holidays

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds critical behavioral context: default model is free Workers AI, using Anthropic requires a BYO API key (cost implication), and the return structure includes per-model details. No contradictions with annotations.

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

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

The description is 6 sentences, each adding value. Front-loaded with the main action, then details on default model, cost, return format, and use cases. No superfluous 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?

Despite having no output schema, the description clearly explains the return structure: per-model fields (score, confidence, signals, raw_response) plus a combined view. The 4 parameters are explained adequately. For a probe tool, this 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?

All 4 parameters have descriptions in the schema (100% coverage). The description adds meaning beyond the schema by explaining the default model, cost implications for _apiKey, and the role of context for disambiguation. It could have elaborated on expected values for models, but the schema already specifies supported strings.

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 uses specific verbs ('probe', 'score') and identifies the resource ('LLMs for what they know about a business / brand / product / topic'). It clearly distinguishes itself from sibling tools (e.g., scan_competitor_ai_presence) by focusing on visibility scoring per model.

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 lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It provides clear context for when to use the tool, though it does not explicitly state when not to use it or directly compare to similar 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: Pipeworx 'picks the right tool, fills the arguments, and returns the result,' indicating automated tool selection and parameter filling. However, it doesn't mention potential limitations like rate limits, error handling, or authentication requirements.

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: it starts with the core functionality, explains the automation benefit, and provides concrete examples. Every sentence adds value without redundancy, 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 the tool's complexity (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 answers or data sources to expect, which could be important for an AI agent to manage expectations.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining that the 'question' parameter should be 'in plain English' and providing examples like 'Look up adverse events for ozempic,' which clarifies the expected format and scope beyond the schema's '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 distinguishes itself from siblings by emphasizing natural language interaction without needing to browse tools or learn schemas.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly suggesting not to use other tools for simple queries) 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 already indicate safe read (readOnlyHint=true, destructiveHint=false). The description adds behavioral context: it resolves market, classifies bet, fans out to packs, and returns evidence packet with comparison. No contradictions; it enhances understanding of the tool's internal process beyond annotations.

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

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

The description is well-structured: starts with purpose, then input format, then behavior, then use cases. Every sentence adds value with no redundancy. It is appropriately sized for a tool of this complexity.

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

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

Despite no output schema, the description explains return values (evidence packet and market-vs-model comparison) and mentions classification types. It covers enough for an agent to understand the tool's functionality, though it lacks details on error handling or evidence packet format specifics.

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 baseline is 3. The description reinforces parameter meaning for both 'market' (three input forms) and 'depth' (quick vs thorough) by explaining the fan-out logic. It adds context about classification and data sources, slightly elevating understanding 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 researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and distinguishes itself from siblings by being the only bet-specific research tool, as seen in the sibling list which includes general tools like ask_pipeworx and entity_profile.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It also implies when not to use by stating agents that use this tool convert better than those discovering packs themselves, but does not provide explicit when-not or 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, so description carries full burden. Discloses return of paired data and pipeworx:// URIs, mentions data sources (SEC EDGAR, FDA). Lacks details on 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?

Three sentences with zero waste. Front-loaded with purpose, then details types and benefits. 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 output schema, description adequately explains return value nature (paired data + URIs) and data sources. Covers all critical info for an agent to decide and invoke 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 coverage is 100%, but description adds meaning beyond schema: explains how 'values' should be formatted per type (tickers/CIKs for company, drug names for drug) and what data fields are returned.

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 tool compares 2–5 entities side by side, specifying data fields for 'company' and 'drug' types. Distinguishes itself as a batch replacement for 8–15 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?

Explicitly says when to use (comparing 2–5 entities) and implies efficiency advantage over sequential calls. No explicit exclusions or alternatives, but context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search based on natural language queries and returns ranked results. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be helpful for complete 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 perfectly concise and well-structured in two sentences. The first sentence explains the core functionality, and the second provides crucial 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 annotations and no output schema, the description provides good context about the tool's purpose and usage. However, it doesn't describe the format of returned results (beyond mentioning 'names and descriptions') or potential limitations. Given the complexity is moderate and schema coverage is complete, the description is mostly adequate but could benefit from output format details.

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 thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'describing what you need' which aligns with the query parameter, but provides no additional semantic context about 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 tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes itself from sibling tools (get_holidays, is_today_holiday, next_holidays) by focusing on tool discovery rather than holiday-related operations.

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

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

The description provides explicit usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use this tool (large tool catalog scenarios) and implies it should be prioritized over other tools for discovery purposes.

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

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

No annotations provided, so description must carry burden. It describes data sources but does not disclose behavioral traits like rate limits, authentication, error handling, or idempotency. Agent cannot infer safety or 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?

Description is concise, front-loaded with purpose, and uses a clear list structure for data types. 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 complexity of bundling many data sources, description lacks output format, limits, or error behavior. While it lists data types, no output schema exists, so more detail on return structure 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%, and the schema already describes the type and value parameters with usage notes. The description adds no new parameter meaning beyond what is in the schema.

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

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

Description clearly states the tool returns a full profile of an entity across all relevant Pipeworx packs in one call, listing specific data types for company type. It distinguishes itself by replacing 10-15 sequential calls, though sibling differentiation is implicit.

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 not to use the tool: for federal contracts, call usa_recipient_profile directly because bundling is too slow. This provides clear usage 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, the description carries full burden. It states 'Delete' implying a destructive mutation, but doesn't disclose behavioral traits such as whether deletion is permanent, requires specific permissions, has side effects, or what happens if the key doesn't exist. This leaves significant gaps for a mutation 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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy 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?

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior (e.g., error handling, permanence), usage context, and output expectations, which are critical for safe and effective tool invocation.

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

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

Schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose unambiguous. It doesn't explicitly distinguish from siblings like 'recall' or 'remember', but the verb 'Delete' implies a destructive operation versus retrieval or storage.

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?

No guidance is provided on when to use this tool versus alternatives. While 'Delete' suggests it's for removing memories, there's no mention of prerequisites (e.g., needing to know the key), when not to use it, or how it relates to siblings like 'recall' (which likely retrieves memories).

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, idempotentHint=true, and destructiveHint=false. The description adds that the tool fetches the page and outputs a text blob, providing some context beyond annotations but no further behavioral traits like rate limits or auth needs.

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 followed by bullet points of use cases. It is front-loaded with the main action, no wasted words, and easy to parse.

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

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

The tool is simple with two parameters fully described. The output is explained as a single text blob. No output schema exists, but the description covers what the agent needs to know. Complexity is low, and all aspects are addressed.

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 both parameters have descriptions. The description reinforces the URL and max_links usage but does not add extra parameter-specific meaning beyond the schema, warranting a baseline 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 generates an llms.txt file for any URL, specifying the verb 'generate', the resource 'llms.txt file', and the audience 'AI crawlers'. It includes the process (fetch, extract, emit) and distinguishes from siblings by its unique 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 explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitor. It does not exclude alternatives, but given the sibling list, no similar tools exist, so 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 the use of ISO country codes, which is useful, but does not cover other important traits such as data source reliability, rate limits, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

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

The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the purpose and provide key usage details without any unnecessary information. Every sentence earns its place by contributing essential context.

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, 2 parameters with full schema coverage, and no output schema, the description is adequate but incomplete. It covers the basic purpose and parameter format but lacks details on behavioral aspects like response structure or limitations, which are important for a tool with no annotations.

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. The description adds minimal value by reiterating the ISO code format with examples, but does not provide additional semantics beyond what the schema specifies. 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 ('Get public holidays') with the target resource ('for a country and year'), distinguishing it from sibling tools like 'is_today_holiday' and 'next_holidays' which focus on different temporal queries. It uses precise terminology that immediately communicates the tool's function without redundancy.

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

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

The description provides clear context for when to use this tool by specifying it retrieves holidays for a given country and year, but it does not explicitly mention when not to use it or name alternatives like the sibling tools. The context is sufficient for basic usage but lacks explicit differentiation from similar 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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool's function but does not describe behavioral traits such as whether it requires authentication, has rate limits, returns a boolean or detailed response, or handles edge cases (e.g., invalid country codes). This is a significant gap 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 is front-loaded with the core purpose. It contains zero wasted words, making it highly concise and well-structured for quick understanding.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects and output format, which are needed for full contextual understanding despite the simple schema.

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

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

Schema description coverage is 100%, with the parameter 'country_code' fully documented in the schema (ISO 3166-1 alpha-2 code). The description adds no additional parameter semantics beyond implying the country context, so it meets the baseline of 3 where the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('public holiday'), specifying the temporal scope ('today') and contextual parameter ('given country'). It distinguishes from sibling tools like 'get_holidays' (likely lists holidays) and 'next_holidays' (likely future holidays) by focusing on today's status.

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 ('today', 'given country') but does not explicitly state when to use this tool versus alternatives like 'get_holidays' or 'next_holidays'. It provides basic guidance but lacks explicit exclusions or named alternatives, leaving room for ambiguity in tool selection.

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 the tool retrieves data 'from today forward,' which implies a read-only, non-destructive operation, but it does not cover other aspects such as rate limits, error handling, authentication needs, or the format of returned data. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core functionality and includes the scope ('from today forward') in a compact manner. Every part of the sentence earns its place, making it highly concise and well-structured.

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

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

Given the tool's low complexity (one required parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and scope but lacks details on behavioral traits, usage guidelines compared to siblings, and output expectations. While it meets the minimum for a simple tool, it does not provide a complete picture for optimal agent use, especially without annotations or output schema.

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

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

The input schema has 100% description coverage, with the 'country_code' parameter fully documented as an ISO 3166-1 alpha-2 code. The description does not add any additional semantic details beyond what the schema provides, such as examples of country codes or handling of invalid inputs. Given the high schema coverage, a baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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: 'Get upcoming public holidays for a country (from today forward).' It specifies the verb ('Get'), resource ('public holidays'), and scope ('from today forward'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'get_holidays' or 'is_today_holiday', which might have overlapping or distinct functionalities, preventing a perfect score.

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

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

The description provides minimal usage guidance, stating only that it retrieves holidays 'from today forward.' It does not specify when to use this tool versus alternatives like 'get_holidays' or 'is_today_holiday,' nor does it mention any prerequisites or exclusions. This lack of comparative context limits its effectiveness in guiding the agent's selection.

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 limiting (5 messages per identifier per day) and content requirements. Does not describe response behavior but acceptable for a feedback tool.

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

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

Four sentences that front-load purpose, then usage categories, then content guidelines, then rate limit. No wasted words; 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?

For a simple feedback tool with no output schema and good parameter descriptions, the description covers purpose, usage, content rules, and limits. Missing return value info but that is acceptable.

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 detailed enum explanations for 'type' and descriptions for 'context' and 'message'. Description adds usage context but does not enhance parameter meaning beyond 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?

Clearly states the tool sends feedback to the Pipeworx team and enumerates specific use cases (bug reports, feature requests, missing data, praise). Differentiates from sibling tools that focus on queries, comparisons, and holidays.

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 categories and content guidelines (describe what you tried, avoid end-user prompts). Mentions rate limits. Does not include when-not-to-use but no sibling overlap makes that unnecessary.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable details: data source (CF analytics-engine), no PII, caching window (5min-1h), and aggregation logic, exceeding what annotations provide.

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

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

The description is informative and front-loaded with the core functionality, but it includes multiple sentences that could be slightly tightened without losing meaning.

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 covers data source, privacy, caching, and the output tuple (pack, tool, count). It lacks explicit output structure details but is adequate for the tool's simplicity.

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

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

The single parameter `window` is fully described in the input schema with enum and explanation. The tool description does not add significant new meaning beyond the schema, so 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 it returns top tools, packs, and call volume over configurable windows, and provides three specific use cases that differentiate it from siblings like discover_tools or ask_pipeworx.

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

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

Explicitly lists three scenarios where the tool is useful, giving clear context for when to use it, though it does not explicitly mention when not to use it or direct to 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?

The description fully aligns with annotations (read-only, open-world, non-destructive) and adds operational details: it walks child markets, extracts dates/thresholds, sorts, and reports violations. No contradictions, and it provides sufficient 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 thorough yet efficient, with a clear front-loaded purpose. Every sentence contributes meaningful information without redundancy. A slight trim could improve conciseness, but it is well-structured.

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

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

Given the simple input schema and no output schema, the description fully equips an agent: it explains the domain concept, the detection logic, and the exact return format. No gaps remain for a correct invocation.

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 parameter 'event' is fully described in the schema (100% coverage). The description adds value by clarifying acceptable inputs (slug or URL) and how the tool uses them, which goes beyond the schema's minimal type/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 explicitly states the tool finds arbitrage opportunities via monotonicity violations in Polymarket events. It explains the underlying price ordering rule, which clearly distinguishes it from general market analysis tools like 'polymarket_edges'.

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

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

The description explains when to use the tool (when an event has multiple date/threshold markets) and provides a concrete example. It does not explicitly state when not to use it, but the specificity makes misuse unlikely.

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 provide readOnlyHint, openWorldHint, destructiveHint. The description adds behavioral details: groups by asset, fetches price history once, computes model probability, ranks by |edge|, and returns trade direction. No contradictions; description enriches 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 paragraph covering all essential aspects without waste. It is front-loaded with the main purpose. Slightly dense but still concise; minor structure improvement could help readability.

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 exists, but the description explains return values (top N with edge magnitude and trade direction) and the algorithm (lognormal model, grouping). Completes the picture for agents to understand inputs, processing, and outputs.

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 descriptions for all three parameters. The description reinforces semantics by stating defaults (limit 10, window 1wk, min_edge_pp 0.5) and explains how parameters affect the computation (grouping, ranking). Adds context beyond schema.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price, specifically for crypto-price bets. It differentiates from siblings by focusing on edge detection for opportunity discovery.

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

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

Explicitly states the tool is built for 'what should I bet on today' questions, enabling discovery without manual paging. It implies when not to use (e.g., for specific market analysis) and explains the methodology, guiding agents on when to invoke.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds context about why spreads exist (different pools), the two modes, and what the output contains (leg-by-leg prices and spread), enhancing transparency without contradictions.

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 main purpose, followed by modes and output details. While it is lengthy, every sentence is useful and well-structured; minor trimming could improve conciseness.

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 lacking an output schema, the description fully explains the return values (leg-by-leg prices and spread). It covers parameter usage, modes, and interpretation, making it complete 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?

With 100% schema coverage, the description still adds value by explaining the topic shortcuts list, the override behavior of explicit parameters, and the output meaning (spread calculation and raw probabilities), going beyond the schema.

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

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

The description clearly states the tool finds cross-venue spreads between Kalshi and Polymarket for the same resolving question, distinguishing it from siblings by specifying the two venues and the arb signal.

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 defines two modes (topic shortcuts and explicit tickers) with examples, guiding when to use each. It does not explicitly state when not to use it, but the guidance is clear enough for proper selection.

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 adequately describes the core behavior (retrieval vs listing based on key presence) but lacks details about error handling, session persistence specifics, or performance characteristics like rate limits or memory size constraints.

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 explains the dual functionality, the second provides usage context. It's front-loaded with the core purpose and wastes no 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 single-parameter tool with 100% schema coverage but no annotations or output schema, the description provides good context about behavior and usage. It could be more complete by addressing potential edge cases (e.g., what happens with invalid keys) or describing the return format, but it 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?

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds valuable semantic context by explaining the dual behavior (retrieve vs list) based on whether the key is provided, which goes beyond the schema's technical specification.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from sibling tools like 'remember' (which stores) and 'forget' (which removes) 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 ('omit key to list all keys'), 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 full burden. It discloses parallel fan-out behavior, date format options (ISO and relative), and return structure (structured changes + count + URIs). It does not discuss error handling or rate limits, but the provided details are sufficient for safe invocation.

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 paragraph that starts with purpose, provides operational details, and ends with use-case guidance. Every sentence contributes meaningful information without redundancy.

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

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

Given the absence of an output schema, the description adequately describes the return values (structured changes, total_changes count, pipeworx:// URIs). It also covers the supported entity type and date formats, making the tool's behavior fully understandable.

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 explains the 'since' parameter with examples and recommended default ('30d' or '1m'), clarifies that 'value' accepts ticker or CIK, and notes that 'type' is currently limited to 'company'. 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 defines the tool's purpose: retrieving changes about a company entity since a given time by fanning out to SEC EDGAR, GDELT, and USPTO. It distinguishes itself from sibling tools like 'entity_profile' by focusing on delta monitoring.

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 suggests use cases: 'brief me on what happened with X' or change-monitoring workflows. It does not list alternatives or when not to use, but the context is clear enough for an agent.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool stores data in session memory, distinguishes between authenticated users (persistent memory) and anonymous sessions (24-hour duration), and implies it's a write operation. However, it doesn't cover potential limitations like storage limits, error conditions, or data format constraints.

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 states the core purpose and usage context, the second adds important behavioral details about persistence. Every sentence adds value without redundancy, making it appropriately sized and front-loaded with 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?

Given the tool's moderate complexity (write operation with persistence behavior), no annotations, and no output schema, the description does a good job covering the essential context: purpose, usage, and key behavioral traits. However, it lacks details about return values, error handling, or storage limitations, which would be helpful for a tool with no output schema.

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

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

The input schema has 100% description coverage, with both parameters ('key' and 'value') well-documented in the schema itself. The description adds minimal semantic value beyond the schema, mentioning what can be stored but not providing additional syntax, format, or constraint details. This meets the baseline of 3 when schema coverage is high.

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

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

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (retrieve) and 'forget' (remove). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and well-differentiated.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives. While it implies usage for persistence across calls, it lacks explicit exclusions or comparisons to siblings like 'recall' or 'forget'.

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 explains it returns ticker, CIK, company name, and pipeworx:// URIs, and specifies version 1 supports only company. It does not mention error handling or side effects, but as a read-only lookup, behavior is sufficiently disclosed for safe invocation.

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 purpose, and contains zero wasted words. It 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 tool's simplicity (2 params, no output schema, no nested objects), the description covers all essential aspects: what it does, input format, output contents, and its efficiency benefit. No 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 description enriches the schema by providing concrete input examples (ticker 'AAPL', CIK '0000320193', name 'Apple') and clarifying the version constraint on type. Schema coverage is 100%, and the description adds real-world usage context.

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

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

The description clearly states the verb 'resolve', the resource 'entity', and the result 'canonical IDs'. It provides specific example for company entities, distinguishing it from sibling tools which are unrelated (ask_pipeworx, holidays, memory, etc.).

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 use when needing canonical IDs for an entity and mentions it replaces 2–3 lookup calls, giving context. It does not explicitly exclude scenarios or list alternatives, but given sibling tools, none compete for this task.

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, idempotentHint, etc. The description adds that it internally calls ai_visibility_check per entity, ranks results, and returns scores/confidence/signal density. No contradictions.

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 concise sentences that front-load the core purpose and functionality. Every sentence adds value, no 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 the absence of an output schema, the description adequately explains the return value (ranked list with score, confidence, signal density). It covers parameter usage and internal process. Minor gap: no mention of tie-breaking or ordering details.

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 descriptions for all parameters. The description further clarifies semantics: entities order matters (first is subject), models supported and API key requirements, context purpose. Adds significant 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?

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check, and returns a ranked list. It distinguishes from sibling ai_visibility_check which covers single 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 a use case (competitive AI-marketing audits) and an example. It implies when to use (multi-entity comparison) vs. single-entity via sibling, but doesn't explicitly list alternatives or 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?

With no annotations, the description fully discloses the behavior: reads from SEC EDGAR + XBRL, returns verdicts with citations and percent delta. It implies a read-only operation with no destructive side effects. Could add more on auth requirements or rate limits, but 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?

Two sentences, no wasted words. First sentence states purpose, second sentence elaborates on supported claims and output. Front-loaded and scannable.

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 (one param, no nested objects) and lack of output schema, the description covers purpose, input format, output fields, and source. Could mention error handling or unsupported claim types 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?

Schema description coverage is 100% for the only parameter 'claim'. The description adds value by providing realistic examples and clarifying the natural-language format, going beyond the schema's generic 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 uses a specific verb ('Fact-check') and resource ('natural-language claim against authoritative sources'), clearly distinguishes its scope (company-financial claims for public US companies) and mentions it replaces sequential agent calls, differentiating it from sibling tools like 'compare_entities' or '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?

It explicitly states when to use (fact-checking claims) and supports specific claim types, but does not explicitly mention when not to use it or provide exclusion criteria. However, the scope is clear enough for an agent to decide.

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