Myvariant

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by detailing the return structure (per-model score, confidence, signals, raw response) and the BYO key mechanism for Anthropic, which is beyond 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 a single paragraph of four sentences, front-loading the main action. It is efficient and every sentence contributes value, though a bulleted list could improve scannability for the agent.

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 per model and combined view. All four parameters are covered, and the tool's moderate complexity (1 required param) is sufficiently addressed for an agent to use it correctly.

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

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

Schema description coverage is 100%, so each parameter is documented. The description adds context beyond the schema, such as explaining the default model (Workers AI Llama-3.3-70b) and the BYO key requirement for Anthropic, enhancing understanding.

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

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

The description clearly states the tool's purpose: to probe LLMs for knowledge about a subject and score visibility per model. It specifies the verb 'probe' and resource 'LLMs', and distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on per-model scoring.

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 (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use `_apiKey` and `models` parameters. However, it does not directly contrast with overlapping sibling tools like 'scan_competitor_ai_presence' or 'bet_research', leaving some ambiguity.

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, destructiveHint. The description adds valuable context: it routes to specific tools, fills arguments, and returns structured answers with stable citation URIs. This goes beyond annotations but is 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 somewhat lengthy but front-loaded with the critical preference statement. Every sentence adds value, though it could be slightly more concise. Structure is logical: preference, then details, then examples.

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 output schema and the presence of many sibling tools, the description is highly complete. It explains purpose, usage, behavior, and parameter semantics, covering all necessary context for 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?

Schema has 100% coverage with one parameter 'question' and five aliases. The description lists all aliases and provides three examples. This adds clarity beyond the schema alone, but the schema already documents the parameter adequately.

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

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

The description states a specific verb-resource ('answer questions about current/historical data') and distinguishes itself from web search by emphasizing structured data from 3,745 tools across 884 sources. It clearly differentiates from siblings like 'deep_research' or 'web search'.

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 'PREFER OVER WEB SEARCH' and lists concrete example queries. It provides when-to-use guidance for factual questions and implicitly excludes open-ended or interpretive queries, making decision 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?

Adds behavioral context beyond annotations: hallucination-resistant extraction, explicit refusal reasons, extra LLM cost. 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?

Well-structured with front-loaded purpose. Some redundancy (e.g., 'Same routing as ask_pipeworx') but overall efficient. Slightly long due to refusal reasons list.

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?

Covers return value shape (answer, evidence, confidence, source, fetched_at, refusal_reason) and lists all refusal reasons. No output schema 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?

Schema coverage is 100% with clear descriptions for all 6 parameters (aliases for question). Description adds no additional parameter 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?

Clear statement of purpose: 'Hallucination-resistant answer mode for high-stakes reads.' Distinguishes from sibling ask_pipeworx by mentioning extra cost and use case.

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 (high-stakes, quoted/cited/acted-on answers) and when not (casual lookups), and recommends sibling ask_pipeworx as alternative.

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, openWorldHint, idempotentHint, destructiveHint false. The description adds extensive behavioral details: fan-out logic, low-confidence short-circuit, closed market handling, wide-spread tradeability, resolution-rule risk parsing, and more. 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?

The description is quite long and contains some redundancy (e.g., repeated mentions of closed market behavior). However, it is logically structured with clear sections (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, etc.) and front-loaded with purpose. Could be more concise.

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

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

Given the tool's complexity (multiple classifiers, fan-out examples, edge cases like low confidence, closed markets, wide spreads, cancellation rules), the description covers almost all aspects. It explains response shapes despite no output schema, and addresses blocking routes and safety measures.

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 valuable context: market input types, depth enum meanings ('quick = 2-3 evidence sources, thorough = full fan-out'), and include_raw explanation (summarized for ~20KB vs full payloads). This exceeds baseline.

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 researches Polymarket bets by pulling Pipeworx data, with specific input examples (slug, URL, question text) and a clear output (evidence packet + comparison). It distinguishes from siblings by its focus on Polymarket bets with category-specific data packs.

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 use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and covers many conditional behaviors. However, it does not explicitly state when NOT to use this tool or suggest alternative sibling tools like polymarket_edges or polymarket_arbitrage.

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 valuable behavioral details: correct handling of off-calendar fiscal years, data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), sorted results by primary metric, and citation URIs. 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?

The description is a single paragraph that efficiently packs usage examples, data sources, and behavioral notes. It front-loads with common user queries. While not using bullet points, it is concise and avoids 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 only 2 parameters, no output schema, and thorough annotations, the description fully explains what the tool does, when to use it, parameter semantics, and return data (paired data + citation URIs). It addresses entity types and data fields, making it complete for an agent to 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 the description adds meaning beyond the schema by explaining value formats (tickers/CIKs for company, drug names) and giving examples (['AAPL','MSFT']). It reinforces min/max items 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 performs side-by-side comparisons of 2-5 companies or drugs, with explicit usage examples like 'Compare X and Y'. It distinguishes itself from sequential single-pack lookups and specifies the data sources for each entity type.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool. It also defines constraints (2-5 entities, company or drug types) and hints at avoiding sequential lookups.

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

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

The description adds significant behavioral context beyond annotations: decomposition into sub-questions, parallel routing, citations, gaps array to avoid hallucination, and expected execution time. 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 long but well-structured, front-loaded with the core purpose. Each sentence adds value, though some details could be slightly more concise (e.g., account requirement and signup link). Overall clear and efficient.

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

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

Despite no output schema, the description fully explains the tool's behavior, input semantics, output structure (structured findings with evidence, gaps, citations), constraints (account, plan, time), and how to choose between this and sibling tools. Complete for an agent to use 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 already describes both parameters (100% coverage). The description adds value by explaining the depth enum values (quick=3, standard=5 default, thorough=8 paid) and clarifying that the question can be broad/multi-part, enhancing the agent's understanding.

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

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

The description clearly states the tool's purpose: grounded multi-source research in one call, decomposing questions into sub-questions and routing in parallel across many tools/sources. It distinguishes from sibling 'ask_pipeworx' which is for single lookups.

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?

Explicit usage guidance: use for broad or multi-part questions, use ask_pipeworx for single lookups. Also mentions account requirements and plan limitations for certain depth levels.

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, destructiveHint=false, idempotentHint=true. Description adds that it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that results are 'ready to call directly'. This expands on annotations by specifying output contents and readiness for reuse.

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 front-loaded with the core purpose, followed by usage context and return details. It is concise but covers essential points without redundancy. Could be slightly tighter, but no unnecessary sentences.

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 6 parameters with aliases and no output schema, the description explains the return value and usage well. It does not mention error cases or behavior when no tools match, but for the tool's purpose this is adequate. Overall fairly 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 coverage is 100% with all parameters described. Description adds value by providing concrete example queries ('analyze housing market trends', 'look up FDA drug approvals') and clarifying that query accepts multiple aliases (task, q, description, search). This helps the agent understand parameter usage beyond 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?

Clearly states 'Find tools by describing the data or task.' The verb 'discover' and resource 'tools' are specific, and it distinguishes itself from sibling tools by providing a meta-capability: browsing available tools rather than using a specific data source.

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 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use context and implies not to use it when you already know the tool.

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 indicate read-only, open-world, idempotent, non-destructive. Description adds that it fans out across multiple sources, returns specific fields like CIK, filings, fundamentals, mentions USPTO sunset and soft-fail, and doesn't contradict 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 a dense single paragraph that front-loads common queries and then details return structure. Every sentence adds value, though slightly lengthy; still highly efficient.

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

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

Despite no output schema, the description thoroughly explains what is returned (CIK, filings, fundamentals, patents, news, LEI) and notes limitations (USPTO sunset, name resolution), covering the tool's complexity adequately.

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 both parameters (type, value). Description adds context: type limited to 'company', value can be ticker or CIK but not names, with examples, enhancing what schema provides.

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

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

The description clearly states it provides a 'full cross-source profile of a US public company', lists example queries, and enumerates data sources. This distinguishes it from siblings like resolve_entity and individual lookup tools.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack... lookups when the user asks for a holistic view' and advises using resolve_entity if only a name is available, providing clear when-to-use and 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?

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data but does not provide additional behavioral details beyond that.

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 action, and contains no 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?

Given the tool's simplicity (one parameter, no output schema, rich annotations), the description fully covers usage context, action, and pairing with other tools.

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 a description for the 'key' parameter. The description mentions 'by key' but does not add significant new meaning beyond the schema.

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

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

The description states 'Delete a previously stored memory by key,' clearly specifying the verb (delete) and resource (memory). It distinguishes from sibling tools like remember and recall.

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 says when to use it ('when context is stale, task is done, or you want to clear sensitive data') and pairs it with remember and recall.

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 mark the tool as read-only, idempotent, and non-destructive. The description adds behavioral detail: it fetches the page, extracts title/description/key links, and emits markdown format. This exceeds annotation information without contradiction.

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

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

The description is a single paragraph of ~60 words, front-loading the main action. It efficiently covers purpose, process, and use cases without unnecessary detail. Slightly more structure (e.g., bullet points) could improve readability, but it's concise.

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

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

For a simple tool with two parameters and no output schema, the description covers the core function, process, output format, and target audience. It lacks mention of edge cases (e.g., invalid URLs), but is complete enough for typical 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 coverage is 100%, so the baseline is 3. The description adds minimal extra meaning for parameters: it mentions 'key links' hinting at max_links' purpose, but the schema already explains both parameters clearly.

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 a production-ready llms.txt file for any URL, using specific verbs and resource names. It distinguishes itself from sibling tools like ai_visibility_check by focusing on generating the file itself.

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 lists three concrete use cases (indexing a client's site, drafting for own project, auditing a competitor), giving clear context for when to use. It does not explicitly mention when not to use it or alternatives, but the guidance is strong.

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 readOnly, idempotent, non-destructive. Description adds default active filter and return field details. No contradiction.

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. Efficiently conveys purpose, usage, and return format.

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 read-only list tool with comprehensive annotations and one parameter, the description provides all necessary context including return fields.

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 the single parameter well (100% coverage). Description adds value by listing return fields beyond the schema.

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

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

Clearly states the tool lists the caller's active subscriptions and specifies return fields. Distinct from siblings like subscribe and unsubscribe.

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 guidance: use to review monitoring before adding more or to find an id to cancel. Implies alternatives but doesn't list all 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds specific metadata content (data sources, build versions, variant counts) beyond annotations, enhancing transparency about what the tool returns.

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 sentence that front-loads the key purpose and lists specific metadata categories. Every word adds value, with no 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?

With zero parameters and no output schema, the description fully conveys what the tool returns (data sources, build versions, variant counts). It is complete for a simple metadata retrieval tool, especially given the rich annotations that cover safety and idempotency.

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 0 parameters, so schema coverage is 100%. The description does not need to add parameter meaning because there are none. Baseline for 0 params is 4, and the description is sufficient.

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 it provides 'Dataset statistics and source/release metadata' for MyVariant.info, listing specific items (data sources, build versions, variant counts). This clearly distinguishes it from sibling tools like 'variant' which likely handles individual variant queries.

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

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

The description implies usage when one wants dataset metadata (statistics, sources, builds). It is clear but does not explicitly state when to avoid or compare with alternatives. Still, it provides sufficient context for basic 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?

Annotations are basic (non-readOnly, non-destructive). The description adds critical behavioral traits: rate limit of 5 per identifier per day, free usage, no quota consumption. 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?

The description is compact (3 sentences) and well-structured: purpose first, then usage guidelines, then constraints. No 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 feedback tool with 3 parameters (including nested object) and no output schema, the description provides all necessary context: what to include, constraints, and how to format feedback.

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 clear descriptions. The description adds extra semantic guidance: 'describe the issue in terms of Pipeworx tools/packs' and 'don't paste the end-user's prompt,' which helps the agent provide better feedback.

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: sending feedback about bugs, features, data gaps, or praise. It enumerates specific use cases, making it easy to understand when to use this tool.

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 (for bugs, features, etc.) and when not to (don't paste end-user prompts). It also mentions rate limits and quota impact, and implicitly distinguishes from sibling 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?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds value by stating it is self-aggregating, derived from CF analytics-engine, no PII, and cached (5min-1h). This goes beyond annotations without contradiction.

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

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

The description is front-loaded with the core function, followed by bulleted use cases, then technical details. Every sentence is relevant and there is no redundancy. It is efficient 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?

The tool is simple (1 optional param, no output schema). The description covers the essential context: data source, no PII, caching. However, it does not specify the return format (e.g., structure of 'top tools'), which could help an agent interpret results.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning by explaining that shorter windows surface what's hot now and longer windows show steady-state demand, which is not 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?

The description clearly states the tool returns top tools, packs, and call volume over a window. The verb 'Returns' and resource 'top tools/top packs/total call volume' are specific. It distinguishes from siblings like 'discover_tools' by focusing on trending/aggregate data.

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 lists three explicit use cases (discovering hot data, confirming canonical choice, aligning use case). It provides clear context on when to use but does not explicitly state when not to use or suggest alternatives among 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?

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds rich behavioral details: semantic anchor using Jaccard similarity, partition filter, fill check against live CLOB depth, and conditions that yield null signals. No contradiction with annotations; consistent with safe read operations.

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 long but efficiently packed with necessary technical details. It begins with a clear requirement ('REQUIRES one of...'), front-loading the key constraint. While slightly verbose, every sentence adds value given the tool's 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?

The description thoroughly covers two modes, validation checks, and response structure (opportunities[], partition_check, fill_check). It addresses edge cases like skipped_low_similarity and placeholder filters. No output schema exists, so the description compensates well, though minor details like result limits are omitted.

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 both event and topic parameters. The description adds significant context: explains how each mode works, provides example values (slugs, URLs, seed questions), and clarifies the behavior difference. Goes well beyond schema to aid correct invocation.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It defines two explicit modes (event for single-event, topic for cross-event) with distinct use cases, effectively distinguishing itself from siblings like polymarket_edges and polymarket_fill_risk.

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 specifies when to use each mode: event recommended for a specific market, topic for cross-event scanning. It warns that calling with no args fails and refers to sibling tool polymarket_fill_risk for custom sizing, providing clear guidance on 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 discloses extensive behavioral details beyond annotations, including caching behavior (KV level keyed on all knobs), that Fed bets are excluded from ranking, the diagnostic information returned, and the detailed model families. This adds significant value beyond the readOnly/idempotent 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 long but front-loaded with the main purpose. Each section adds necessary detail for the tool's complexity (model families, parameters, response structure). While dense, it is well-structured and 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?

Despite no output schema, the description thoroughly explains the response structure (by_segment, diagnostics), the three model families with formulas, and caching behavior. It covers all aspects needed for an agent to understand 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?

With 100% parameter coverage in the input schema, the baseline is 3. The description adds context by explaining how parameters like min_liquidity and max_spread_pp act as 'tradeable-edge knobs' and how min_partition_leg_kelly applies to per-leg Kelly, providing additional meaning beyond the schema descriptions.

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

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

The description clearly states it scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, using specific verb+resource combination. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on a single exchange and providing a comprehensive opportunity discovery mechanism.

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 it is built for 'what should I bet on today' and that agents can discover opportunities without paging hundreds of markets, providing clear context for when to use it. However, it does not explicitly exclude use cases or mention alternative tools for different scenarios.

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 the tool as read-only, open-world, idempotent, and non-destructive. The description adds extensive behavioral details: it reads snapshots, returns tracked edges with time-series, trend analysis, expired opportunities with lifespan, and snapshot dates. Limits such as 60-day TTL and decay computation from daily closes are clearly stated.

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 comprehensive and well-structured with a clear RESPONSE section explaining output fields. While it is dense, every sentence adds value. It could be slightly more concise, but remains efficient.

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

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

With no output schema, the description fully compensates by detailing the response structure: tracked, expired, and snapshot_dates arrays with field meanings. Two parameters are fully explained, and limits are noted. Extremely complete for an agent to understand the tool's behavior.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds further context: defaults, valid ranges (days clamp 2-30), and window options (24hr, 1wk, 1mo). This enhances the schema information.

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: providing edge persistence and decay telemetry from daily snapshots. It explicitly answers the question of how long an edge has existed and whether it is shrinking, distinguishing it from the sibling tool 'polymarket_edges' which likely provides current edge data.

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 historical edge context (e.g., fresh vs. stale edges), but does not explicitly state when to use this tool versus alternatives like polymarket_edges. No direct guidance on when-not-to-use or alternative tools is provided.

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, and non-destructive. The description adds context about walking the ladder, returning verdicts, and risks like forced directional risk, but does not contradict 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?

Well-structured with clear mode demarcations, but slightly verbose. Every sentence adds value; no 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?

Fully describes both modes, expected returns (top_of_book, vwap_fill_price, verdicts, capture_ratio, thin_legs, forced_directional_risk), and critical risks without an output schema. Comprehensive 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%, but the description provides significant added meaning: explains mode-specific interpretation of parameters (e.g., size_usd as spend vs settlement notional), default behaviors, and required combinations ('market' vs 'event').

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 performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth', specifying two distinct modes (single-market and basket). This distinguishes it from sibling tools like polymarket_arbitrage and 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?

Explicitly instructs to use this tool before acting on certain signals or trades above ~$500, and warns against thin books and partial basket fills. Includes specific when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds extensive behavioral context: compatibility_warning conditions, temporal_alignment significance, skipped leg counters, and cross-type/subtype mismatch handling. There is 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 relatively long but well-structured with sections for modes, response, and safety fields. Every sentence provides essential information, though some details (like skipped_cross_type explanations) could be slightly condensed without losing clarity.

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 thoroughly explains all response fields: leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, and skipped counters. It covers edge cases (non-equivalent bet shapes, temporal misalignment, unrelated events) and provides complete context 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%, but the description adds meaning by explaining the two modes (topic vs. explicit), listing valid topic values, and detailing how parameters override each other. It also describes the response structure, which is not present in the input schema or output 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It explicitly distinguishes from sibling tools like polymarket_arbitrage and bet_research by focusing on inter-venue price differences.

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 two distinct modes (topic shortcuts vs. explicit event pairing) and explains when each is appropriate. It also warns that pre-mapped topics often yield compatibility_warnings, guiding agents to not assume tradeability. However, it lacks explicit 'when not to use' instructions or direct comparison 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?

Annotations already declare readOnly, openWorld, idempotent, destructive=false; description adds valuable behavioral context: which databases are merged (dbSNP, ClinVar, CADD, gnomAD) and return structure ({ total, hits }). No contradiction.

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 concise sentences, each serving a purpose: purpose, query types, merged data, return format. Front-loaded with main action.

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?

Thoroughly explains the tool's functionality for a complex data source: multiple query formats, merged annotations, response structure, and link to variant tool. No output schema needed given clear return description.

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; description adds meaning by providing concrete examples for the query parameter and noting default for fields. Adds value beyond schema 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?

Clear verb+resource: 'Search aggregated human genetic-variant annotations on MyVariant.info.' Provides specific query examples but does not explicitly differentiate from sibling tool 'variant', though it hints at coordination.

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 examples of query types (rsID, HGVS, fielded) and notes that hit._id can be passed to the variant tool. Implicitly guides usage but lacks explicit when-not-to-use or 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?

Annotations already indicate readonly and idempotent; description adds context about scoping and pairing with other tools, without contradicting 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?

Front-loaded with purpose, each sentence adds unique value. No wasted words, yet comprehensive.

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

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

For a simple retrieval tool, the description covers purpose, usage, scoping, and relationship to siblings. No gaps given the schema and 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 coverage is 100%, so baseline 3. Description adds value by explaining the effect of omitting the key (list all keys), going beyond the schema's property 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 specific verb 'retrieve' and resource 'value previously saved via remember' and distinguishes from sibling tools like 'remember' and 'forget'.

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 clear guidance on when to use (look up stored context) and implies contrast with 'remember' and 'forget'. Could explicitly mention when not to use, but current guidance is sufficient.

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 states that setting mark_read:true flags events as read, modifying server state, which contradicts the annotation readOnlyHint=true indicating no writes.

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 a single focused paragraph, front-loading the main action, then listing key features in a clear, efficient manner with 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?

Provides good coverage of tool behavior, return contents, filtering, and mark_read side effect; lacks explicit mention of unread_only parameter but schema covers it, and no output schema is present.

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?

Adds value beyond schema by explaining mark_read side effect and examples for type filter (sec_8k) and since parameter usage, even though schema already describes parameters.

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

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

The description clearly states the tool pulls fired events from the subscription feed, specifies return contents (source, citation_uri, payload), and distinguishes from siblings by focusing on events rather than subscriptions or other actions.

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 context that polls work and the same feed is available via a REST endpoint for scripts, but does not explicitly differentiate from sibling tools like list_subscriptions or subscribe in terms of when to use this tool over them.

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

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

Annotations already safe (readOnly, openWorld, etc.). Description adds substantive behavioral details: multi-source fan-out (SEC, GDELT/GNews, USPTO), fallback logic, and soft-fail for USPTO sunset.

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?

Dense but well-organized. Every sentence adds value, with examples and comparisons. No 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?

Despite lacking output schema, description explains return structure (changes grouped by source, total_changes, citation URIs). Covers edge cases like rate limitations and API sunset.

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

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

Schema has 100% description coverage. Description adds extra meaning: explains 'since' formats with examples, clarifies 'value' as ticker or CIK, and notes 'type' only supports 'company'.

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

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

Description clearly states it returns a change feed for a company in a time window. It distinguishes from sibling entity_profile by contrasting static vs. dynamic data.

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 tells when to use this tool vs. entity_profile. Provides example queries and typical 'since' values like '30d'.

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 idempotentHint=true, but description adds scoping by identifier and persistence details (authenticated vs anonymous). Does not contradict annotations. Mild gap: does not discuss idempotency 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?

Three sentences, front-loaded with core purpose, no superfluous words. Efficiently covers usage, examples, and pairing instructions.

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 two-param tool with no output schema, description fully covers what the tool does, when to use, storage behavior, and relationship to siblings. No missing elements.

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 already covers all parameters with descriptions. Description adds example patterns like 'subject_property' but no new semantics beyond schema. Baseline 3 applies due to high schema coverage.

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

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

The description clearly states the tool saves data for reuse across conversations or sessions, with specific verb 'save' and resource 'data'. It distinguishes from sibling tools like recall and forget, and provides examples of what to store.

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 'Use when you discover something worth carrying forward' and gives concrete examples. Also instructs to pair with recall and forget, providing clear context for when to use vs 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?

Annotation hints (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) indicate safe, idempotent reads. The description adds behavioral context: internally cascades through multiple lookup endpoints, replacing 2-3 manual lookups. No contradictions are present, and the extra detail aids the agent in understanding efficiency and internal complexity.

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 with front-loaded examples and a usage directive. Every sentence adds value, but it could be slightly more concise by merging the return detail sentences. Still, it balances completeness and 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?

Given the lack of an output schema, the description thoroughly explains return formats for both entity types, including citation URIs. It covers input acceptance, internal cascading, and the tool's role as a resolver. No gaps remain for an agent to understand when and how to use this tool correctly.

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

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

Schema coverage is 100%, and the description enriches parameter meaning: it explains how the 'value' parameter accepts different input forms per type (ticker, CIK, name for company; brand or generic name for drug) and details return fields (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand, citation for drug). This goes well beyond the schema's minimal 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 resolves names to canonical identifiers, provides concrete examples (ticker, CIK, RxCUI), and explicitly instructs 'Use FIRST whenever you have a name but need an ID.' It effectively distinguishes from sibling tools like entity_profile or compare_entities which operate on already-resolved IDs.

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 says 'Use FIRST whenever you have a name but need an ID,' giving a clear usage context. It lists supported entity types and what they return. While it doesn't exclude specific scenarios or name alternatives, the guidance is clear and the tool's role is well-defined among 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. This complements annotations but doesn't reveal additional behavioral traits beyond what's implied. 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?

Three sentences, front-loaded with the core purpose. No extraneous words. Every sentence adds value: purpose, method, and use case. Ideal conciseness for a tool description.

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 4 parameters, no output schema, and rich annotations, the description adequately explains the tool's behavior: it uses ai_visibility_check, returns ranked list with score/confidence/density. It covers the main output but could mention pagination or error handling. Still, for a competitive analysis tool, it is sufficiently 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?

With 100% schema coverage, baseline is 3. The description adds meaning: entities' first entry is the 'subject', models default to workers-ai, and _apiKey is conditional. This extra context helps the agent understand parameter roles beyond the schema descriptions.

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

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

The description clearly states 'Compare AI visibility across multiple entities side-by-side', specifying the verb (compare) and resource (AI visibility). It distinguishes from sibling tools like ai_visibility_check (single entity probe) and compare_entities (generic comparison), making the tool's unique role evident.

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 concrete use case: 'competitive AI-marketing audits' with an example query. It implies when to use but does not explicitly exclude other scenarios or contrast with alternatives like compare_entities. The context is clear but could be more precise.

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

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

Discloses partial failure behavior, timing for first bundlephobia measurement (5-30s), and graceful degradation with sources_failed field. Return structure is fully described. Annotations confirm read-only, idempotent, non-destructive; description adds context 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?

Dense paragraph front-loads purpose and use cases. Could be slightly more structured with bullet points, but no unnecessary words. Every sentence contributes 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?

No output schema, but description fully explains return values (summary block, advisories, links, versions). Covers edge cases (scoped packages, partial failures, default version). Complete for a complex composite 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%, and description adds meaning: scoped packages accepted, version defaults to latest. Provides clarity beyond schema descriptions.

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

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

The description clearly states the tool performs a composite check for evaluating npm packages across multiple data sources (deps.dev and bundlephobia), addressing questions about safety, popularity, and size. It distinguishes itself from siblings by being a one-call composite check.

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 (e.g., 'is X safe / popular / small') and when not to (NPM only; other ecosystems fall under different tools). Provides clear context for agent decision-making.

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 readOnlyHint, idempotentHint, etc. The description adds implementation details (BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap with truncation flag) and notes that passages include offsets for verification, exceeding 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 concise, using four sentences to convey purpose, usage, behavior, and output. It is front-loaded with the main action. Minor room for improvement in structuring, but overall efficient.

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

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

Given no output schema, the description adequately explains return values (top-N passages with offsets and scores). It covers usage, behavioral limitations, and parameter constraints, making the tool fully usable without external documentation.

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 all three parameters. The description does not add new information beyond what is already in the schema. It mentions return format (character offsets, similarity scores) but that pertains to output, not 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 performs semantic search inside a fetched record, with concrete examples (SEC 10-K, article). It distinguishes its scope from the sibling 'ask_pipeworx_grounded' by explaining the pairing pattern.

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

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

The description explicitly advises use when the record is too large for the prompt, and outlines a workflow paired with 'ask_pipeworx_grounded'. While it doesn't list explicit when-not-to-use scenarios, the context 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.

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

Annotations indicate a write operation (readOnlyHint=false) that is idempotent and non-destructive. The description adds important behavioral details: returns subscription id, requires OAuth, delivery channel limitations (SMS cap of 10/day, webhook auto-disable after 10 failures), and the one-time return of webhook secret.

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 fairly long but well-structured with bullet points and examples. Every sentence adds value, though it could be slightly more concise. The front-loading of the purpose is effective.

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

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

Given the complexity of multiple subscription types and delivery options, the description is remarkably complete. It covers return value, prerequisites, limitations, and behavioral details, compensating for the lack of 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?

Schema coverage is 100%, but the description adds significant value beyond the schema by providing concrete examples for each subscription type and detailed explanations for the delivery object, including webhook signing verification and constraints.

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

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

The description uses a specific verb 'Create' and clearly states the tool creates a proactive monitoring subscription to a live-data event stream, returning the subscription id. It also distinguishes from sibling tools like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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 (creating subscriptions) and prerequisites (requires Pipeworx OAuth account, not for anonymous/BYO). It lists supported types and their parameters, but does not explicitly compare to other tools, though implicit distinction exists.

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=false. The description adds behavioral context by explaining that the tool returns example questions from a live catalog, and that omitting the topic gives a full spread while passing a topic focuses the results. There is no contradiction with annotations, and the description provides useful supplementary detail about the tool's 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 fairly concise given the amount of detail it provides. It front-loads multiple synonymous phrases and then explains the return format. While it is longer than ideal (almost 100 words), every sentence contributes to understanding the tool's purpose and usage. A slight trim could improve conciseness, but it remains 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 has only one optional parameter, no output schema, and no nested objects, the description is complete. It explains what the tool returns (category-bucketed example questions with tool+argument shapes), how to call it (with or without topic), and its role as an onboarding tool. No additional information 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?

Schema coverage is 100% with one parameter (topic) described. The description adds semantic value by listing example values ('finance', 'pharma', etc.) and explaining that omitting the topic yields a cross-category spread. This goes 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 clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions along with the exact tool and argument shapes. It uses multiple synonymous questions ('What can I ask Pipeworx?', 'give me ideas', etc.) to immediately convey the function. This distinguishes it from siblings like ask_pipeworx or discover_tools.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools', providing clear guidance on when to use it. It also explains the optional topic parameter for focusing. However, it could be more explicit about when not to use this tool or identify specific alternatives, though the context of onboarding makes it sufficient.

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?

Beyond annotations (readOnlyHint false, destructiveHint false, idempotentHint true, openWorldHint true), the description adds critical behavior: ownership enforcement, deactivation vs deletion, and linkage to recent_alerts. 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?

Two sentences with no wasted words. Purpose is front-loaded, and every sentence earns its place by adding distinct 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 simple tool with one parameter and no output schema, the description fully covers purpose, ownership, effect on data, and relation to another tool (recent_alerts). Complete for the tool's 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 has 100% description coverage for the single parameter 'id', but the description adds context that the id is a subscription uuid returned by subscribe, which adds meaning 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 'Cancel a subscription by id', which uses a specific verb and resource, and distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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 ownership enforcement ('you can only cancel your own subscriptions') and explains that the row is deactivated, not deleted, with historical events still available via recent_alerts. Provides clear guidance on when to use and not use.

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

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

The description goes beyond annotations (which indicate read-only, idempotent, non-destructive) by detailing what the tool returns (verdict, structured form, actual value with citation, percent delta) and its efficiency benefit (replaces 4-6 sequential calls). 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 well-structured with a front-loaded purpose, example triggers, usage guidance, domain scope, return details, and efficiency note. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (one parameter, no output schema), the description thoroughly covers the tool's behavior, return values, and domain limitations. It is complete enough for an AI agent to understand 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?

The input schema has 100% description coverage for the single 'claim' parameter, providing format and examples. The tool description adds context about the expected nature of claims (e.g., company-financial) but does not significantly enhance parameter-level meaning beyond what the schema already 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 explicitly states the tool performs natural-language claim verification against authoritative sources, supported by example queries like 'Is it true that...' and specifies the domain (company-financial claims). It clearly distinguishes its purpose from sibling tools by noting it replaces multiple sequential calls.

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

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

The description provides clear guidance on when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also scopes the domain to company-financial claims. However, it does not explicitly state when not to use it or suggest 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds behavioral context by listing the aggregated data sources (dbSNP, ClinVar, CADD, dbNSFP, gnomAD) and confirming it returns annotations. This goes beyond what annotations provide, though no additional details on rate limits or output structure are given.

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 and a parameter example, all concise and front-loaded. The first sentence states the core purpose, the second provides usage guidance. No filler or redundant information; 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 tool's complexity (multiple data sources) and lack of an output schema, the description adequately communicates the kind of data returned (pathogenicity, population frequency) and the input format. It does not detail the output structure, but the coverage of parameter schema (100%) and the clear purpose compensate. The agent can infer enough to decide whether to invoke this 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%, with both 'id' and 'fields' described. The description reinforces the meaning of 'id' by providing an HGVS example and explains 'fields' as comma-separated return fields with an example ('clinvar,gnomad_genome.af,cadd.phred'). This adds practical usage context beyond the bare schema, warranting a score above baseline 3.

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 the full merged annotation for a single human genetic variant by its HGVS id'. It provides a concrete example (e.g., 'chr7:g.140453136A>T') and lists the specific data sources (dbSNP, ClinVar, CADD, dbNSFP, gnomAD), making the action and resource very clear. This distinguishes it from sibling tools, which cover different domains like AI search, poly market arbitrage, 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 explicitly tells when to use the tool: 'Use to look up a known variant and read its pathogenicity and population frequency.' It implies the tool is for single variant lookup by HGVS id, but does not explicitly exclude other use cases or mention alternatives. However, given the sibling tools list, no obvious alternative exists for this specific task, so the guidance is sufficient.

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