xkcd

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 describes the tool's behavior: Pipeworx selects the data source and fills arguments automatically. However, it lacks details on limitations (e.g., rate limits, data source availability, error handling) or response format, which are important for a tool with no output schema. The description adds value but is incomplete for behavioral 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 well-structured and concise, with three sentences that each serve a purpose: stating the tool's function, explaining its advantage, and providing examples. There is no wasted text, and key information is front-loaded, making it efficient for an agent to parse.

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

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

Given the tool's complexity (natural language processing with automatic tool selection) and lack of annotations and output schema, the description is moderately complete. It explains the core functionality and usage but omits details on output format, error conditions, or data source constraints, which are important for contextual understanding. The examples help, but gaps remain.

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

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

The input schema has 100% description coverage, with the 'question' parameter documented as 'Your question or request in natural language.' The description reinforces this by stating 'Ask a question in plain English' and providing examples, adding context about the expected format. Since schema coverage is high, the baseline is 3, but the description enhances understanding with practical examples, justifying a higher score.

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'), distinguishing it from sibling tools like 'discover_tools' or 'recall' which likely have different functions.

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

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

The description provides explicit usage guidance: 'No need to browse tools or learn schemas — just describe what you need.' It indicates when to use this tool (for natural language queries) versus alternatives (implying other tools might require browsing or schema knowledge), and includes examples like 'What is the US trade deficit with China?' to 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?

Annotations provide readOnlyHint=true and destructiveHint=false, and the description aligns by stating it 'pulls data' and 'returns' results. Beyond annotations, it details the process: resolving market, classifying bet, fanning out to packs, and comparing probabilities—adding significant 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, well-organized paragraph: purpose, input, process, output, use cases, and value proposition. Each sentence earns its place without redundancy or unnecessary detail.

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

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

With no output schema, the description explains the return format as 'evidence packet plus market-vs-model comparison'. It covers the input, process, and use cases. However, it omits details on the comparison's structure or evidence packet contents, which would be helpful for a complex tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying that 'market' can be a slug, URL, or question text, and that 'depth' controls the number of evidence sources. This goes beyond the schema's enum and default 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 specifies a clear verb ('Research'), resource ('Polymarket bet'), and output ('evidence packet plus market-vs-model comparison'). It distinguishes from siblings by noting it is the core demo product and that agents using it convert better, avoiding generic actions like '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 gives explicit use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It implies that for general Pipeworx queries, 'ask_pipeworx' would be more appropriate, but does not explicitly state when not to use this tool or list 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?

No annotations are provided, so the description fully carries the burden. It discloses return format (paired data + URIs) and the high-level data fields for each type. It does not mention idempotency or side effects, but as a read operation this is acceptable.

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

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

Two sentences with no wasted words. The purpose and key details are front-loaded. Every sentence adds essential 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?

Despite no output schema, the description covers the return format, parameter details, and use case. It is fully adequate for an AI 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?

Input schema has 100% coverage, but the description adds significant value by explaining the meaning of each field (e.g., revenue, net income for companies; adverse-event reports for drugs) and the expected format (tickers/CIKs vs. drug names).

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, specifying distinct fields for company and drug types. It differentiates itself from sequential calls and provides concrete output examples.

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 recommends this tool to replace 8–15 sequential agent calls, giving clear context for when to use it. It does not provide explicit when-not scenarios, but the efficiency gain is well highlighted.

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 of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools based on natural language queries. 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 with two sentences that each serve distinct purposes: the first explains what the tool does, the second provides usage guidance. Every word earns its place with no redundancy or unnecessary 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?

For a search tool with no output schema, the description provides good context about what the tool returns ('most relevant tools with names and descriptions'). However, it doesn't specify the format or structure of the returned data, which would be helpful since there's no output schema. The guidance about when to use the tool is excellent contextual 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?

The input schema has 100% description coverage, so parameters are well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's in the schema, but the schema already provides good documentation for both parameters (query and limit).

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

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

The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes this tool from its siblings (get_comic, get_latest, random_comic) by focusing on tool discovery rather than comic retrieval.

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

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

The description 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.' This gives clear context about the appropriate scenario (large tool catalog) and positioning (first step in workflow).

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 fully shoulders the burden, disclosing that it bundles multiple data sources, returns URIs, and excludes federal contracts. It is transparent about capabilities 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 concise—only a few sentences—with no redundancy. Key information is front-loaded (purpose, contents, alternatives). Every sentence adds substantive 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?

Given two parameters with full schema and no output schema, the description provides a comprehensive overview: data types returned, format (URIs), and exclusion (federal contracts). It is complete for an agent to understand what to expect.

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?

100% schema coverage, and the description adds value by explaining that type only supports 'company' and value can be ticker or CIK, with a note that names are not supported. This goes beyond the schema to aid correct use.

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 it returns a full profile of an entity across all Pipeworx packs, listing specific data types for companies (SEC filings, XBRL, patents, news, LEI) and mentioning citation URIs. It distinguishes from a sibling tool (usa_recipient_profile) and describes its value as replacing 10-15 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?

The description explicitly tells when to use this tool (for comprehensive entity profile) and when not to (for federal contracts, use usa_recipient_profile). It also advises using resolve_entity if only a name is available, providing clear 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 full burden for behavioral disclosure. 'Delete' implies a destructive mutation, but the description doesn't specify whether deletion is permanent, requires specific permissions, or what confirmation/response to expect. It lacks critical behavioral context 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 a single, efficient sentence with zero wasted words. 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 insufficient. It doesn't explain what constitutes a 'stored memory', how keys are structured, what happens on success/failure, or return values. The context demands more completeness for safe 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 description coverage is 100%, so the schema already documents the single 'key' parameter adequately. The description adds no additional meaning beyond what's in the schema (both state it's the 'memory key to delete'), 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 action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' or 'remember', which likely involve memory retrieval/creation rather than deletion.

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. With siblings like 'recall' (likely retrieving memories) and 'remember' (likely storing memories), there's no indication of prerequisites, when deletion is appropriate, or what happens if the key doesn't exist.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's purpose but does not describe behavioral traits such as whether it's read-only, potential rate limits, error handling, or response format. This leaves significant 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 a single, efficient sentence that front-loads the core purpose without any wasted words. It is appropriately sized for a simple tool, making it easy to understand at a glance.

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

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

Given the lack of annotations and output schema, the description is incomplete. It does not address behavioral aspects like safety, performance, or return values, which are crucial for an AI agent to use the tool effectively. For a tool with no structured data support, more context is needed.

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 single parameter 'number'. The description adds no additional parameter semantics beyond what the schema provides, such as validation rules or examples. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('Get') and resource ('a specific XKCD comic by its number'), distinguishing it from sibling tools like 'get_latest' and 'random_comic' which serve different purposes. It precisely communicates the tool's function without ambiguity.

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 implicitly suggests usage when a specific comic number is known, but it does not explicitly state when to use this tool versus alternatives like 'get_latest' or 'random_comic'. It provides clear context for its use case but lacks explicit exclusions or named 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?

No annotations are provided, so the description carries the full burden. It describes what data is returned (title, image, alt text) and that it fetches the 'latest published' comic, which adds useful context. However, it lacks details on potential errors, rate limits, or network behavior, leaving gaps in behavioral disclosure 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 a single, efficient sentence that front-loads the key information: the action, resource, and returned data. There is no wasted text, and it is appropriately sized for a simple tool with no parameters.

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 (0 parameters, no output schema, no annotations), the description is largely complete for its purpose. It specifies what data is returned, which compensates for the lack of output schema. However, it could be more complete by mentioning potential limitations or errors, though this is a minor gap for such a straightforward 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose and output. This meets the baseline for tools with no parameters, as it adds value without redundancy.

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') and resource ('latest published XKCD comic'), including the exact data returned ('title, image, and alt text'). It distinguishes from siblings by specifying 'latest' versus 'get_comic' (likely by ID) and 'random_comic' (random selection).

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 specifying 'latest published' comic, suggesting it's for retrieving the most recent one. However, it does not explicitly state when to use this tool versus alternatives like 'get_comic' or 'random_comic', nor does it mention any exclusions or prerequisites.

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 rate limit (5 messages per day) and that it's free, but does not describe the result or expected outcome after sending feedback.

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, each with distinct purpose: purpose, use cases, content guidelines, rate limit. No redundancy or unnecessary 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?

For a simple feedback tool with no output schema, the description covers purpose, when to use, content guidelines, and rate limits. Sufficient for an agent to correctly invoke 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 covers 100% of parameters with descriptions, so baseline is 3. Description adds value with guidance on message content (not including user prompt), but does not significantly enhance type or context semantics beyond schema.

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

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

The description clearly states 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, etc.), which distinguishes it from sibling tools like ask_pipeworx or get_comic.

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

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

Explicitly states when to use (bug reports, feature requests, etc.) and provides content guidelines ('Describe what you tried... do not include end-user prompt'). Context with siblings makes alternatives clear, though no explicit when-not-to-use is given.

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

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

The description details the process: walks child markets, extracts dates/thresholds, sorts, and checks for violations. Annotations confirm read-only and non-destructive behavior, which aligns with the description's logic.

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 focused and informative, explaining the concept concisely while providing necessary detail. Slightly verbose but every sentence adds value; 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?

For a simple schema and no output schema, the description sufficiently covers the tool's purpose, usage, and return format. The complexity of arbitrage detection is well explained.

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

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

The single 'event' parameter is described in both schema and description. The description adds context on how the parameter is used to identify the event and its child markets, beyond the schema's basic string type.

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 finds arbitrage opportunities within a Polymarket event by checking monotonicity violations, with a specific example of the price ordering rule. It is distinct from sibling tools like bet_research 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 explains when to use the tool (to check for monotonicity violations in a single event) and what input to provide (event slug or URL). It lacks explicit guidance on when not to use it or alternatives, but the context is clear enough.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds value by detailing the model (lognormal from FRED + coinpaprika), the grouping and ranking process, and the output format. No contradiction with annotations.

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

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

The description is slightly long but every sentence earns its place: it explains the model, the process, and the intended use case. Front-loaded with the core purpose.

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

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

With no output schema, the description adequately explains the return format (top N ranked by edge magnitude with suggested trade direction). Combined with annotations and parameter coverage, the tool is well-specified for the given 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 description doesn't need to add much. It provides context like 'V1 covers crypto-price bets' but adds no new parameter-specific details beyond the schema's descriptions.

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

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

Description clearly states the tool scans high-volume Polymarket markets, computes where Pipeworx data disagrees with market price using a lognormal model, and returns top N ranked by edge magnitude with suggested trade direction. This is specific and distinguishes it from siblings like polymarket_arbitrage.

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

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

The description explicitly labels the tool as built for the 'what should I bet on today' question, indicating when to use it. While it doesn't list alternatives or when-not-to-use, the context is clear enough for an agent to infer 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?

No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits such as rate limits, error handling, or response format. The description is minimal and doesn't add context beyond the basic operation.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand 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 incomplete for a tool that likely returns comic data. It doesn't explain what is returned (e.g., image URL, title, metadata), leaving gaps in understanding the tool's behavior and output.

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?

There are 0 parameters, and schema description coverage is 100%, so no additional parameter information is needed. The description doesn't add param details, but with no parameters, this is acceptable, aligning with the baseline for zero 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 ('Get') and resource ('a random XKCD comic from the full archive'), specifying it's random and from the full archive. This distinguishes it from siblings like 'get_comic' (likely specific) and 'get_latest' (most recent).

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 a random comic, but doesn't explicitly state when to use this tool versus alternatives like 'get_comic' or 'get_latest'. It provides context (random, full archive) but lacks explicit guidance on exclusions or comparisons.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: retrieving stored memories by key or listing all keys, and clarifies persistence across sessions ('saved earlier in the session or in previous sessions'). It doesn't mention error handling or performance characteristics, but covers core functionality well.

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

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

The description is perfectly concise and well-structured in two sentences. The first sentence states the dual functionality, the second provides usage context. Every word earns its place with zero 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?

For a tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and how parameters affect behavior. It could benefit from mentioning the return format (e.g., memory content vs. key list), but otherwise covers the essentials 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 has 100% description coverage, so the baseline is 3. The description adds significant value by explaining the semantic meaning of omitting the key parameter ('omit to list all keys'), which clarifies the dual functionality beyond what the schema's technical description 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'), and distinguishes it from sibling tools like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving context saved earlier in sessions.

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

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

The description provides explicit usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions' establishes the primary use case. It also specifies when to omit the key parameter ('omit key to list all keys'), giving clear operational instructions.

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

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

Since no annotations are provided, the description carries the full burden. It discloses parallel execution behavior, accepted parameter formats (ISO date or relative), and the return structure (structured changes, total_changes count, pipeworx:// URIs). This is good, but lacks details on error handling or rate limits.

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

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

The description is concise at 5 sentences, front-loaded with the core purpose, then details parameters and return format. Every sentence adds value without redundancy or filler.

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

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

Given no output schema, the description adequately explains the return structure. It covers all parameters, usage context, and the tool's behavior (parallel fan-out). There are no obvious gaps for a tool of this complexity.

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

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

Schema coverage is 100%, but the description adds significant value: it clarifies that 'since' accepts ISO dates or relative strings, suggests typical monitoring values ('30d' or '1m'), and specifies that 'value' accepts tickers or CIK numbers with examples. This goes well beyond the schema definitions.

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

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

Description explicitly states the tool's purpose: find what's new about an entity since a point in time. It details the supported type (company) and the parallel fan-out to SEC EDGAR, GDELT, and USPTO. This clearly distinguishes it from siblings like entity_profile (static profile) 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 usage guidance: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This clarifies the intended use cases. However, it does not mention when not to use it or alternatives, which prevents a higher score.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence difference between authenticated users ('persistent memory') and anonymous sessions ('last 24 hours'), and the cross-tool context capability ('across tool calls'). However, it doesn't mention potential limitations like storage capacity or rate limits.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, the second adds crucial behavioral context about persistence differences. 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 2-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and key behavioral aspects. The main gap is the lack of information about return values or error conditions, which would be helpful given there's 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?

With 100% schema description coverage, the schema already documents both parameters thoroughly. The description doesn't add meaningful parameter semantics beyond what's in the schema - it mentions 'key-value pair' generically but doesn't provide additional context about parameter usage, constraints, or examples beyond the schema's descriptions.

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

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

The description clearly states the tool's purpose with specific verb ('Store') and resource ('key-value pair in your session memory'), distinguishing it from siblings like 'recall' (retrieve) and 'forget' (delete). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), providing concrete examples.

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 the tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name specific alternatives. It distinguishes from 'recall' by implication (store vs. retrieve) but doesn't explicitly compare with other siblings.

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

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

No annotations are provided, so the description carries the full burden. It accurately describes the operation as a single call resolving entities, but lacks details on side effects, authentication, or error handling, though for a resolution tool this is acceptable.

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, front-loaded with the main action, and includes key details without redundancy. 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 no annotations or output schema, the description sufficiently covers the tool's purpose, inputs, outputs, and use-case (replacing multiple calls). The tool is simple, and the description is 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%, and the description adds value by providing examples and stating the return format. It does not repeat the schema but enhances it with context about the return and the purpose of each parameter.

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

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

The description clearly states the tool resolves an entity to canonical IDs, gives specific examples for type and value, and mentions the return values including ticker, CIK, company name, and resource URIs. This is a specific verb+resource with clear scope.

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

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

The description explains that it replaces 2-3 lookup calls, implying efficiency gains over alternative approaches. However, it does not explicitly contrast with sibling tools like ask_pipeworx or state when not to use it.

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

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

With no annotations, the description bears full burden. It discloses supported domains, data sources, and return fields (verdict, structured form, actual value, citation, delta). However, it omits behavioral traits like non-destructive nature, error handling, or limitations (e.g., only US public companies, specific metrics). This is adequate but not exhaustive.

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

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

The description is three sentences, front-loaded with purpose, and efficiently conveys key points without redundancy. 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?

Given the single parameter and no output schema, the description covers the return values and source. It provides sufficient context for selection and invocation, though it could mention error conditions or 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?

Schema coverage is 100% with an example, so the description's addition is minimal. The description reiterates the parameter's purpose without adding deeper semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states it fact-checks natural-language claims against authoritative sources, specifically company-financial claims, using SEC EDGAR + XBRL. This distinguishes it from siblings like ask_pipeworx or compare_entities which serve different purposes.

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 when to use it—for verifying financial claims about public US companies—and notes it replaces multiple sequential agent calls, suggesting efficiency. However, it does not explicitly state when not to use it or list alternatives, leaving some ambiguity.

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