flights

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: Pipeworx picks the right tool and fills arguments automatically, and it returns results. However, it lacks details on limitations, such as rate limits, error handling, or what happens if no data source is found, which is important for a tool with no annotations.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. Every sentence adds value: explaining the tool's function, its automation benefits, and providing concrete examples. There is no wasted text, making it efficient and easy to understand.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The description is two sentences plus structured type-specific details. It is front-loaded with the core purpose and contains no redundant or irrelevant information.

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

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

Given the absence of an output schema, the description explains return fields for each type and mentions paired data and URIs. It lacks details on error handling or invalid inputs, but covers the essential context for using the tool.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For a destructive tool with no annotations and no output schema, the description is minimally adequate. It states the purpose and parameter, but lacks crucial context like deletion consequences, error handling, or return values. Given the complexity (mutation) and lack of structured data, it should provide more behavioral guidance to be complete.

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

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

Schema description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format, examples, or constraints. Baseline 3 is appropriate when the schema already fully describes parameters.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Two sentences front-load the core functionality (retrieve/list) and usage context, with zero wasted words. Each sentence earns its place by providing essential information without redundancy.

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

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

Given the tool's low complexity (1 optional parameter, no output schema), the description is mostly complete. It covers purpose, usage, and parameter semantics adequately. However, without annotations or output schema, it could benefit from more behavioral details like return format or error cases.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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