated data) and when not to use it (for federal contracts, use usa_recipient_profile directly). It also provides a clear alternative for resolving names (use resolve_entity first). This gives strong 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting other tools). It also doesn't describe the response format or error conditions, leaving significant behavioral gaps for a mutation 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 a single, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and efficient. Every word earns its place, achieving optimal conciseness for this 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 mutation tool with no annotations and no output schema, the description is incomplete. It lacks critical context such as what constitutes a valid 'key', whether deletion is idempotent, what happens on success/failure, or how this interacts with sibling tools like 'remember' and 'recall'. The agent would need to guess or trial-error these aspects.

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, with the 'key' parameter fully documented in the schema itself ('Memory key to delete'). The description adds no additional semantic context beyond what the schema provides (e.g., key format, examples, or constraints), so it meets the baseline of 3 for high schema coverage without adding 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 action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'recall' (which presumably retrieves memories) or 'remember' (which presumably stores memories), missing an opportunity for sibling differentiation that would warrant a score of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., needing an existing memory to delete), when-not-to-use scenarios, or explicit references to sibling tools like 'recall' or 'remember' for context. This leaves the agent without usage context beyond the basic purpose.

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

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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds behavioral steps: fetches page, extracts title/description/key links, outputs markdown. This contextualizes the operation beyond the safe annotations.

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

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

Two sentences plus bullet list, no wasted words. Front-loads the main purpose and hints at behavior. Efficient and scannable.

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 full schema coverage and informative annotations, the description covers everything needed: purpose, usage, behavior, and output format. No gaps for a simple tool without 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?

All parameters are fully described in the input schema (100% coverage). The description does not add new semantics beyond what the schema provides, so baseline of 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for any URL, with a specific verb (Generate) and resource (llms.txt file). It distinguishes from siblings like 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 provides concrete use cases (getting a client's site indexed, drafting for own project, auditing competitor). While it doesn't explicitly state when not to use, the intended context is clear and helpful.

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 states the conversion function but does not disclose behavioral traits such as rate limits, accuracy considerations, error handling, or authentication needs. For a geocoding tool with zero annotation coverage, this leaves significant gaps in understanding its operation.

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 directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

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 (geocoding with one parameter) and no output schema, the description adequately covers the basic purpose. However, it lacks details on return values, error cases, or behavioral aspects, which are important for a tool with no annotations. It is minimally viable but has clear 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?

The schema description coverage is 100%, with the single parameter 'query' documented as 'Address or place name to geocode'. The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('convert') and resource ('address or place name') to produce the output ('latitude/longitude coordinates'). It distinguishes from sibling tools like 'reverse_geocode' (which does the opposite conversion) and other location-related tools like 'get_country' or 'get_timezone' by focusing on forward geocoding.

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 converting addresses to coordinates but does not explicitly state when to use this tool versus alternatives like 'reverse_geocode' (for coordinates to addresses) or other siblings. It provides basic context but lacks explicit guidance on exclusions or comparisons.

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 states the tool 'gets' information, implying a read-only operation, but doesn't disclose behavioral traits such as error handling (e.g., for invalid inputs), rate limits, authentication needs, or what 'detailed information' includes (e.g., format, fields). This leaves significant gaps for an agent to understand how to invoke it correctly.

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 without unnecessary words. Every part earns its place by specifying the action, resource, and lookup method, making it easy 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?

Given the complexity of a tool with one parameter but no annotations or output schema, the description is incomplete. It doesn't explain what 'detailed information' entails (e.g., response format, fields), behavioral aspects, or usage context relative to siblings. This could hinder an agent from selecting and invoking the tool effectively.

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

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

The input schema has 100% description coverage, with the parameter 'code_or_name' fully documented in the schema. The description adds minimal value beyond the schema by reiterating 'by name or ISO code', but doesn't provide additional semantics like examples or edge cases. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information about a country'), and it specifies the lookup mechanism ('by name or ISO code'). However, it doesn't explicitly differentiate from siblings like 'geocode' or 'reverse_geocode', which might also provide country information in different contexts.

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 siblings like 'geocode' (which might provide location data) or 'get_timezone' (which might include country info), nor does it specify prerequisites or exclusions 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on traits like rate limits, error handling, authentication needs, or response format. For a tool with no annotations, 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: 'Get sunrise and sunset times for a location.' It is front-loaded with the core purpose and has no wasted words, making it highly concise 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 the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, and output, which are needed for full completeness. It meets the minimum viable standard with clear 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?

The input schema has 100% description coverage, clearly documenting all parameters (latitude, longitude, date). The description implies location-based parameters but adds no extra meaning beyond the schema. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.

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: 'Get sunrise and sunset times for a location.' It specifies the verb ('Get') and resource ('sunrise and sunset times'), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'get_timezone' or 'geocode,' which might also involve location data, so it's not a perfect 5.

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 sibling tools like 'get_timezone' or 'geocode,' nor does it specify contexts or exclusions for usage. This leaves the agent without clear direction on 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't specify error handling for invalid coordinates, rate limits, authentication requirements, or the format of the returned timezone and local time. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose ('Get the current timezone and local time for a location'), making it easy to understand at a glance. Every part of the sentence earns its place by specifying the resource and context.

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

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

Given the complexity of a geospatial query tool with no annotations and no output schema, the description is incomplete. It doesn't address how results are returned, potential errors, or dependencies on external services. For a tool that likely interacts with timezone databases or APIs, more context on behavior and output expectations is needed to be fully helpful to an AI agent.

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

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

The input schema has 100% description coverage, with clear documentation for both 'latitude' and 'longitude' parameters. The description adds no additional semantic meaning beyond what the schema provides—it doesn't explain coordinate formats, valid ranges, or how the location is used to determine timezone. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 a specific verb ('Get') and resource ('current timezone and local time for a location'). It distinguishes itself from siblings like 'geocode' or 'get_sunrise_sunset' by focusing on timezone data rather than geographic coding or astronomical events. However, it doesn't explicitly differentiate from 'get_country', which might also provide location-based information.

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 when to choose this over 'get_country' for location-based queries or 'reverse_geocode' for coordinate-based lookups. There are no explicit instructions on prerequisites, such as needing valid coordinates, or exclusions for 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?

With no annotations, the description must cover behavioral traits. It discloses rate limiting ('5 messages per identifier per day') and notes it is free. However, it does not explain side effects, data handling, or whether feedback is stored/replied to. Some behavioral gaps remain.

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

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

The description is four sentences, each serving a distinct purpose: stating the action, listing use cases, providing content guidelines, and noting rate limits. No redundant or unnecessary text; it is concise and front-loaded.

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

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

Given no output schema, the description omits what the user can expect after sending feedback (e.g., success message, confirmation). While it covers input guidance and constraints, the absence of any mention of return values leaves a moderate completeness gap for a feedback-dispatching 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 parameters are well-documented in the schema. The description adds value by reinforcing usage context (e.g., rate limit, content rules) and clarifying the intent behind each enum value, which goes beyond the schema's 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: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) and provides a distinct purpose that differentiates it from sibling tools, which are unrelated to feedback.

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

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

The description explicitly lists when to use the tool and provides content rules: 'Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim.' It also mentions rate limiting. However, it does not explicitly state when not to use it or suggest alternatives, though no alternative feedback tool exists 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds critical context: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This explains data source, privacy, and caching 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?

Single paragraph efficiently covers purpose, use cases, and technical details. Front-loaded with main output, no redundant sentences.

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

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

No output schema exists, but description explains output components (pack, tool, count) and caching. Sufficient for a simple lookup tool; minor gap on exact response format.

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 caching details per window (5min-1h) not present in schema description, providing extra context that aids agent understanding.

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

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

The description clearly states 'Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d)'. This provides a specific verb and resource, distinguishing it from siblings like ask_pipeworx or discover_tools. The use cases further clarify its unique role.

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

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

The description lists three specific use cases (discovering hot data sources, confirming canonical tools, checking alignment with trend). It provides clear context but does not explicitly state when not to use or name alternatives.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. Description adds behavioral context about the two modes and their logic, but does not disclose further 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?

Description is informative and well-structured, starting with purpose, then modes, then cross-event rationale, then return info. Slightly wordy but earns its length by providing essential 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?

Despite no output schema, description specifies return format: 'ranked opportunities with suggested trade direction + reasoning.' Covers both modes and their use cases. Adequate for a moderately complex search tool.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds examples and explains the difference between event and topic, which adds value over the schema alone. Baseline of 3 is appropriate.

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

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

Description clearly states what the tool does: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It explicitly names two modes (event and topic) and contrasts them, distinguishing it 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?

Provides clear guidance on when to use each mode: event for a single event slug, topic for cross-event searching. Explains why cross-event mode catches cases missed by single-event. Does not explicitly state when not to use, 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 reveals key behavioral traits: it scans top markets, groups by asset, fetches price history once (efficiency), computes model probability, and ranks by edge magnitude. It also notes this is V1 covering crypto-price bets. Annotations (readOnlyHint, no destructive) are consistent and the description adds valuable context beyond them.

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

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

The description is well-structured and front-loaded with the main purpose. Each sentence adds value, covering model details, process, return type, and intended use case without redundancy. It is appropriately sized 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 lacking an output schema, the description explains the return value (top N ranked by edge magnitude with suggested trade direction). It covers the model source, grouping logic, and limitations (V1, crypto-only). Combined with annotations, it provides a complete picture for a complex tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond what the schema already provides for limit, window, and min_edge_pp; it merely restates the schema's 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 scans high-volume Polymarket markets, identifies where Pipeworx data disagrees with market price, and returns edges ranked by magnitude. It specifies the model (lognormal from FRED + coinpaprika) and distinguishes from siblings like polymarket_arbitrage by focusing on edge opportunities.

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 the tool is built for the 'what should I bet on today' question and helps agents/users discover opportunities without manual paging. It implies usage for crypto-price bets but does not explicitly exclude other market types or compare with sibling tools like ask_pipeworx.

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, non-destructive, idempotent behavior. Description adds that the spread is a real arbitrage signal and details the return format (leg-by-leg prices in probability 0-1, spread in percentage points). 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?

Description is reasonably concise for the complexity, with clear front-loading of purpose and modes. Each sentence adds value, though could be slightly shortened without losing 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?

Fully covers all aspects: modes, parameters, return format, and rationale. With no output schema, the description correctly specifies the output structure. Sufficient for an agent to understand and invoke 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%, but description adds operational context: how topic auto-fetches matching events, how explicit parameters override the mapped side, and lists example topic values. This clarifies parameter relationships 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 explicitly states it computes the cross-venue spread between Kalshi and Polymarket for the same resolving question, with two modes (topic shortcuts vs explicit tickers). This clearly differentiates it from siblings like polymarket_arbitrage which likely focuses on Polymarket-only 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?

Describes two modes and when to use each: topic for pre-mapped shortcuts, explicit for custom pairings. Provides context on why spreads exist (different participant pools). Lacks explicit when-not-to-use guidance but covers key usage scenarios.

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

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it can retrieve memories from current or previous sessions, and the conditional behavior based on parameter presence. However, it doesn't mention error handling, data formats, or persistence details that would be helpful for a memory 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 well-structured sentences with zero waste. The first sentence states the core functionality with parameter guidance, the second provides usage context. Every word earns its place in this efficient description.

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

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

For a single-parameter tool with good schema coverage but no annotations or output schema, the description provides adequate context about behavior and usage. It could be more complete by mentioning return formats or error cases, but it covers the essential operational semantics given 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?

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the conditional behavior: omitting the key triggers listing all memories, while providing it retrieves a specific memory. This clarifies the parameter's semantic role beyond what the schema states.

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 ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool ('to retrieve context you saved earlier') and when to omit parameters ('omit key to list all keys'). It implicitly distinguishes from 'remember' for storage and 'forget' for deletion, though it doesn't name alternatives directly.

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 for behavioral traits. It details the fan-out to three sources (SEC, GDELT, USPTO) in parallel, the since parameter format (ISO or relative), and the output structure (structured changes, total_changes count, pipeworx URIs). It does not disclose auth needs or rate limits, but for a read operation this is adequate.

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

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

The description is moderately concise, with each sentence adding value (purpose, fan-out, parameter details, output, use case). It is front-loaded with the main purpose. A minor redundancy: 'in parallel' could be assumed, but overall structure is good.

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 (fan-out to multiple sources) and no output schema, the description provides sufficient context: purpose, input constraints, output shape, and use case. It lacks error handling details but is complete enough for an agent to use correctly in typical scenarios.

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

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

The schema covers all three parameters with descriptions. The description adds value beyond the schema by providing examples for since (relative like '7d', '30d'), typical usage ('30d' or '1m'), and clarifying value can be ticker or CIK. This enhances the agent's understanding of valid 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 clearly states the tool's purpose: returning what's new about an entity since a given time. It specifies the verb ('brief me on what happened') and the resource (entity changes across SEC, GDELT, USPTO). The purpose is distinct from sibling tools like entity_profile or compare_entities, which focus on static profiles or comparisons.

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.' This provides clear usage context. However, it does not explicitly state when not to use the tool or mention alternatives among siblings, which prevents a higher score.

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 performs a write operation ('store'), specifies persistence characteristics ('authenticated users get persistent memory; anonymous sessions last 24 hours'), and implies it's for session-scoped data. However, it doesn't mention error conditions, rate limits, or specific authentication requirements beyond the persistence distinction.

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, with two concise sentences that directly address purpose and behavioral context. Every sentence earns its place by providing essential information without 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 moderate complexity (a write operation with persistence nuances), no annotations, and no output schema, the description is reasonably complete. It covers purpose, usage context, and key behavioral traits like persistence rules. However, it lacks details on return values, error handling, or specific limitations, which would be helpful for full 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?

The input schema has 100% description coverage, providing clear documentation for both parameters. The description adds minimal value beyond the schema, as it doesn't elaborate on parameter usage, constraints, or examples. The baseline score of 3 is appropriate since the schema already 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 tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), and distinguishes it from sibling tools like 'recall' (which presumably retrieves) and 'forget' (which presumably deletes). It also specifies the type of data that can be stored ('intermediate findings, user preferences, or 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 intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use it or name alternatives. It implies usage for persistence across calls but lacks explicit exclusions or comparisons to other storage mechanisms.

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?

Without annotations, the description carries the burden of transparency. It discloses that it is a single call, version 1 with limited support (company type), and lists return fields. No contradictions are present, but it could add details about potential errors or limitations.

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

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

The description is two sentences, concise and front-loaded with the core purpose. Every sentence adds necessary 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?

While there is no output schema, the description explicitly lists the return fields (ticker, CIK, name, resource URIs) and explains the tool's benefit. This provides sufficient context for an agent to use it effectively, though error handling or rate limits are not mentioned.

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

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

The schema covers 100% of parameters with descriptions. The description adds value by providing examples (AAPL, 0000320193, Apple) and clarifying the type parameter's current version constraint, which goes beyond the schema.

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

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

The description clearly states the tool resolves entities to canonical IDs in a single call, specifying accepted inputs (ticker, CIK, name) and outputs (ticker, CIK, name, resource URIs). It distinguishes from alternatives by noting it replaces 2-3 lookup 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 explains when to use the tool (for entity resolution) and implies it replaces multiple calls. However, it does not explicitly mention when not to use it or provide direct comparisons with sibling tools, though context signals show many siblings are unrelated.

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 of behavioral disclosure. It states the conversion action but doesn't reveal any behavioral traits such as accuracy, rate limits, data sources, error handling, or output format. For a 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 that directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, making it easy 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like performance, limitations, or what the returned address includes (e.g., full address, components). For a conversion tool with no structured context, more detail is needed to guide effective use.

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

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

The description mentions 'latitude/longitude coordinates' but doesn't add meaning beyond what the input schema provides. With 100% schema description coverage, the schema already documents both parameters adequately. The description doesn't specify coordinate formats, ranges, or units, so it meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: converting coordinates to an address. It uses specific verbs ('convert') and resources ('latitude/longitude coordinates', 'address'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'geocode' (which likely does the opposite conversion).

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 sibling tools like 'geocode' (reverse operation), 'get_country', 'get_sunrise_sunset', or 'get_timezone', nor does it specify use cases or prerequisites. The agent must infer usage from the purpose alone.

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 the tool is read-only, idempotent, and not destructive. The description adds valuable behavioral details: it probes each entity with ai_visibility_check, ranks results, and returns 'score, confidence, signal density'. This goes beyond annotations and informs the agent about the tool's operation.

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

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

The description is concise (4 sentences) and front-loaded with the main purpose. Every sentence earns its place: purpose, method, use case, and return format. 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 the lack of an output schema, the description adequately explains the return format: 'ranked list with score, confidence, signal density per entity.' It also covers the tool's behavior (probing, ranking) and use case. All necessary context is provided for an agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, providing a baseline of 3. The description adds semantic value by explaining that 'the first entry is treated as the subject for narrative; rest are competitors' and that it uses ai_visibility_check internally. This clarifies parameter usage beyond the schema.

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

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

The description clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare), resource (AI visibility), and distinguishes from siblings by mentioning it 'probes each entity with ai_visibility_check' and 'ranks by score'. The example use case further clarifies its unique function.

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: 'Useful for competitive AI-marketing audits' with a concrete example. However, it does not state when not to use it or how it differs from sibling tools like 'compare_entities' or 'entity_profile', leaving some ambiguity for the 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?

Without annotations, the description discloses key traits: it uses SEC EDGAR + XBRL, returns verdict and citation. It does not mention data staleness, rate limits, or error behavior, but the core functionality is transparent. The 'Replaces sequential calls' hint suggests efficiency but not safety or side effects.

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

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

Two very concise sentences front-load the main purpose and immediately provide domain scope, output details, and efficiency benefit. 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?

The description covers what the tool does, its domain, inputs, and outputs. Lacking details on error handling, data freshness, or what happens with unsupported claim types, but given the simplicity of the tool (single param, no output schema), it is sufficiently complete for typical use.

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

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

The single parameter 'claim' is well-described in the schema with examples in the description. Schema coverage is 100%, so the description adds value with natural-language examples and context. This goes beyond the schema's basic 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 explicitly states the tool's purpose: fact-checking natural-language claims against authoritative sources, specifies domain (company-financial claims for US public companies) and what it returns. It is specific and distinct from sibling tools like compare_entities or resolve_entity.

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

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

The description mentions it replaces 4-6 sequential agent calls, giving a clear usage context. However, it does not explicitly state when not to use it or compare with siblings like compare_entities or resolve_entity. The scope limitation (v1 supports only certain claims) is helpful but could be more explicit about exclusions.

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