iplookup

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 explains that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which describes the tool's decision-making process. However, it doesn't mention limitations like rate limits, authentication requirements, or potential accuracy constraints. The behavioral description is helpful but not comprehensive.

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 efficiently structured: the first sentence states the core functionality, the second explains the mechanism, the third provides usage guidance, and the fourth gives examples. Every sentence adds value, and there's no redundant information. The description is appropriately sized for a single-parameter 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 complexity (natural language processing with backend tool selection), no annotations, and no output schema, the description does well but has gaps. It explains the tool's purpose and usage clearly but doesn't describe the format or structure of returned answers, potential limitations, or error conditions. For a tool that abstracts multiple data sources, more context about reliability and scope would be helpful.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds value by providing context about the parameter: it should be 'a question or request in natural language' and gives concrete examples ('What is the US trade deficit with China?', etc.). This enhances understanding beyond the schema's basic 'Your question or request in natural language' 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, fills the arguments'). It distinguishes itself from siblings by emphasizing natural language processing rather than requiring specific tool selection or schema knowledge.

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 stating users should use this when they want to ask questions in plain English rather than using specific tools. The examples further illustrate 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the batch processing capability, input limit (100 IPs), and output ordering (same order as input). However, it lacks details on error handling, rate limits, or authentication requirements, which are common for API tools, leaving some behavioral aspects unclear.

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 highly concise and well-structured in two sentences. The first sentence states the core functionality and constraint, while the second explains the output format. Every word earns its place with no redundancy, making it easy for an agent to parse and 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 the tool's moderate complexity (batch processing with a limit), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, input constraints, and output behavior. However, it lacks details on error responses or example outputs, which would be helpful for an agent to handle edge cases, slightly reducing 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, clearly documenting the 'ips' parameter as an array of IPv4/IPv6 addresses with a maximum of 100. The description adds minimal value beyond this, only reiterating the limit and input type. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't provide additional semantic context like format examples or validation rules.

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 ('look up geolocation') and resources ('multiple IP addresses'), and distinguishes it from the sibling tool 'geolocate_ip' by emphasizing batch processing ('in a single request'). It explicitly mentions the scope ('up to 100 IPs') and output format ('array of results'), 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 provides explicit usage guidance by specifying when to use this tool ('for multiple IP addresses in a single request') and implicitly when not to use it (for single IPs, suggesting the sibling 'geolocate_ip' as an alternative). It also sets clear constraints ('up to 100 IPs'), helping the agent choose appropriately based on input volume.

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 effectively discloses the return data (revenue, net income, etc., or adverse events, FDA approvals) and that it returns paired data with URIs. It does not mention potential side effects, but for a read-only comparison tool, this is sufficient.

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) and front-loaded with the purpose. Every sentence serves a clear function: purpose, type distinction, return values and efficiency benefit.

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 or annotations, the description adequately covers the tool's functionality, inputs, and returns. It could be more explicit about the structure of 'paired data', but overall it provides sufficient context 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 coverage is 100%, so baseline is 3. The description adds value by providing examples for the 'values' parameter (tickers for company, drug names for drug) and explaining how 'type' determines the data returned, beyond the schema descriptions.

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

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

The description clearly states the tool's action ('Compare 2–5 entities side by side in one call') and distinguishes between entity types with specific metrics, which differentiates it from siblings like '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 explains the tool's use for comparing multiple entities efficiently, noting it replaces 8–15 sequential calls. It doesn't explicitly state when not to use it, but the context is clear.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'the most relevant tools with names and descriptions,' which gives some insight into output behavior, but it lacks details on aspects like rate limits, authentication needs, error handling, or pagination. The description adds basic context but falls short of fully compensating for the absence of 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 purpose in the first sentence, followed by a clear usage guideline. Both sentences are essential and contribute directly to understanding the tool's role and application, with no wasted words or redundant information. It is efficiently structured and appropriately sized for its function.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is mostly complete. It covers the purpose, usage guidelines, and basic behavioral context effectively. However, without annotations or an output schema, it could benefit from more details on the return format or error conditions, slightly limiting completeness for a 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 description coverage is 100%, so the schema already documents both parameters (query and limit) thoroughly. The description does not add any additional meaning or clarification beyond what the schema provides, such as examples or usage tips for the parameters. This meets the baseline score of 3, as the schema handles the parameter documentation adequately.

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 ('Search the Pipeworx tool catalog'), the resource ('tool catalog'), and the method ('by describing what you need'), distinguishing it from sibling tools like batch_geolocate and geolocate_ip. It explicitly mentions returning 'the most relevant tools with names and descriptions,' making the purpose unambiguous and distinct.

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 specific condition (500+ tools) and a clear alternative scenario (using it as an initial step). This directly addresses when to use it versus other tools, offering strong contextual direction.

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; description carries full burden. Clearly states it's a single composite call replacing many sequential calls and returns pipeworx:// URIs. Does not detail error handling or rate limits, but otherwise transparent.

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 with no wasted words. First sentence lists data sources concisely; second provides crucial usage guidance. Front-loaded and efficient.

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?

Comprehensive for a complex multi-source tool: lists data sources, return format, input restrictions, and alternative for federal contracts. No output schema, but return value is described as citation URIs.

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

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

Schema coverage is 100%, and description adds key context: names not supported, use resolve_entity first, and for value, specifies ticker or CIK format. This goes beyond schema constraints.

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 returns a full company profile across multiple data sources, lists each source (SEC, XBRL, patents, news, LEI), and distinguishes from siblings like resolve_entity and compare_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?

Provides explicit when-to-use (company profiling) and when-not-to (federal contracts, refer to usa_recipient_profile). Also advises using resolve_entity if only a name is available.

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 this is a deletion operation, implying it's destructive, but doesn't specify whether deletion is permanent, reversible, requires confirmation, or has side effects. For a destructive tool with zero annotation coverage, this leaves critical behavioral traits undocumented.

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 communicates the core purpose without any wasted words. It's appropriately sized for a simple deletion operation and is front-loaded with the essential action. Every word earns its place in this minimal but complete statement.

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 operation with no annotations and no output schema, the description is insufficiently complete. It doesn't address what happens after deletion (success/failure indicators), whether the operation is idempotent, or what constitutes a valid memory key. The agent lacks critical context needed to use this tool safely and effectively.

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

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

Schema description coverage is 100%, with the single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional semantic context beyond what's already in the schema (e.g., key format, examples, or constraints). Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes from sibling 'recall' (which retrieves) and 'remember' (which stores), though doesn't explicitly mention these alternatives. The verb+resource combination is specific but could be more precise about what type of memory is being deleted.

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. While it's clear this deletes memories, there's no mention of prerequisites (e.g., memory must exist), consequences, or when to choose deletion over other operations. The agent must infer usage from the purpose alone without explicit context.

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

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

With no annotations provided, the description carries the full burden. It discloses the return data (country, region, city, etc.), which is useful behavioral context. However, it lacks details on rate limits, error handling, or data freshness, leaving gaps in transparency for a network-dependent 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, well-structured sentence that efficiently conveys purpose, scope, and return values without redundancy. Every element serves a clear informational role, making it highly concise.

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

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

Given the tool's low complexity (one parameter, no annotations, no output schema), the description is reasonably complete. It covers purpose, input type, and return data. However, without an output schema, it could benefit from more detail on response format 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?

The input schema has 100% description coverage, fully documenting the 'ip' parameter with examples. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 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 verb 'look up' and the resource 'geolocation, ISP, and network information for a single IP address', specifying both IPv4 and IPv6 support. It distinguishes from the sibling tool 'batch_geolocate' by emphasizing 'single IP address'.

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 context by stating 'for a single IP address', which suggests this tool is for individual lookups versus batch processing. However, it does not explicitly name the sibling tool 'batch_geolocate' as an alternative or provide exclusion criteria.

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

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

Discloses rate limiting (5 messages per identifier per day). No annotations present, so description carries burden. Lacks details on what happens after sending (e.g., confirmation, storage). Adequate but not rich.

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, no wasted words. Purpose first, then usage details, then rate limit. Well-structured and efficient.

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?

Missing output schema explanation (no return value). Does not define 'identifier' for rate limiting. Serviceable for a simple feedback tool but incomplete for full agent 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 coverage is 100% with detailed descriptions. Description adds extra guidance on being specific and length limits, enhancing clarity beyond schema.

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

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

Description clearly states 'Send feedback to the Pipeworx team' and lists specific use cases: bug reports, feature requests, missing data, or praise. Distinguishes itself from sibling query/utility tools.

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

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

Explicitly says when to use (for feedback types) and provides guidance on what to include (describe tools/data, avoid end-user prompt). Mentions rate limit. Could be improved by stating 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 explains the dual functionality (retrieve by key vs. list all) and persistence across sessions, which is valuable context. However, it doesn't describe error behavior (e.g., what happens with invalid keys), rate limits, or authentication requirements, leaving some behavioral aspects unclear.

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 efficiently structured in two sentences: the first explains the dual functionality, and the second provides usage context. Every word serves a purpose 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?

For a simple retrieval tool with one optional parameter and no output schema, the description provides adequate context about functionality and usage. It could be more complete by mentioning what format memories are returned in or error handling, but given the tool's simplicity, it covers the essential aspects well.

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 schema already documents the optional 'key' parameter. The description adds meaningful context by explaining the semantic difference between providing a key (retrieve specific memory) and omitting it (list all keys), which helps the agent understand when to use each mode.

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 in the session or in previous sessions.' It also specifies when to omit the key parameter to list all memories, offering clear usage 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?

With no annotations, the description carries the full burden and does well: it discloses parallel fan-out to multiple sources, accepted date formats, and return structure (structured changes + total_changes count + URIs). However, it lacks details on rate limits, authentication needs, or potential delays.

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 paragraph of 3-4 concise sentences. It efficiently conveys purpose, sources, parameter formats, and use cases. Slightly more structure (e.g., breaking into steps) could improve readability, but overall it is well-organized.

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 (multisource fan-out, multiple date formats, return structure), the description is complete. It covers entity type, parameter semantics, and output format despite no output schema. Minor omission: no mention of error handling or result limits.

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 good descriptions. The description adds extra value by explaining that type is limited to 'company', providing example formats for since (ISO or relative), and clarifying that value can be a ticker or zero-padded CIK. This goes beyond the schema alone.

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

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

The description clearly states the tool's purpose: retrieving recent changes for an entity since a given time. It specifies the entity type (company) and the sources it fans out to (SEC EDGAR, GDELT, USPTO), distinguishing it from sibling tools like entity_profile which provide full profiles, or compare_entities for 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 advises using the tool for 'brief me on what happened with X' or change-monitoring workflows, which is a clear when-to-use guideline. It implies usage context but does not explicitly list when not to use or compare with alternatives like entity_profile or compare_entities.

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

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which is critical for understanding tool behavior beyond basic storage. However, it does not mention rate limits, error conditions, or memory size 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?

Two sentences, zero waste: first states purpose with examples, second adds critical behavioral context (persistence rules). Every sentence earns its place, and 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 2-parameter tool with no annotations and no output schema, the description is mostly complete: it covers purpose, usage context, and key behavioral traits (persistence rules). However, it lacks details on return values or error handling, leaving minor gaps given the tool's 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description does not add meaning beyond what the schema provides (e.g., no additional syntax or format details). 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 verb ('Store') and resource ('key-value pair in your session memory'), with specific examples of what to store ('intermediate findings, user preferences, or context across tool calls'). It distinguishes from sibling 'recall' (which likely retrieves) and 'forget' (which likely removes).

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 ('save intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use or name alternatives (e.g., 'forget' for removal). It implies usage across sessions 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 full burden. It discloses that it's a single call and returns specific fields, but lacks details on error handling, permissions, 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?

The description is two concise sentences, front-loaded with the main purpose, and every sentence adds value without redundancy.

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

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

The description covers purpose, inputs, outputs, and benefit. It lacks details on failure modes or edge cases, but for a simple tool with no output schema, it is reasonably 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 baseline is 3. The description adds examples and context (e.g., 'v1: type="company"') but does not significantly enhance understanding beyond the schema.

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

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

The description states the specific verb 'resolve' and resource 'entity to canonical IDs', provides a concrete example (company type with ticker/CIK/name), and distinguishes itself from sibling tools by noting it replaces multiple 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 implies this tool is more efficient than alternatives (replaces 2-3 lookup calls) but does not explicitly state when to use or not use it, nor name specific alternative tools.

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

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

No annotations provided, so description carries full burden. It discloses return values (verdict types, structured form, citation, delta) and explains it replaces multiple sequential calls. Does not mention auth or rate limits, but these are less relevant for a read-like 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?

Four sentences, front-loaded with main purpose, each sentence adds value without redundancy. Extremely concise and 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 all return values (verdict, structured form, actual value, citation, delta). Also specifies domain limitations and supported sources. Complete for a single-parameter tool.

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

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

Only one parameter 'claim' with schema description coverage 100%. The tool description offers example claims but does not add significant meaning beyond the schema description. 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?

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies supported domain (company-financial for US public companies), and distinguishes itself by replacing multiple sequential 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?

Description explicitly states v1 supports only company-financial claims (revenue/net income/cash) for public US companies, and mentions it replaces 4-6 agent calls, providing clear context. It does not explicitly list exclusions but effectively sets scope.

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