airports

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 that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which explains the tool's behavior as an orchestrator. However, it lacks details on error handling, rate limits, authentication needs, or response format, leaving gaps 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 front-loaded with the core functionality, followed by supporting details and examples. Every sentence adds value: the first explains the purpose, the second describes the process, the third provides usage guidance, and the examples illustrate practical applications. No wasted words.

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

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

Given no annotations and no output schema, the description is moderately complete. It explains the tool's role and usage but lacks details on behavioral traits like error handling or response structure. For a tool that orchestrates other tools, more context on limitations or expected outputs would be beneficial.

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 'question' parameter documented as 'Your question or request in natural language.' The description adds minimal value beyond this, reiterating 'plain English' and providing examples. Baseline 3 is appropriate since the schema adequately covers the single parameter.

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

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

The description clearly states the 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. The examples further clarify the 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 this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools like discover_tools or search_airports by offering a simplified, natural language interface. The examples provide clear context for 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?

Beyond annotations (readOnly, openWorld), the description details internal steps: market resolution, bet classification, pack fan-out, and return of evidence packet plus market-vs-model comparison. No contradictions with annotations.

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

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

Description is four sentences, front-loaded with purpose. While slightly lengthy, each sentence adds useful detail. Efficient given 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?

Without output schema, description explains return type (evidence packet + comparison). Covers process adequately. Could mention any limitations or prerequisites but is sufficiently complete 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?

Schema coverage is 100%, but description adds value by explaining the market parameter accepts slug, URL, or question text, and clarifies depth defaults to thorough. Enhances schema 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 the tool researches Polymarket bets using Pipeworx data. It specifies the verb 'research' and the resource 'Polymarket bet', and distinguishes itself from siblings by focusing on integrated data fan-out for betting edge analysis.

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 'should I bet on X?' and notes agents convert better with this tool than discovering packs themselves. Lacks explicit alternatives but provides clear context for when 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 full burden for behavioral disclosure. It states the calculation method ('great-circle distance') and return format ('distance in both kilometers and miles'), which adds useful context. However, it doesn't disclose error handling, precision, or computational characteristics that would help an agent understand behavioral traits.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence explains what the tool does and its inputs, while the second explains the output format. There's zero wasted language and the information is front-loaded appropriately.

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 relatively simple tool with 2 parameters, 100% schema coverage, and no output schema, the description provides adequate context. It explains the calculation method, input format, and output units. However, without annotations or output schema, it could benefit from more behavioral context about error cases or limitations.

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 both parameters ('from' and 'to') clearly documented in the schema. The description adds no additional parameter information beyond what the schema provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in 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 ('calculate the great-circle distance'), the resource ('between two airports'), and the identification method ('by their IATA codes'). It distinguishes itself from sibling tools like 'get_airport' and 'search_airports' by focusing on distance calculation rather than airport information retrieval.

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 when distance between airports is needed, but provides no explicit guidance on when to use this tool versus alternatives. There's no mention of prerequisites, limitations, or comparison with other distance calculation methods. The context is clear but lacks specific when/when-not instructions.

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

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

No annotations provided, but description fully discloses behavior: type-dependent data sources (SEC EDGAR, FDA), output includes paired data and URIs, and performance benefit of replacing multiple calls.

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

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

Extremely concise: two primary sentences plus a compact listing of types. Front-loaded with the core action, no redundant or vague phrases.

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 absence of annotations and output schema, description covers purpose, parameters, return value hints (paired data + URIs), and performance aspect. Lacks error or edge-case details but adequate for standard use.

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

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

Schema coverage is 100% with clear descriptions. Description adds value by explaining how 'values' should be formatted per type (tickers/CIKs vs drug names) and connects 'type' to data source, going 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?

Description states specific verb 'compare', resource 'entities', and differentiates by type with data sources. Clearly distinguishes from siblings like 'resolve_entity' by focusing on multiple entities.

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 context: comparing 2-5 entities side by side, and notes efficiency gains. However, does not explicitly state when not to use it or guide towards alternatives for single-entity 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation that returns relevant tools with names and descriptions, and it should be called first in specific scenarios. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool discovery function.

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, followed by usage guidance, with no wasted words. Both sentences earn their place by providing essential information efficiently, making it easy for an agent to quickly understand and apply the 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?

Given the tool's moderate complexity (search function with 2 parameters) and lack of annotations or output schema, the description is mostly complete: it covers purpose, usage, and basic behavior. However, it could improve by mentioning output format or error cases, but it's sufficient for an agent to use it effectively in the given 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%, so the schema already documents both parameters (query and limit) thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining query formatting or limit implications in more detail, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role for discovering tools among 500+ options, unlike the sibling tools which are specific operations like calculating distance or searching airports.

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'), including a clear condition (500+ tools) and an alternative context (vs. directly using specific tools like siblings). It effectively tells the agent when to prioritize this tool over others.

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

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

No annotations provided, so description carries full burden. It does not disclose behavioral traits such as authentication requirements, rate limits, or whether the operation is read-only. Only mentions output includes citation URIs but no further 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?

Three sentences front-load the main purpose, list data sources, and give usage guidelines without waste. Every sentence provides essential information efficiently.

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

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

Given the tool's complexity (multiple data sources) and lack of output schema/annotations, the description covers what data is included and provides alternative paths. Could be improved with response structure hints or error conditions, but adequate for 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 already covers 100% of parameters with descriptions. Description adds significant value by providing example values (ticker, CIK), clarifying that names are not supported, and directing to resolve_entity for name resolution, surpassing schema 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?

Description clearly states it returns a full profile of an entity across multiple Pipeworx packs in one call, listing specific data sources (SEC filings, XBRL, patents, news, LEI) and distinguishing from sibling tools like resolve_entity and 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 tells when not to use (for federal contracts, use usa_recipient_profile) and implies needing resolve_entity for name resolution, providing clear context for when this tool should be selected.

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. It states the action ('Delete') but doesn't describe consequences (e.g., whether deletion is permanent, if it affects other data, or what happens on invalid keys). For a destructive operation, this lack of detail 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 with zero waste. It's front-loaded with the core action ('Delete'), making it immediately clear. Every word earns its place, and there's no 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?

For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, confirmation needs, or return values. Given the complexity of a delete operation, more context is needed for safe and 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?

Schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, only implying the key identifies what to delete. Since the schema already does the heavy lifting, 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 verb ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes from siblings like 'remember' (create) and 'recall' (retrieve), though it doesn't explicitly mention these alternatives in the description itself.

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), when not to use it, or how it relates to sibling tools like 'remember' or 'recall'. The agent must infer usage from context 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what information is returned (name, city, country, coordinates, altitude, timezone) and the input requirement (IATA code), but doesn't mention error handling, rate limits, authentication needs, or whether the data is static or real-time. It adequately describes the core behavior but lacks operational context.

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

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

The description is perfectly concise and front-loaded. The first sentence states the complete purpose and input requirement, and the second sentence lists the return values. 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 low complexity (single parameter, no annotations, no output schema), the description is reasonably complete. It explains what the tool does, what it requires, and what it returns. The main gap is the lack of output schema, but the description compensates by listing the return fields. For a simple lookup tool, this is nearly complete.

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

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

Schema description coverage is 100%, so the schema already fully documents the single parameter. The description adds value by providing examples ('JFK', 'LHR', 'NRT') and clarifying it's a three-letter code, but doesn't add significant semantic meaning beyond what the schema provides. Baseline 3 is appropriate 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 tool's purpose with specific verb ('Get detailed information') and resource ('airport'), and distinguishes it from siblings by focusing on individual airport lookup rather than distance calculation or search. It explicitly mentions the IATA code requirement, which differentiates it from search_airports.

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 (getting detailed info about a specific airport by IATA code), but doesn't explicitly state when not to use it or name alternatives. The sibling tool names suggest alternatives (calculate_distance, search_airports), but the description doesn't guide the agent on choosing between them.

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

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

With no annotations, the description carries the burden. It discloses rate limiting (5/day) and content guidelines, which is sufficient for a non-destructive, feedback 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?

Three efficient sentences with no filler. Purpose first, then usage, then constraint. Very concise.

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

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

For a simple feedback tool with no output schema, the description covers purpose, parameters, and constraints adequately. Nested context object is described in 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 covers all params with descriptions. The description adds behavioral guidance on message content (not including user prompts), which enriches usage beyond schema.

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

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

The description clearly states the tool sends feedback, enumerates use cases (bug reports, feature requests, etc.), and is distinct from sibling tools.

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

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

Explicitly tells when to use (feedback types) and what to include/exclude (describe Pipeworx tools/data, not user prompts), plus mentions rate limit.

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

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

Annotations already indicate read-only and non-destructive behavior. The description goes further by detailing the internal process: walking child markets, extracting dates/thresholds, sorting, and reporting violations. It explains the monotonicity rule and what constitutes an arbitrage, providing comprehensive 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 informative but somewhat lengthy. It front-loads the purpose and then explains the logic, which is good. However, it could be slightly more concise without losing clarity. The structure is logical and easy to follow.

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 (one parameter, no output schema, no enums), the description is remarkably complete. It explains the arbitrage scenario, input format, internal logic, and return format (list of entries). No output schema exists, so the description effectively covers what the tool returns.

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

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

The schema covers the single parameter 'event' with a description. The tool description adds significant value by clarifying that it can be a slug or full URL and explaining how it's used to fetch event details. This goes beyond the schema baseline (schema description coverage is 100%), earning a 4.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities in Polymarket events by checking monotonicity violations. It uses specific verbs (find, check, report) and provides a concrete example, 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?

The description instructs users to pass a Polymarket event slug or URL, which is clear. It explains the condition under which to use it (when there are multiple 'by date' or 'by threshold' markets). While it doesn't explicitly mention when not to use it or alternative tools, the context is well 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?

Description details the process (scans top markets, groups by asset, fetches price history once, computes model probability) and outputs. Annotations already indicate read-only and non-destructive, and description adds valuable behavioral context beyond annotations.

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

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

Three sentences with no wasted words. Front-loaded with purpose, then methodology, then use case. Efficient 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?

No output schema, but description explains return format (top N ranked by edge magnitude with suggested trade direction). Combined with annotations (readOnlyHint, openWorldHint), it is sufficient for a discovery tool.

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

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

Input schema covers all 3 parameters with descriptions (100% coverage). Description provides context (e.g., 'Top N edges to return after ranking') but does not add significant meaning beyond what schema already offers. 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 states the specific action: scanning Polymarket markets, using Pipeworx data and a lognormal model, returning markets with highest disagreement. Distinguishes from sibling tools like 'polymarket_arbitrage' and 'bet_research'.

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

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

Explicitly states the use case: 'what should I bet on today' and that it avoids manual paging. No explicit when-not-to-use or alternative comparisons, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's dual functionality (retrieve by key vs. list all) and persistence across sessions. However, it doesn't mention error handling, rate limits, or authentication requirements, which would be valuable additions.

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 with two sentences that each serve distinct purposes: the first explains functionality, the second provides usage context. There's zero wasted verbiage and the information is front-loaded effectively.

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

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

Given the tool's moderate complexity (dual functionality, session persistence) and lack of annotations/output schema, the description does well but has minor gaps. It explains what the tool does and when to use it, but doesn't describe return formats, error conditions, or data persistence details that would be helpful 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?

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic implication of omitting the key parameter ('omit to list all keys'), which goes beyond the schema's technical description. This elevates the score above baseline.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from sibling tools 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 usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains when to omit the key parameter ('omit key to list all keys'), giving clear operational instructions.

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

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

No annotations provided, so description carries full burden. It discloses fan-out behavior to SEC, GDELT, USPTO, explains since format options, and describes return structure. Could add latency or limits but current detail is strong.

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

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

Four sentences, front-loaded, no fluff. Each sentence adds distinct information: purpose, fan-out, since format, return structure, and use case.

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 3 required params, no output schema, and no annotations, the description covers purpose, behavior, parameter semantics, and return shape. Could mention pagination or time range limits but 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 3. Description adds value by clarifying the type enum limitation ('Only company supported today') and providing usage examples ('30d or 1m for typical monitoring'), exceeding schema 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?

Description clearly states 'What's new about an entity since a given point in time' with specific verb+resource. Distinguishes from siblings like entity_profile by focusing on delta and mentioning fan-out to multiple sources.

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

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

Explicitly says 'Use for brief me on what happened with X or change-monitoring workflows.' Provides clear context but does not explicitly list when not to use or alternatives, though sibling differentiation is implicit.

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 (implied by 'Store'), specifies persistence behavior ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and indicates it's for session-scoped storage ('in your session memory'). It doesn't mention error conditions, rate limits, or specific permission requirements, but covers the essential operational context.

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

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

The description is perfectly concise with two sentences that each earn their place: the first states the core functionality with examples, and the second adds crucial behavioral context about persistence. There's no wasted language, and information is front-loaded with the primary purpose stated immediately.

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 write operation tool with no annotations and no output schema, the description provides good contextual completeness. It covers the purpose, usage guidelines, and key behavioral aspects (persistence differences). However, it doesn't describe what happens on success (e.g., confirmation message) or failure (e.g., error conditions), which would be helpful given the absence of 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 schema already fully documents both parameters (key and value). The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It provides general context about what to store but no additional syntax, format, or constraints for the parameters themselves.

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 siblings by focusing on storage rather than retrieval (recall) or deletion (forget). It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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

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

The description explicitly states when to use this tool ('to save intermediate findings, user preferences, or context across tool calls') and implicitly distinguishes it from alternatives by not overlapping with sibling tools like recall (for retrieval) or forget (for deletion). It also provides context on persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), guiding usage based on user authentication status.

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 must disclose behavior. It details accepted input formats (ticker, CIK, name) and return fields (ticker, CIK, company name, resource URIs). Does not cover error handling or auth, but for a read-like tool, 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?

Two dense sentences, front-loaded with purpose. Every sentence provides essential information: purpose, input options, and output summary.

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?

Tool has only 2 parameters with full schema descriptions, no output schema, and no nested objects. Description covers input formats, output structure, and version constraint. Sufficient for an entity resolution tool of moderate complexity.

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

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

Schema coverage is 100%. Description adds concrete examples like 'AAPL', '0000320193', 'Apple', and clarifies that 'type' is limited to 'company' in v1. This adds meaning beyond the enum 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?

Description clearly states verb 'Resolve' and resource 'entity to canonical IDs across Pipeworx data sources'. It specifies v1 supports 'company' type and lists accepted inputs (ticker, CIK, name). Distinguishes from siblings 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?

Explicitly states when to use (single call for canonical IDs). Implies efficiency gain over multiple calls but does not specify when not to use or name alternative tools. Good but lacks explicit exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation (implying read-only, non-destructive) and specifies a pagination limit ('Returns up to 30 results per page'). However, it lacks details on permissions, rate limits, error handling, or the exact return format, which are 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: two concise sentences that directly state the purpose and key behavioral constraint (pagination limit). Every sentence earns its place with no wasted words, making it efficient and easy to parse.

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 with pagination), no annotations, and no output schema, the description is partially complete. It covers the basic operation and pagination but lacks details on output structure, error cases, or integration with sibling tools. It's adequate as a minimum viable description but has clear gaps for full contextual understanding.

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 ('query' and 'page') thoroughly. The description adds minimal value beyond the schema: it reiterates that 'query' can be for 'name, city, or country' (which is in the schema) and implies pagination with 'per page' (aligned with the 'page' parameter). Baseline 3 is appropriate as 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 tool's purpose: 'Search for airports by name, city, or country.' It specifies the verb (search) and resource (airports), and mentions the searchable fields. However, it doesn't explicitly differentiate from sibling tools like 'get_airport' (which likely retrieves a single airport by ID) or 'calculate_distance', so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_airport' or 'calculate_distance', nor does it specify scenarios where this search tool is preferred over direct retrieval or other operations. Usage is implied by the purpose but not explicitly 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?

With no annotations, the description carries the full burden. It discloses the returned verdict types, structured form, citation format, and delta. It also notes the version limitation (v1, company-financial). Lack of auth or rate limit info is acceptable for a read-only tool, but not exhaustive.

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, information-dense paragraph without redundant sentences. It is front-loaded with the core purpose and then provides details. Could be slightly more structured (e.g., bullet points) but remains concise.

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

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

For a single-parameter tool with no output schema, the description comprehensively covers input, output, domain, version, and even compares to manual alternatives. No significant gaps given the tool's simplicity.

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

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

Schema coverage is 100% with a clear parameter description. The description adds specific examples and clarifies the nature of acceptable claims, enhancing meaning beyond the schema baseline. No additional constraints or details needed.

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

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

The description states a specific verb ('Fact-check') and resource ('natural-language claim against authoritative sources'), specifies the domain (company-financial claims), and distinguishes from siblings like compare_entities or ask_pipeworx by detailing the structured output and targeted use case.

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 clearly indicates when to use the tool (for fact-checking claims, especially company-financial) and mentions that it replaces multiple agent calls, implying it is more efficient. However, it does not explicitly state when not to use it or list alternative sibling tools for edge cases.

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