Opensea

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already mark it as safe. Description adds default model, BYO key cost details, and return structure. 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?

Four concise sentences, front-loaded with core function, efficient use of words, 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?

Given 4 params (all covered) and no output schema, description explains return structure well. Could mention error handling, but overall complete for the complexity.

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

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

Schema coverage is 100%. Description adds meaning: explains default model, BYO key, purpose of 'context' for disambiguation. Adds value beyond schema.

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

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

Describes probing LLMs and scoring visibility. The verb 'probe' and resource are clear, but does not differentiate from sibling tool 'scan_competitor_ai_presence', which seems similar.

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?

Mentions use cases (AI-marketing audits, brand checks, monitoring) but lacks explicit when-not-to-use or alternatives. Provides some context but not full 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 read-only, idempotent, non-destructive behavior. The description adds significant behavioral context: it routes to 3,436 tools, fills arguments, and returns structured answers with citation URIs. This goes beyond annotations and sets clear expectations.

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 key preference statement and is well-structured with use cases, mechanism, and examples. It is slightly verbose with the long list of sources, but still efficient for the 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?

Given the tool's complexity (routing to many sources) and lack of output schema, the description provides sufficient context: what kinds of questions, how it works, what the output contains (structured answer with citations). It could mention output format more explicitly, but overall complete.

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

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

Schema description coverage is 100%, so the description does not need to add much. However, it reinforces that the parameter is a natural language question and gives examples, but adds little new semantic information beyond the schema.

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

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

The description clearly states the tool's purpose: routing factual questions to authoritative structured data sources. It specifies the types of data (SEC filings, FDA, etc.) and explicitly prefers this tool over web search, distinguishing it from siblings like '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 provides explicit guidance on when to use the tool ('PREFER OVER WEB SEARCH', fact-based queries) with concrete examples. It lacks explicit when-not-to-use guidance, but the positive use cases are well covered.

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 behavioral traits beyond annotations: it costs one extra LLM call, returns structured output with refusal reasons, and only answers from tool data. Annotations already indicate readOnly, idempotent, non-destructive, which are consistent.

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

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

The description is well-structured and concise: first sentence states purpose, then explains mechanism, return format, refusal reasons, and usage guidance. Every sentence adds value without repetition.

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 (refusal reasons, evidence extraction, cost trade-off) and the absence of an output schema, the description fully covers what the tool does, its return types, and its behavioral guarantees.

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 the single required parameter 'question' and its aliases. The description adds no further parameter meaning beyond the schema, meeting the baseline for high 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's purpose: a hallucination-resistant answer mode for high-stakes reads, which extracts answers only from tool results. It distinguishes itself from the sibling tool 'ask_pipeworx' by emphasizing groundedness and explicit refusal types.

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

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

The description explicitly advises using this tool when answers will be quoted, cited, or acted on (financial, legal, medical, etc.) and recommends preferring 'ask_pipeworx' for casual lookups. This provides clear 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?

The description goes far beyond annotations by detailing the market resolution process, classifier system, parallel fan-out, response shapes, resolver contract, parent event extraction, news fallback handling, safety short-circuits, and market closure detection. This comprehensive disclosure provides rich behavioral context that annotations alone do not cover.

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 purpose and use cases, then organized into labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). While lengthy, each section adds valuable detail and no sentence is wasted. The structure aids readability despite the length.

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 and absence of an output schema, the description fully compensates by thoroughly explaining response fields (market, analysis, evidence), resolver contract with confidence levels and alternatives, parent event extraction, news error handling, and status codes for common edge cases. No critical behavioral aspect is left undocumented.

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 already documents all three parameters with complete descriptions (depth enum, market string, include_raw boolean). The description adds no new parameter-level meaning; it only provides high-level examples of market_input in the schema examples. Since schema coverage is 100%, baseline score of 3 is appropriate.

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

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

The description opens with a clear verb-resource pair ('Research a Polymarket bet by pulling relevant Pipeworx data'), specifies input formats (slug, URL, question text), and provides use-case examples. However, it does not explicitly differentiate this tool from sibling tools like polymarket_arbitrage or polymarket_edges, leaving potential ambiguity.

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

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

The description lists concrete use cases ('should I bet on X', 'what does the data say', 'is there edge') and includes blocking conditions (low-confidence, closed markets, illiquid spreads). It does not, however, explicitly state when NOT to use this tool or suggest alternatives, so it lacks exclusion 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 declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds behavioral context beyond annotations: it notes correct handling of off-calendar fiscal years, sorting by primary metric, and returns citation URIs. No contradictions with annotations.

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

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

The description is well-structured, starting with trigger phrases, then data sources per type, then sorting and output. It is informative without being overly verbose. Some sentences could be tightened, but overall it is efficient and front-loaded.

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

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

Despite no output schema, the description explains what is returned: paired data with citation URIs, sorted by primary metric. It covers both entity types and their respective data. For a tool with two well-defined parameters, this is sufficiently complete for an AI 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 coverage is 100% with descriptions, but the description adds significant meaning: it explains that 'company' type retrieves latest 10-K revenue, net income, cash, debt, and 'drug' type retrieves FAERS adverse events, FDA approvals, active trials. It also clarifies that values are tickers/CIKs for companies and names for drugs, with limits implied.

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 does side-by-side comparison of 2-5 companies or drugs in one parallel call. It lists specific trigger phrases like 'Compare X and Y', 'X vs Y', and explains what data each type retrieves (SEC EDGAR for companies, FAERS/FDA/trials for drugs). It distinguishes itself from sequential single-entity 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear guidance on when to use. It gives natural language query examples. However, it does not explicitly state when NOT to use or mention alternatives beyond single 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?

Description adds context about returning top-N tools with full schemas and curated examples, which complements the annotations (readOnlyHint, idempotentHint). Does not contradict annotations. Additional details on response structure could be beneficial but not required.

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 purpose and includes a long list of examples, which is slightly verbose but acceptable. Each sentence adds value; could be more compact 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?

Given no output schema, the description adequately explains what the tool returns (names, descriptions, schemas). It does not cover error handling or pagination, but for a discovery tool this is sufficient.

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

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

Schema coverage is 100%, so the description does not need to add much. It mentions 'top-N' aligning with the limit parameter, but no additional semantic guidance beyond what the 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 'Find tools by describing the data or task' with specific verbs and resources. It distinguishes from sibling tools by emphasizing that it discovers options and returns full schemas ready to call.

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 recommends calling this first when many tools are available ('Call this FIRST...'). Lists example use cases but does not explicitly state when not to use it 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 declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds detailed behavioral traits: soft-fail for patents (USPTO API sunset), specific return fields with URI formats, sorted fundamentals, and cross-source fan-out. No contradictions with annotations.

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

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

Description is long but well-structured with examples upfront and detailed return fields. Each sentence adds value; however, it could be slightly tighter by omitting some repetition (e.g., 'soft-fails until reactivated'). Still efficient for 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?

Despite no output schema, the description fully explains return values (cik, company_name, recent_filings with URIs, fundamentals sorted, patents fallback, news, LEI). Covers all key aspects given the tool's multi-source complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema by clarifying that value can be ticker or CIK (not name) and that type is limited to 'company' for now. This helps correct parameter usage.

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 provides a 'full cross-source profile of a US public company' with concrete examples ('Tell me about X', 'research Acme') and lists all data sources (SEC EDGAR, XBRL, etc.). It is specific and well-distinguished from sibling tools like resolve_entity.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views and warns that names are not supported, directing users to resolve_entity first. This provides clear 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 provide destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data and that deletion is the action. Does not describe error behavior for missing key, but idempotency is implicit.

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 efficient sentences, front-loaded with purpose. No redundancy. Every sentence adds value.

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

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

For a one-parameter destructive tool with no output schema, the description covers purpose, usage guidance, and sibling tools. Complete for the tool's simplicity.

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

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

Schema coverage is 100% with key description 'Memory key to delete'. Description repeats 'delete by key' without adding new semantics beyond the schema. Baseline 3 applies.

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 'Delete a previously stored memory by key' with specific verb and resource. Implicitly distinguishes from siblings like remember (store) and recall (retrieve) through pairing mention.

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 when-to-use conditions: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with remember and recall, guiding 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 read-only, idempotent, non-destructive behavior. Description adds value by detailing the process (fetch, extract, emit markdown) and output format, which annotations do not cover. 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 with no wasted words. Front-loaded with the primary action ('Generate a production-ready llms.txt file...'), followed by process details and use cases. Efficient and clear.

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?

Tool has 2 parameters with 100% schema coverage, no output schema, and simple function. Description covers purpose, process, output format, and usage scenarios comprehensively. No missing details.

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

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

Schema description coverage is 100%, so baseline is 3. Description does not add extra meaning beyond the schema for parameters; it only indirectly refers to 'url' via 'fetches the page'. No additional context for 'max_links'.

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

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

Description clearly states the tool generates an llms.txt file for a given URL, specifying the action, input, and output format. It distinguishes itself from sibling tools by focusing on AI crawler indexing, which is unique among the listed siblings.

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

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

Description explicitly lists three use cases (client indexing, personal projects, competitor auditing), providing clear when-to-use guidance. It does not mention when not to use or alternatives, but the use cases are specific enough.

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

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds value by listing the specific fields returned (name, description, contracts, social links, fees, image, banner), which is behavioral context beyond the 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 concise with two sentences, no redundant information. The first sentence states the action and output, the second provides parameter sourcing guidance.

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

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

Given the simple input (one non-enum parameter), presence of an output schema, and annotations covering safety, the description is complete. It explains what the tool does, what it returns, and how to obtain the required slug.

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

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

The single parameter 'collection_slug' is described in the schema with its meaning. The description additionally tells the user where to find the slug, adding practical value 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 uses 'Fetch' as a specific verb and identifies the resource as 'collection by OpenSea slug'. It lists the returned fields, clearly distinguishing the purpose from sibling tools like get_collection_stats or list_collection_nfts.

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 guidance on finding the required slug ('Find slugs at opensea.io/collection/<slug>'), but does not explicitly state when to use this tool versus its siblings or when not to use it.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by listing the specific data fields returned, providing behavioral context beyond the 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?

Single sentence, no waste, front-loads key information. Every word is necessary and informative.

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 an output schema present, the description need not explain return values. It lists all expected metrics, and the single parameter is well-documented. Complete and sufficient for this simple tool.

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

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

Schema description coverage is 100% (collection_slug described as 'OpenSea collection slug'). The description does not add additional parameter semantics beyond the schema, so baseline 3 is appropriate.

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

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

Description clearly states the tool returns floor price, volume, supply, owners, and best-offer for a collection. It specifies the resource (collection) and the exact metrics, distinguishing it from siblings like get_collection or get_nft.

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

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

No explicit guidance on when to use this tool versus alternatives. Usage is implied for obtaining collection statistics, but no context on when not to use it or which sibling tools to prefer for other needs.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral context beyond the input fields. With annotations covering safety, the description does not need to repeat, but it offers no extra transparency.

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

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

The description is extremely concise (6 words) and front-loaded. It is efficient but could be slightly more informative without losing conciseness, e.g., mentioning the return format briefly.

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 output schema exists (context signal), the description need not explain return values. It covers the core functionality well, though it omits edge cases or usage hints. Overall adequate for a simple lookup tool.

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

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

Schema description coverage is 100% (all parameters have descriptions). The description does not add additional meaning beyond what the schema already provides, meeting the baseline for no extra value.

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 'Single NFT by chain + contract address + token ID' clearly states the tool retrieves a single NFT, specifying the three identifiers. It distinguishes itself from sibling tools like 'get_collection' or 'list_collection_nfts' which operate at collection level.

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

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

No explicit guidance on when to use this tool vs alternatives (e.g., list_owned_nfts or get_collection). The description implies it's for a single NFT lookup, but lacks when-not scenarios or prerequisites.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context by stating it is paginated and detailing the return fields, which is useful beyond the 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 single, front-loaded sentence that efficiently conveys the tool's purpose and key behavior, 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?

Given the presence of an output schema (implied by 'Returns...') and straightforward parameters, the description sufficiently covers the tool's functionality. It lacks explicit details about edge cases or error handling, but is adequate for a list operation.

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 each parameter described in the input schema. The description does not add significant new meaning beyond the schema, but it reinforces the pagination behavior. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'List', the resource 'NFTs inside a collection', and indicates pagination. It also lists return fields (identifier, name, image, traits, owner, last sale). This distinguishes it from sibling tools like list_owned_nfts and get_nft.

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 its usage by stating its purpose and pagination, but it does not explicitly provide guidance on when to use this tool versus alternatives such as list_owned_nfts or get_nft. The context from sibling tool names partially fills this gap.

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, so safety is clear. The description adds that the tool is paginated, which is key behavioral context not in annotations. 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, front-loaded sentence that conveys the core purpose and pagination. Every word earns its place with 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?

Given the output schema exists and annotations cover safety and idempotency, the description is complete for a straightforward list tool. It mentions pagination and scope (chain, address), which are the key contextual 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 coverage is 100%, so the schema already describes all parameters. The description adds no extra meaning beyond what's in the schema, so baseline of 3 is appropriate.

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

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

The description clearly states the verb 'list' and resource 'NFTs owned by an address on a specific chain', with pagination noted. It distinguishes from sibling tools like get_nft and list_collection_nfts by focusing on 'owned by an address' rather than collection or single NFT.

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 querying NFTs owned by an address, but does not explicitly state when to use this tool versus alternatives like list_collection_nfts or get_collection. No when-not or exclusion criteria are 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=true and destructiveHint=false, so the safety profile is clear. The description adds value by detailing the return format, scope ('caller's'), and mentioning active vs inactive subscriptions, 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 two sentences with no unnecessary words. It front-loads the purpose and output, then adds usage advice. Every sentence serves a purpose.

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

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

Given the tool's simplicity (one optional param, no output schema) and rich annotations, the description is complete. It covers what the tool does, what it returns, and when to use it, leaving no major gaps.

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 parameter's description in the schema is sufficient. The tool description does not add extra meaning for the parameter, so a baseline of 3 is appropriate.

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

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

The description clearly states the tool lists the caller's active subscriptions and specifies the exact fields returned. It distinguishes itself from siblings like 'subscribe' and 'unsubscribe' by focusing on listing.

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

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

The description explicitly advises using the tool to review subscriptions before adding more or to find an ID to cancel, providing clear context. It could be more specific about when not to use it, but the guidance is helpful.

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

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

Discloses rate limit (5 per identifier per day), that it's free and doesn't count against quota, and how feedback is used (roadmap influence). Annotations are all false, so 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?

Front-loaded with purpose, each sentence adds value, but slightly verbose with multiple guidelines in one paragraph. Still 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?

Given 3 parameters, 2 required, no output schema, the description fully covers intent, usage, constraints, and expectations. No gaps identified.

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 descriptions. Description adds context for the message parameter (be specific, which tool, what error) and reinforces enum meanings, exceeding 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?

Description clearly states the tool is for providing feedback (broken, missing, praise) and distinguishes from siblings by focusing on feedback specific to Pipeworx 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 states when to use (bug, feature, data_gap, praise) and provides guidelines (describe in terms of tools/packs, don't paste user prompt). Covers format and constraints.

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, non-destructive. The description adds valuable context: data source (CF analytics-engine), privacy (no PII), caching (5min-1h), and data format (pack, tool, count). 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?

Every sentence is purposeful. The main action is front-loaded, followed by use cases, then technical details. No redundant or vague language. 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?

Despite no output schema, the description sufficiently describes the return content (tools, packs, call volume) and data structure (pack, tool, count). Caching behavior and privacy are covered. Complete for a simple one-parameter tool.

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

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

Schema coverage is 100% with a single parameter fully described. The description adds meaningful insight: 'Shorter windows surface what's hot right now; longer windows show steady-state demand,' which aids selection beyond the schema.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a specified window, with a specific verb 'Returns' and resource. It distinguishes itself from sibling tools by focusing on trending/aggregated signals.

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?

Three explicit use cases are provided (discovering hot data sources, confirming canonical tools, aligning use case). No explicit alternatives or when-not-to-use, but the context is clear and helpful.

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 the tool is read-only, idempotent, and non-destructive. The description adds significant behavioral context: it explains the checks (monotonicity, partition-sum), the semantic anchor (Jaccard similarity), partition filters, and output structure. It does not contradict annotations and provides transparency beyond what annotations offer.

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 detailed but well-organized with sections (SEMANTIC ANCHOR, PARTITION FILTER) and starts with a crucial usage note. While it is somewhat long, every sentence provides necessary information, and the structure aids readability. It balances detail with 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 lacking an output schema, the description thoroughly explains the response structure (opportunities array with fields, partition_check object in event mode). Parameters and usage are fully covered. The description is complete for the tool's complexity, leaving no major gaps for an AI agent to invoke 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%, meaning both parameters are already described in the schema. The tool description adds value by elaborating on the intended use of each parameter: event is for single-event mode with example slugs, topic is for cross-event scanning with seed questions. It also explains the behavior differences between modes, which enriches the parameter 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 the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic) with specific use cases, and the name 'polymarket_arbitrage' aligns with the described functionality. Sibling tools like polymarket_edges and polymarket_kalshi_spread have different purposes, but the description does not explicitly differentiate them; however, the purpose is sufficiently clear on its own.

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 the requirement to provide one of `event` or `topic` and explains when to use each: event for a specific market (recommended) and topic for cross-event scanning. It provides example values and outlines the behavior difference. While it does not explicitly list alternatives or when not to use the tool, the guidance is practical and covers the main usage 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?

The description adds substantial behavioral context beyond the annotations, including caching ('Cached 1h at the KV level'), response structure (by_segment, diagnostics), edge calculation details, and Fed bet handling. 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 excessively long and densely packed, making it hard to parse quickly. While front-loaded with purpose, subsequent details (model families, response format, edge formulas) could be structured more concisely or broken into sections.

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 complex tool (9 parameters, no output schema, multiple segments), the description thoroughly explains the output structure, diagnostic info, caching, and edge calculation. It covers all necessary aspects for an agent to understand usage and interpretation.

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 summarizes some parameters as 'knobs' and explains their collective purpose, but adds little new meaning beyond the schema descriptions. The parameter descriptions in the schema are already detailed.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the intended use case ('what should I bet on today') and distinguishes from siblings by focusing on data disagreement, not general trending or arbitrage.

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

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

The description provides context for when to use the tool ('Built for agents discover opportunities without paging hundreds of markets') and explains the segmentation (model_driven, structural_arbitrage, concentrated_longshot) along with tradeable-edge knobs. However, it lacks explicit exclusions or comparisons to sibling tools like 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, destructiveHint. The description adds substantial behavioral context: compatibility_warning conditions, temporal_alignment, skipped cross types/subtypes, and leg-by-leg price output. This goes well beyond the 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 fairly long but well-structured with clear sections (modes, response, safety fields). It is front-loaded with the main purpose. However, it could be slightly more concise by reducing some redundancy, such as repeating 'cross-venue spreads' multiple times.

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

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

No output schema exists, so the description must explain return values. It does so thoroughly: leg-by-leg raw probabilities, top_spreads_pp, safety fields (compatibility_warning, temporal_alignment, skipped_* counters). It also covers edge cases (non-equivalent bet shapes, unrelated events). Complete for a complex tool with no output schema.

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

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

Schema coverage is 100% with clear descriptions for each parameter. The description adds additional semantics: lists all available topics, explains how explicit parameters override topic mapping, and clarifies that topic mode auto-fetches matching events. This significantly enriches 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, with two modes (topic and explicit). It effectively distinguishes itself from siblings like polymarket_arbitrage which focuses on intra-venue arbitrage.

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

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

Provides clear guidance on when to use topic mode vs explicit mode, and warns that pre-mapped topics often return compatibility warnings. However, it does not explicitly contrast with sibling tools like polymarket_arbitrage or validate_claim, leaving some ambiguity about when to choose this tool over others.

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

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

Annotations already provide key behavioral traits: readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds scoping (by identifier) and the dual behavior based on key presence. It does not disclose behavior when a key is missing or error handling, but given strong annotations, the added context is sufficient but not rich.

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

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

The description is three sentences, each serving a distinct purpose: explaining operation, giving use cases, and specifying scope & pairing. No unnecessary words; information is front-loaded and easy to parse.

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

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

No output schema exists, so the description should compensate. It provides example use cases but does not explicitly state the return format (e.g., value when key provided, array when omitted) or error behavior. Adequate but leaves gaps for an agent needing precise return expectations.

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

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

Schema coverage is 100% (one parameter fully described). The description adds meaning by explaining the parameter's role in retrieving a specific value vs. listing all keys, and provides context about scoping. It enhances understanding beyond the schema.

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

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

The description uses specific verbs ('Retrieve', 'list') and clearly identifies the resource (previously saved memories via 'remember'). It distinguishes this tool from siblings by explicitly naming 'remember' and 'forget', leaving no ambiguity about its role.

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

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

The description explicitly states when to use the tool (to look up context stored earlier) and mentions the alternative (omitting key to list all). It references sibling tools 'remember' and 'forget' for pairing. However, it does not explicitly state when NOT to use it or provide an exhaustive list of 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 safe, idempotent, read-only behavior. The description adds valuable behavioral details: the mark_read effect (flags events as read so subsequent calls show only newer ones), and mentions that polling works. No contradictions with annotations.

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

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

The description is four sentences with no waste. First sentence states purpose, second describes return fields, third covers parameters, fourth explains mark_read and alternative access. 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 has 5 optional parameters and no output schema, the description provides sufficient context about return fields, filtering, state flags, and even an alternative REST endpoint. It feels complete for end-user understanding.

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

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

Schema coverage is 100% with adequate parameter descriptions, so the baseline is 3. The description adds extra context like example filter values ('sec_8k'), ISO timestamps, and explains the combined behavior of mark_read with unread_only. This pushes it above 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 the tool pulls fired events from the subscription feed and returns the most recent alerts with specific fields (source, citation_uri, raw payload). The verb 'Pull' and resource are explicit, distinguishing it from sibling tools like '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?

The description explains filtering options (type, since) and the mark_read behavior, and even provides an alternative access method (URL for scripts/dashboards). It could be improved by explicitly stating when not to use this tool vs siblings, but the context is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds behavioral details: fan-out to multiple sources, soft-fail for patents, return format with grouped changes and citation URIs. It 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?

The description is slightly long but front-loaded with example queries. Every sentence adds value; it is efficiently structured 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 3 parameters, no output schema, and rich annotations, the description is complete. It covers sources, constraints, fallback behavior, return format, and provides guidance on when to use an alternative.

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%. The description adds meaning with examples for 'since' (ISO date or relative shorthand), clarifies 'type' only supports 'company', and explains 'value' accepts ticker or CIK.

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: a change feed for a company fetching recent changes from SEC, GDELT/GNews, and USPTO. It provides example queries and distinguishes from sibling tool entity_profile.

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

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

Explicitly says when to use this tool (for recent changes) and when to use entity_profile (for static profile). It also explains the parallel fan-out and news source preference (GDELT preferred, GNews fallback).

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

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

Annotations already provide idempotentHint=true and destructiveHint=false. The description adds valuable context about scoping by identifier and persistence differences between authenticated and anonymous sessions, going beyond annotations.

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

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

The description is three well-structured sentences, front-loaded with purpose, then usage guidelines, then technical details. 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 simplicity of the tool (write a memory), the description covers purpose, usage, persistence, scoping, and complementary tools. No output schema is needed as it's a write operation.

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 parameter descriptions are clear. The description does not add extra meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description states the specific verb 'save' and resource 'data', and clarifies the scope 'across this conversation or across sessions'. It clearly distinguishes from sibling tools like recall 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?

Explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget, providing clear context for when to use this tool versus alternatives.

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

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

Description adds value beyond annotations: reveals auto-disambiguation, specific return fields (ticker, CIK, RxCUI, etc.), and citation URIs. It mentions internal cascading through endpoints. No contradictions with annotations.

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

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

Description is compact (4-5 lines) and front-loaded with common query patterns. Every sentence adds value with examples and specifics. No redundant or unnecessary text.

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 what the tool returns for each type. Covers internal behavior and provides sufficient context for an agent to understand usage. Completeness is high given moderate complexity.

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

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

Explanation of 'type' and 'value' parameters is detailed with examples. For company: value can be ticker, CIK, or name, returning ticker+CIK+name+URI. For drug: value as brand/generic, returns RxCUI+ingredient+brand+URI. Adds significant meaning beyond schema.

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

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

The description clearly states the purpose: resolving user-spoken names to canonical/official identifiers. It provides specific examples (ticker, CIK, RxCUI) and supported types (company, drug). It distinguishes itself from sibling tools by focusing on name-to-ID resolution, which is not done by any sibling.

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 FIRST whenever you have a name but need an ID.' Also notes that it replaces 2-3 manual lookups. Lacks explicit when-not usage, but the positive guidance is strong and 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?

Beyond annotations (readOnly, idempotent, non-destructive, openWorld), the description adds that it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also describes the return format (ranked list with score, confidence, signal density). No contradictions.

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

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

The description is four sentences, each adding value: purpose, mechanism, use case, output. It is front-loaded and efficient, 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?

Given no output schema, the description adequately covers return values (ranked list with score, confidence, signal density). It explains the probing method and subject treatment. Annotations cover safety and idempotency, so the description is complete for this 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 coverage is 100%, so baseline is 3. The description adds value by explaining the 'entities' array treats the first entry as the subject, and clarifies the 'models' parameter's API key requirement. These details enhance the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using a specific verb and resource. It distinguishes itself from the sibling tool `ai_visibility_check` by focusing on multiple entities and ranking.

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 it is useful for competitive AI-marketing audits and provides an example question. It implies when to use (comparing multiple brands) versus the single-entity `ai_visibility_check`, though it does not explicitly list alternatives.

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

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

Discloses behavior beyond annotations: fans out to multiple sources, returns summary block and details, handles partial failures (bundlephobia first measurement delay, sources_failed listing). 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 verbose but well-structured, front-loading the core purpose. Each sentence adds meaningful information. Could be slightly trimmed, but overall efficient for the 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?

Given two parameters and no output schema, the description fully explains the return format (summary block fields, per-advisory detail, links, alternative versions) and partial failure handling. Complete for the tool's scope.

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. Description adds value by noting scoped packages are accepted for 'package' and default behavior for 'version'. Schema already provides basic info, but description clarifies edge cases.

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 is a composite check for npm packages across deps.dev and bundlephobia, with a specific verb+resource structure. It distinguishes itself from siblings by explicitly describing its unique functionality.

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 use cases like 'is X safe / popular / small' and ecosystem limitations (NPM only in v1). Mentions partial failures and fallback behavior. Slightly lacks explicit when-not-to-use guidance, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: it uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, and caps input at 200K chars with truncation and flagging. 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 but is well-structured and informative. Every sentence adds value. It could be slightly more concise, but it is not verbose.

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 explains what is returned: top-N passages with character offsets and similarity scores. It also covers the embedding model, windowing strategy, and input length limit. This is complete for a search tool.

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

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

Schema description coverage is 100%, but the description adds significant meaning: it explains 'text' as document text, 'query' as a natural-language query with concrete examples, and 'limit' as max passages with range and default. This goes beyond the schema's minimal descriptions.

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

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

The description clearly states the tool's purpose: semantic search inside a fetched record. It specifies the action (search within), the resource (record text), and the output (top-N passages with offsets and scores). It also distinguishes from sibling tools like ask_pipeworx_grounded by explaining how they pair together.

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

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

The description explicitly states when to use this tool: 'when the record is too big to cram into the prompt.' It also explains how it pairs with ask_pipeworx_grounded. However, it does not explicitly state when not to use it or list alternatives.

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

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

The description claims creation (write operation), but annotations set readOnlyHint=true, indicating a read-only tool. This is a direct contradiction that misleads about the tool's effect.

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 but front-loads the key purpose. It is informative without being excessively long; minor structural improvements like bullet points would make it more scannable.

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

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

The description covers the type, params, delivery, authentication requirement, and return value (subscription id). Given no output schema, this is adequate. However, it could mention pagination or error cases.

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

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

With 100% schema coverage, the description adds significant context beyond the schema: explains the v1 type, gives examples for params (items), and details delivery options. This helps the agent understand parameter usage.

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 'Create a proactive monitoring subscription to a live-data event stream', clearly identifying the action and resource. It specifies the supported type 'sec_8k' and distinguishes from sibling tools like list_subscriptions 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?

The description provides context on when to use: for monitoring SEC 8-K filings, and mentions prerequisites (Pipeworx OAuth account). It does not explicitly state when not to use or compare with alternatives, but the context is clear.

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

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

Description says 'Cancel a subscription' indicating a write operation, but annotations have readOnlyHint=true. This is a direct contradiction, severely undermining trust. The deactivation vs deletion detail adds value, but the contradiction overrides.

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

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

Two sentences, front-loaded with purpose, then ownership and behavioral nuance. Every sentence serves a distinct purpose with zero waste.

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 ownership, side effects (deactivation vs deletion), and ties to historical availability. Lacks explicit error handling or return behavior, but for a 1-param tool with no output schema, it is adequately 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 has 100% coverage with description for the id parameter. Description adds 'by id' context, which aligns with schema. No additional semantic depth beyond what is already provided.

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?

Explicitly states 'Cancel a subscription by id' with clear verb and resource. Specifies ownership enforcement and deactivation behavior, distinguishing it from siblings 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?

Covers ownership constraint ('only cancel your own subscriptions'), providing clear when-to-use guidance. Does not explicitly state when not to use or mention alternatives, but the context 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?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral details: returns a verdict with specific values, extracted structure, actual value with pipeworx:// citation, and percent delta. It also reveals data source (SEC EDGAR + XBRL). 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 concise, starting with example queries to quickly convey purpose, then specifying scope and output. Every sentence adds value, but it is slightly longer than necessary.

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 one parameter, good annotations, and no output schema, the description provides sufficient context: scope (company-financial), source (SEC EDGAR), and return format (verdict, citation, delta). It is nearly complete, though it lacks example output values.

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% for the single 'claim' parameter, with schema description providing examples. The tool description adds semantic context by explaining the type of claims supported (company-financial) and the natural-language format, going beyond the schema.

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

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

The description clearly identifies the tool as a natural-language claim verification tool with specific examples like 'fact check' and 'verify the claim that…'. It distinguishes it from siblings by stating it replaces 4–6 sequential calls and is specialized for company-financial claims via SEC EDGAR + XBRL.

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: 'Use whenever the agent needs to check whether something a user said is factually correct.' Gives context about replacing multiple calls. However, it does not explicitly state when not to use it (e.g., for non-financial claims).

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