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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: it accepts natural language questions, automatically selects tools and fills arguments, and returns results. However, it doesn't mention limitations like rate limits, authentication needs, or error handling, leaving some 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?

The description is efficiently structured: first sentence states the core functionality, second explains the automation benefit, third provides usage guidance, and examples illustrate scope. Every sentence adds value without redundancy, making it easy 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 complexity (natural language processing with automated tool selection) and lack of annotations/output schema, the description does well by explaining the workflow and providing examples. However, it doesn't detail what types of data sources are available or potential limitations, which could leave the agent uncertain about edge 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 has 100% description coverage for its single parameter, so the baseline is 3. The description adds value by explaining the parameter's purpose beyond the schema's 'natural language' note: it emphasizes plain English questions and provides concrete examples ('What is the US trade deficit with China?'), enhancing understanding of expected input format.

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 it from siblings by emphasizing natural language interaction versus tool-specific 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 'recall' by positioning it as a high-level query interface. The examples provide concrete scenarios for appropriate 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, description must cover behavioral traits. It mentions returns paired data and resource URIs, and lists the fields for each type. However, it does not disclose any mutability, prerequisites, error conditions, or performance characteristics.

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-loaded with purpose, no superfluous words. Efficiently conveys key 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 complexity of two entity types with different fields, and no output schema, the description adequately specifies what data is returned for each type. It is complete enough for an agent to understand the tool's capability.

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 the description adds little beyond what the schema already provides. It reiterates the type and values descriptions but does not introduce new semantic 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 compares 2-5 entities side by side, with distinct fields for company vs drug. The verb 'compare' is specific and the tool distinguishes itself from siblings which include individual entity lookups 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?

Explicitly says when to use it: for comparing entities, and mentions it replaces 8-15 sequential calls, implying efficiency. Does not specify when not to use it, but the usage 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 key behaviors: it's a search operation (implied read-only), returns relevant tools based on a query, and has a specific use case context (large tool catalogs). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.

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

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

The description is perfectly concise and front-loaded: two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial usage guidance without any 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), no annotations, and no output schema, the description is mostly complete. It explains purpose, usage context, and what it returns, but lacks details on output format, error handling, or performance characteristics that would be helpful 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 fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what's in the schema, such as query examples or limit implications. 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 tool's purpose with specific verbs ('Search', 'Returns') and resource ('Pipeworx tool catalog'), and distinguishes it from siblings by specifying it's for searching when many tools are available. It explicitly mentions what it returns ('most relevant tools with names and descriptions').

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 alternative context (vs. not using it when fewer tools are available). It directly addresses when to use it versus alternatives.

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

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

With no annotations, the description carries full burden. It lists what data is returned (SEC filings, XBRL, patents, news, LEI) and mentions citation URIs. It does not mention rate limits or auth, but for a read 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?

The description is well-structured and front-loaded. It efficiently lists data sources and alternatives. Slightly verbose at the end, but each sentence adds utility.

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

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

The description covers the primary data categories but lacks detail on return structure (e.g., exact fields). With no output schema, more specifics about the response format would improve 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?

Schema coverage is 100%, but the description adds valuable context: type only supports 'company', value can be ticker or CIK, and names require resolve_entity first. This goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: getting a full entity profile across multiple Pipeworx packs. It lists specific data sources and distinguishes from the sibling usa_recipient_profile for federal contracts.

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 that it replaces 10-15 sequential calls, and explicitly tells agents to use usa_recipient_profile for federal contracts. However, it does not fully cover when to use alternatives like compare_entities or resolve_entity.

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. While 'Delete' implies a destructive mutation, it doesn't specify whether the deletion is permanent or reversible, what permissions are required, error conditions (e.g., what happens if the key doesn't exist), or any rate limits. This leaves significant behavioral gaps for a destructive operation.

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

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

The description is perfectly concise at 5 words, front-loading the essential action ('Delete') and resource. Every word earns its place with zero waste or 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?

For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't address critical context like what constitutes a 'stored memory', whether deletion is permanent, what response to expect, or error handling. The agent lacks sufficient information 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%, so the schema already fully documents the single 'key' parameter. The description adds no additional semantic context about the key parameter beyond what's in the schema (e.g., format examples, constraints, relationship to other tools). 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 doesn't explicitly distinguish from sibling tools like 'recall' or 'remember', but the verb 'Delete' provides clear differentiation from those read/create 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 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 'recall' (which presumably retrieves memories) or 'remember' (which presumably creates 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?

No annotations are provided, so the description carries full burden. It mentions what metadata is extracted but doesn't disclose behavioral traits like rate limits, authentication requirements, error handling, or what 'and more' encompasses. The description is functional but lacks operational context needed for an agent to use it effectively.

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 concise - a single sentence that communicates the core functionality. It's front-loaded with the main purpose and includes relevant examples. There's no wasted verbiage, though it could be slightly more structured with clearer separation of metadata types.

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 (metadata extraction from URLs) and lack of annotations/output schema, the description is minimally adequate. It covers what the tool does but lacks important context about limitations, return format, error conditions, and how it differs from the sibling screenshot tool. The 'and more' is vague and doesn't provide complete information.

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

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

Schema description coverage is 100% (the single parameter 'url' is fully described in the schema), so the baseline is 3. The description adds no additional parameter semantics beyond what's in the schema - it doesn't elaborate on URL format requirements, supported protocols, 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: 'Extract metadata from any URL' with specific examples of what metadata is extracted (title, description, image, etc.). It uses a specific verb ('extract') and resource ('metadata'), but doesn't explicitly distinguish it from its sibling tool 'take_screenshot' which serves a different function.

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

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

The description provides no guidance on when to use this tool versus alternatives. While it's clear this is for metadata extraction, there's no mention of when to use it instead of 'take_screenshot' or other potential tools. No exclusions, prerequisites, or alternative scenarios are mentioned.

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

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

With no annotations provided, the description carries full burden. It discloses rate limiting (5/day) and that the tool is free, but does not explain side effects, data handling, or whether feedback is stored or anonymous. More detail would improve 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, concise paragraph of three sentences. It front-loads the purpose and uses, then adds usage restrictions and rate limits. No wasted words.

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

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

Given the tool's simplicity (no output schema, no annotations), the description covers purpose, usage guidelines, rate limits, and parameter expectations. It is fairly complete, though it could mention if feedback receives a response or is logged.

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

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

The input schema has 100% description coverage, providing a strong baseline. The description adds value by clarifying the 'type' enum uses, advising on message format (be specific, 1-2 sentences, 2000 chars), and emphasizing not to include the end-user's prompt verbatim.

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

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

The description explicitly states 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise). This clearly defines the tool's purpose and distinguishes it from sibling tools like ask_pipeworx or remember.

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 guidance on when to use the tool (for feedback types) and what to include (describe tried tools/data, avoid end-user's verbatim prompt). It also mentions rate limits. However, it does not explicitly state when not to use it or compare to alternatives, which could be inferred from 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 of behavioral disclosure. It clearly explains the dual functionality (retrieve by key or list all) and persistence across sessions, which are important behavioral traits. It doesn't mention error handling or performance characteristics, but covers the core behavior adequately.

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 states the dual functionality, and the second provides usage context. No wasted words, and the most important information is front-loaded.

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

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

For a tool with no annotations and no output schema, the description provides good coverage of what the tool does and how to use it. It explains the dual functionality, parameter semantics, and persistence across sessions. The main gap is the lack of information about return format or error conditions, which would be helpful given the absence of an output schema.

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

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

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 provides valuable guidance for proper tool invocation.

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 ('retrieve context you saved earlier') and when to omit parameters ('omit key to list all keys'). It distinguishes from alternatives by specifying this is for retrieving previously stored memories rather than discovering new tools or taking screenshots.

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 parallel fan-out behavior for company type, accepted since formats, and the return structure (structured changes, total_changes count, URIs). It does not mention rate limits, authentication, or error handling, but for a read operation, the transparency is good.

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

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

The description is concise (4 sentences) and front-loaded with the main action. Every sentence adds necessary information about behavior, inputs, outputs, or usage. There is 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 multi-source complexity, the description covers the main behavioral aspects and output structure. It lacks details on error handling or edge cases (e.g., no changes found), but overall it provides sufficient context for an agent to select and invoke the tool correctly.

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

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

Schema descriptions cover 100% of parameters, and the description adds significant value beyond that: it explains the implications of type='company' (fan-out), gives recommended defaults for since ('30d' or '1m'), and elaborates on value formats (ticker or CIK). This greatly enhances 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's purpose: getting 'what's new about an entity since a given point in time.' It explains the specific behavior for the company type, including the parallel fan-out to SEC, GDELT, and USPTO. This level of specificity distinguishes it from sibling tools like entity_profile or 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?

The description provides explicit usage scenarios: 'brief me on what happened with X' or change-monitoring workflows. This gives clear when-to-use guidance. However, it does not explicitly state when not to use this tool or mention alternative tools for other use cases, which would have made it a 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a write operation ('Store'), specifies persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and implies session-scoped storage. It does not cover aspects like error handling or size limits, but adds substantial value beyond basic purpose.

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 context and behavioral details. Every sentence earns its place by adding value (e.g., examples, persistence rules) without redundancy, making it efficiently structured and appropriately sized.

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 rules), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral aspects like persistence. However, it lacks details on return values or error cases, which would be helpful for full completeness.

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

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

The schema description coverage is 100%, 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 formatting constraints or usage tips, 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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). 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'), providing clear context. However, it does not mention when not to use it or name specific alternatives (e.g., 'recall' for retrieval), which prevents a perfect score.

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

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

Describes returned data (ticker, CIK, company name, resource URIs) and notes it's a single call. No annotations provided, so description carries full burden; remains transparent about output and efficiency.

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 highly informative sentences with no waste. Front-loaded with main purpose, followed by version details and examples. Every sentence 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?

Given simple 2-param tool with no output schema or annotations, description is fully adequate. Covers inputs, outputs, and efficiency gain. No gaps for an agent to decide 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 has full coverage of 2 parameters. Description enriches with examples (AAPL, 0000320193, Apple) and clarifies that 'type' currently only supports 'company', adding value 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?

Clearly states the tool resolves an entity to canonical IDs, specifies the verb 'resolve' and resource 'entity', and gives concrete examples for type 'company'. Distinguishes from siblings 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?

Explicitly mentions supported type 'company' and accepted input formats (ticker, CIK, name). Implicitly indicates use case via 'replaces 2–3 lookup calls', but lacks explicit when-not-to-use guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions returning 'the screenshot image URL' but omits critical details like authentication needs, rate limits, error conditions, image format, or whether the operation is read-only or has side effects. This leaves significant gaps for agent understanding.

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, front-loaded sentence that efficiently conveys the core functionality without any wasted words. It's appropriately sized for a simple tool with one parameter, making it easy for an agent to parse quickly.

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

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

Given the lack of annotations and output schema, the description is incomplete for a tool that performs an external operation (screenshot capture). It fails to address behavioral aspects like permissions, rate limits, or output details (e.g., image format, URL validity), which are crucial for reliable agent use.

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

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

The schema description coverage is 100%, with the single parameter 'url' well-documented in the schema. The description adds no additional semantic context beyond implying the URL is for a webpage, which is already covered. 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 specific action ('Take a screenshot') and resource ('of a webpage'), and distinguishes from the sibling tool 'get_metadata' by focusing on visual capture rather than metadata extraction. It's precise and 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 no guidance on when to use this tool versus alternatives, such as the sibling 'get_metadata' or other potential tools for webpage analysis. It lacks context about prerequisites, limitations, or typical use cases, offering only a basic functional statement.

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?

Despite no annotations, the description discloses key behavioral traits: it returns a verdict, extracted structured form, actual value with pipeworx:// citation, and percent delta. It also notes it replaces multiple calls and focuses on financial claims, which clarifies its scope and limitations.

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

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

The description is three sentences long, with the purpose stated first. Every sentence adds value: scope, return values, and efficiency benefit. No 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 single parameter and no output schema, the description adequately explains what is returned (verdict, values, citations) and the domain. It could mention potential prerequisites (e.g., internet access) or error conditions, but overall provides sufficient context for 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 the 'claim' parameter already having a detailed description including examples. The tool description adds context about the fact-checking process but does not significantly enhance understanding of the parameter itself, as the schema already provides sufficient semantics.

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's action: 'Fact-check a natural-language claim against authoritative sources.' It specifies the domain (company-financial claims) and sources (SEC EDGAR + XBRL), and distinguishes itself from siblings like ask_pipeworx (general Q&A) by focusing on verification with structured verdicts.

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

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

Explicitly says it supports company-financial claims for public US companies, implying when to use it. Mentions it replaces 4-6 sequential agent calls, indicating efficiency. However, it does not explicitly state when not to use it or provide direct alternatives, though the domain restriction serves as implicit guidance.

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