Openfigi

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

The description goes beyond annotations to explain key behaviors: default model (Workers AI Llama-3.3-70b free), optional Anthropic probing with BYO key, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also implies idempotency and non-destructive nature, consistent with 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, well-organized paragraph that front-loads the core purpose, then efficiently covers defaults, optional configuration, return structure, and use cases. Every sentence adds value; no redundancy or filler. Ideal length for quick comprehension.

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 (4 params, no output schema), the description is fully complete. It explains what the tool does, how to use each parameter, the return format, and typical use cases. It hints at cost implications for Anthropic and mentions the free default. An explicit note about rate limits or data retention would be nice but is not necessary for basic 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?

Although schema coverage is 100%, the description adds valuable semantic details for each parameter: entity examples ('Pipeworx', 'OpenInvoice'), model options with cost implications and defaults, API key format guidance (sk-ant-...), and context usage for disambiguation. This provides the agent with practical knowledge beyond the schema's technical 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 clearly states the tool's purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the action, the resource (LLMs), and the output format. It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on per-model visibility scoring. The reference to default and optional models adds specificity.

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 usage context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It mentions default model and when to provide an API key for Anthropic. However, it does not explicitly state when not to use this tool or compare it to alternatives like 'scan_competitor_ai_presence' or 'entity_profile', which could help an agent choose between them. It implicitly covers when to use but lacks explicit 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?

Description adds value beyond annotations by explaining the tool routes questions to thousands of sources and returns structured answers with stable citation URIs. Annotations already declare safe read-only behavior, and nothing contradicts.

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 key directive, then efficiently lists supported data types, examples, and parameter tips. 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 no output schema, the description clearly states the return type (structured answer with citations). Covers all parameter info, usage scenarios, and behavioral aspects adequately 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 coverage is 100%, so description doesn't need to add parameter details, but it provides useful context on what kinds of questions work best (natural language, domains), which goes beyond the schema definition. The alias list is also mentioned.

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 is for factual questions about current/historical data from authoritative sources, listing specific domains (SEC, FDA, etc.) and example queries. Distinguishes itself from web search with 'PREFER OVER 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?

Explicitly advises using this tool over web search for factual questions, provides a list of question types ('what is', 'look up', etc.) and numerous concrete examples covering a wide range of domains.

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 safe read operation. Description goes far beyond by detailing fan-out, response shapes, resolver contract, parent event extractor, news fallback, safety mechanisms (low-confidence, closed markets), and blocking paths. Full behavioral disclosure.

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?

Long but well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.). Front-loaded with core purpose and usage. Every section adds value, though could be slightly 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?

No output schema, but description covers return values thoroughly: market fields, analysis, evidence, resolver confidence, parent event, news fields, error states. 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 covers 100% parameters with descriptions. Description adds context: explains depth enum values, include_raw impact on response size, and market input formats. Adds 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?

Description clearly states the tool researches Polymarket bets by pulling Pipeworx data. Specifies input types (slug, URL, question text) and output (evidence packet + comparison). Distinguishes from siblings as a dedicated bet research 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?

Explicitly states usage scenarios: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. No explicit exclusions or alternatives, but context is clear.

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

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

Beyond annotations (readOnly, idempotent, not destructive), description details data sources (SEC EDGAR, FAERS), specific metrics per type, handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. Adds substantial behavioral context.

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

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

Description is detailed yet well-structured, front-loading the purpose and then layering specifics. Every sentence adds value; no redundancy. Slightly verbose but acceptable given 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?

Despite no output schema, description fully covers return behavior (paired data, citation URIs, sorting). It addresses off-calendar fiscal years, entity type specifics, and compares to siblings. Complete for a comparison tool.

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

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

With 100% schema coverage, baseline is 3. Description adds meaning by explaining enum semantics (what data is pulled for each type) and providing examples for the values array, thus enriching the schema beyond its basic descriptions.

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

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

Description clearly states verb 'compare' and resource 'entities', specifies range (2-5 companies or drugs), and distinguishes from sibling tools like entity_profile by emphasizing parallel comparison vs sequential 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?

Explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists trigger phrases. Provides clear context for when to use this tool over 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 and idempotentHint, so the description adds value by detailing the return format (top-N tools with names, descriptions, full schemas, examples) and stating that no second schema lookup is needed. 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 relatively concise given the amount of information conveyed. It starts with the core purpose, lists use cases, explains the return value, and gives a usage hint. Each sentence is meaningful, though slightly verbose with domain 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 an output schema, the description adequately explains that the tool returns top-N tools with names, descriptions, and full input schemas with examples. It covers the important aspect that 'each result is ready to call directly.' Missing details like pagination sorting are non-critical.

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 does not add semantic value beyond what the schema already provides; it merely repeats that the tool takes a query and limit. No additional parameter meaning is introduced.

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' and lists numerous specific domains (SEC filings, FDA drugs, etc.), making the purpose unmistakable. It also distinguishes from siblings by emphasizing that this is the tool to call first for discovering available options, not for directly executing a task.

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

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

The description provides explicit usage guidance: 'Use 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.' It lacks explicit when-not-to-use or alternative tool suggestions, but the context is clear enough for an agent.

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

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

Annotations already declare readOnly/openWorld/idempotent. Description adds: fans out across multiple sources, soft-fails for patents, returns specific fields, names not supported. 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?

Description is long but well-structured with example queries at start, then details. Every sentence adds value; could be slightly more concise but 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 2 parameters with full schema coverage, rich annotations, no output schema, the description thoroughly explains return fields (CIK, filings, fundamentals, patents, news, LEI) and fallbacks. 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%, but description adds crucial context: type only 'company', value accepts ticker or zero-padded CIK, names not supported. This clarifies use 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?

Description clearly states it returns a full cross-source profile of a US public company in one call, contrasting with chaining single-pack lookups. Verbs like 'tell me about', 'research', 'brief me' matched to 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?

Explicitly says when to use (holistic view) and when not to (names only, use resolve_entity first). Warns about USPTO API sunset and fallback behavior.

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, so the description primarily adds the context that this is a subset of /search, confirming its behavioral scope. No contradictions exist.

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 with no wasted words. It is front-loaded with the tool's purpose, making it highly efficient for rapid comprehension.

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 and comprehensive annotations, the description is nearly adequate. However, it fails to explain the optional parameters, which are necessary for full usage understanding, making it slightly incomplete.

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 0%, so the description must compensate. It mentions idType and value, the required parameters, but does not explain the optional parameters currency and exchCode, leaving their purpose unclear. This is insufficient for full parameter 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 'Filter search by idType + value (subset of /search)' clearly states the tool's function as a filter and distinguishes it from the sibling tool 'search', indicating it's a more specialized version. However, it does not specify the exact scope or what the output is, slightly limiting clarity.

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 the tool should be used when filtering by idType and value, as it is a subset of /search. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it, 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 provide destructiveHint=true and idempotentHint=true. The description adds context like 'previously stored' and 'clear sensitive data', but doesn't detail failure modes or irreversibility. Still adds value 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?

Three concise, front-loaded sentences that each serve a purpose: stating the action, providing usage guidance, and pairing with related tools.

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 one-parameter tool with comprehensive annotations, the description covers all necessary aspects: purpose, usage, and behavioral hints. No 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% with a description for the 'key' parameter. The description does not add new information beyond saying 'by key'. 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 explicitly states 'Delete a previously stored memory by key', with a clear verb and resource. It distinguishes from sibling tools like 'remember' and 'recall' by focusing on deletion.

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

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

Provides explicit when-to-use scenarios: 'context is stale, task is done, or want to clear sensitive data'. It also suggests pairing with 'remember' and 'recall', offering clear guidance.

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

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

Annotations already confirm read-only, idempotent, non-destructive. The description adds context about fetching, extracting, and emitting format. It could mention limitations like URL accessibility but overall adds value.

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 3 sentences covering purpose, process, output, and use cases. No redundant information.

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

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

The description explains output format and process well for a simple tool. Missing error handling or edge cases, but acceptable given the tool's straightforward nature.

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 tool description repeats some info but adds no new semantic details 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 generates an llms.txt file for a URL, with specific verb 'Generate' and resource 'llms.txt file'. It distinguishes from siblings like scan_competitor_ai_presence by focusing on file generation.

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

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

The description provides explicit use cases: getting client site indexed, drafting own project, auditing competitor. However, it does not mention when not to use or alternative tools for similar tasks.

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, idempotentHint, destructiveHint. Description adds batch nature but no new behavioral traits beyond annotations. 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?

Single sentence, front-loaded with purpose, no wasted words. 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?

With output schema and rich annotations, the description covers the core purpose. Missing batch size limits or error handling, but overall 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 baseline 3. Description adds no extra meaning beyond schema; the parameter structure is fully documented in the schema.

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

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

Description clearly states the tool maps batches of instrument-id queries to FIGIs, with a specific verb and resource. This distinguishes it from siblings like resolve_entity or 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?

Context is clear: batch mapping to FIGIs. No explicit when-not or alternatives, but the purpose implies usage. Lacks exclusions but is adequate.

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 all false (readOnly, destructive, etc.). The description adds important behavioral details: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. This provides transparency 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 a single paragraph that is well-structured and front-loaded with purpose, then usage, then constraints. It is clear and not verbose, though could be slightly 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 simplicity (feedback submission) and full schema coverage, the description is complete. It covers purpose, usage, behavioral details, and parameter semantics. 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%. The description adds meaning to the 'type' parameter with examples, explains context as optional structured context, and specifies 'message' details (be specific, 1-2 sentences, 2000 chars max). This enriches 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: to tell the Pipeworx team about bugs, missing features, data gaps, or praise. It specifies the types of feedback and distinguishes itself from sibling tools which are for queries, research, 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?

The description explicitly states when to use: when a tool returns wrong data (bug), when a missing tool is needed (feature/data_gap), or for praise. It also gives guidance on what to include and what not to include (don't paste end-user prompt).

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 cover read-only, idempotent, non-destructive behavior. The description adds valuable context: data source (CF analytics-engine), privacy (no PII), and caching policy (5min-1h). 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 lean, front-loading the core purpose in the first sentence. Use cases are bulleted efficiently. No redundant or filler sentences; 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 tool with one parameter, no required fields, and no output schema, the description covers purpose, usage context, data source, privacy, and caching. It could optionally elaborate on the return format (e.g., structure of 'top tools'), but the current detail is sufficient for effective use.

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

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

Schema coverage is 100% with one parameter having an enum. The description adds meaningful interpretation: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', which goes beyond the raw enum list.

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 ('returns') and identifies the exact resource ('top tools, top packs, total call volume over a recent window'). It clearly distinguishes itself from sibling tools like 'discover_tools' or 'search' by focusing on aggregated trending usage by AI agents.

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 (hot data sources, canonical choice, alignment check) along with guidance on window selection. While it lacks explicit when-not-to-use or alternative tool references, the context is sufficient for appropriate invocation.

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 well beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by detailing internal logic: monotonicity checks, partition-sum checks, Jaccard similarity threshold, placeholder filtering, and response structure. 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 detailed and well-structured with sections (REQUIRES, mode descriptions, SEMANTIC ANCHOR, PARTITION FILTER, Response). However, it is somewhat verbose and could be trimmed for conciseness without losing essential information.

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

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

Given the tool's complexity, high schema coverage, and absence of an output schema, the description is remarkably complete. It covers input requirements, internal processing (filters, similarity checks), and response structure. Missing error handling details are minor.

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

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

With 100% schema description coverage, the parameters are already well-documented in the schema. The description adds significant value by explaining the behavior of each parameter, such as the format for event slugs, the semantic anchor for topic, and how modes differ. This surpasses the baseline of 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: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the resource (arbitrage opportunities on Polymarket) and the action (find) while distinguishing between two modes (event and topic), which differentiates it from sibling 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 provides explicit guidance on when to use each mode: 'event (recommended for a specific market)' and 'topic (for cross-event scanning)'. It also warns that calling with no arguments fails. However, it does not directly compare to sibling tools like polymarket_edges or polymarket_kalshi_spread, which could provide alternative functionalities.

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 as false. The description goes far beyond, detailing caching at 1h, the three response segments, edge calculations, and diagnostics. It explains why Fed bets are weak without paid data. 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 dense and long, but it is front-loaded with the core purpose and then structured into segments. It uses capitalization for segment names and line breaks to improve readability. While not concise, every sentence adds value for a complex tool. A perfect 5 would require more brevity while retaining completeness.

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 (9 parameters, no output schema, nested objects described in text), the description is remarkably complete. It explains the full output structure by_segment, diagnostics, and metadata like edge_pp_net and 24h-move warnings. It also covers the rationale behind each segment's gates. Without output schema, the description fully compensates.

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

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

All 9 parameters have schema descriptions (100% coverage), so baseline is 3. The description adds context by explaining the 'tradeable-edge knobs' concept, such as how min_liquidity and max_spread_pp filter unrealizable opportunities, and why min_partition_leg_kelly is needed for partitions. This adds meaning beyond the schema, justifying a 4.

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 what the tool does: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses specific verbs and resources, and distinguishes itself from siblings like polymarket_arbitrage by focusing on goal-oriented opportunity discovery rather than just arbitrage. The phrase 'Built for "what should I bet on today"' reinforces its distinct purpose.

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 positions the tool as the choice for daily betting decisions, avoiding the need to manually page through markets. It notes that Fed bets surface but are excluded from ranking, implying a filter context. However, it does not explicitly state when not to use this tool or mention alternatives like polymarket_arbitrage for specific tasks, 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?

The description goes well beyond the annotations (readOnlyHint, idempotentHint) to explain non-obvious behaviors: compatibility warnings for non-equivalent bet shapes, temporal alignment checks, and skipped cross-type/subtype counters. 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-organized with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence adds value, though the length could be slightly trimmed without losing clarity. Headings improve scannability for an AI 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?

Without an output schema, the description fully covers the return structure: leg prices, spreads, compatibility_warning branches, temporal_alignment, and skipped counters. It also explains edge cases like when spreads are meaningless, ensuring the agent can interpret results 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 for all parameters. The description adds significant context beyond the schema, such as the list of topic shortcuts, the fact that explicit tickers override topic-mapped sides, and the meaning of the response fields related to safety.

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: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It specifies two modes (topic shortcuts and explicit tickers) and distinguishes it from related tools like polymarket_arbitrage by focusing on cross-venue pricing 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 explicit guidance on when to use (e.g., to find spreads) and when not to (via compatibility_warning cases). It notes that most pre-mapped topics return warnings and are not tradeable. However, it does not directly compare with 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=true, idempotentHint=true, destructiveHint=false. The description adds value by specifying scoping ('by anonymous IP, BYO key hash, or account ID') and the expected pairing with remember and forget.

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 concise sentences: the first states the core action, the second provides usage context, and the third adds scoping and pairing details. No wasted 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 lacking an output schema, the description clearly explains what the tool returns (value or list of keys), scoping, and relationship with siblings. It is fully adequate for a simple retrieval 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 the description's explanation ('omit the key argument to list all keys') mirrors the schema's own description, adding no new semantic information beyond what is already present.

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 resources ('value previously saved', 'saved keys'), and explicitly distinguishes from sibling tools (remember, forget) by mentioning them in context.

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

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

It states when to use (look up stored context) and mentions omitting key to list, but does not explicitly contrast with alternatives like search or provide when-not-to-use 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 readOnly, openWorld, idempotent, non-destructive hints. Description adds valuable behavioral context: fans out to multiple APIs, explains GDELT→GNews fallback on rate limits/errors, and notes USPTO soft-fail until reactivated. 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 packed with useful information but is somewhat lengthy. However, it is well-structured: opens with example queries, then explains backend behavior, parameter details, and finally exclusion condition. 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 lacking an output schema, the description clearly states the return format (structured changes grouped by source, total count, citation URIs). It also covers fallback behavior and soft-fail cases. For a complex tool with 3 required parameters, this is thorough.

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 parameter descriptions. Description enriches understanding: explains accepted formats for 'since' (ISO date or relative shorthand with examples), clarifies 'value' accepts ticker or CIK, and notes 'type' is currently limited to 'company'. Adds practical guidance 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?

Description explicitly states it provides a change feed for a company in a given time window, with concrete example queries. It clearly distinguishes from the sibling tool 'entity_profile' which serves static profiles.

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?

Begins with usage examples (e.g., 'What's new with X') and ends with an explicit when-not-to-use: 'Use entity_profile instead when you want the static profile...'. Provides clear context for when this tool is appropriate 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 identity scoping, persistence differences (auth vs anonymous, 24-hour expiration) that annotations don't cover. No contradictions with annotations (idempotentHint, destructiveHint).

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 concise sentences with front-loaded purpose. 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?

Given simple tool (2 params, no output schema, good annotations), description thoroughly covers usage, storage semantics, and sibling relations. Complete and self-contained.

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 100% of parameters with descriptive examples. Description confirms key-value nature but does not provide additional semantic detail beyond what's in the schema.

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

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

Description clearly states tool saves data for later reuse across conversations/sessions, with specific examples (ticker, address, preference). Distinguishes from siblings 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 advises using when discovering something worth carrying forward and notes pairing with recall/forget. Provides real-world examples of appropriate use cases.

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

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

The description adds significant behavioral context beyond the annotations: it reveals that the tool internally cascades through multiple endpoints, auto-disambiguates input, returns specific fields (ticker, CIK, RxCUI, citation URIs), and supports two entity types with different return structures. Annotations already indicate safe read-only, idempotent behavior, and the description complements them well.

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

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

The description is well-structured: front-loaded with practical examples, followed by clear purpose, usage directive, and detailed type-specific information. Every sentence adds value, and there is no redundancy or fluff.

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

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

Given the complexity of two entity types and cascading lookups, the description covers all necessary details: input acceptance, return fields, citation URIs, and internal behavior. No output schema exists, but the description adequately explains expected 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 coverage is 100% with both parameters described. The description enriches parameter meaning by providing concrete examples ('AAPL', '0000320193', 'ozempic'), explaining auto-disambiguation, and detailing return fields per type. This adds value 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: resolve a user-spoken name to a canonical/official identifier. It provides specific examples ('ticker', 'CIK', 'RxCUI') and distinguishes itself from siblings with the directive 'Use FIRST whenever you have a name but need an ID.'

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 the tool ('Use FIRST whenever you have a name but need an ID') and notes it replaces multiple manual lookups. However, it does not specify when not to use it or name alternative tools for other 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?

Describes the probing mechanism, ranking by score, and output fields (score, confidence, signal density). Adds value beyond annotations (readOnlyHint, idempotentHint) by explaining the comparative analysis 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?

Two sentences plus an example, front-loaded with purpose. No wasted words; every sentence adds essential information.

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

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

Given tool complexity (multiple entities, models, optional context), description covers purpose, input semantics, output structure, and use case. No output schema but description explains output. Annotations cover safety. Reasonably 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%, but description adds extra semantics: first entity is treated as subject, models default to workers-ai, and explains API key usage. This enriches 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 ai_visibility_check, ranking, and surfacing most/least recognized. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic).

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 a concrete use case example for competitive AI-marketing audits, implying when to use. Does not explicitly state when not to use or list alternatives, but the context is clear enough.

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

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

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds context: fans out to two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, sources_failed will list it. Also explains return structure (summary block, per-advisory detail, links). 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?

Description is somewhat long but well-structured: purpose, usage, return details, limitations. Front-loaded with core functionality. Each sentence provides distinct information. Could be slightly tighter but appropriate for 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?

No output schema, so description must fully explain returns—and it does. Covers summary block, advisories, links, alternative versions. Also handles partial failures, timing, ecosystem limitations. 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 coverage 100% with descriptions for both parameters. Description adds useful details: 'Scoped packages accepted' for package, and 'Defaults to latest published version' for version. 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?

Description clearly states composite check for npm package addition, fanning out to deps.dev and bundlephobia. It specifies the exact use case (is X safe/popular/small) and what it returns, distinguishing from siblings like deps.dev:version.

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: when agent asks about package safety/popularity/size. Also states NPM-only in v1 and alternatives for other ecosystems (PyPI, Maven, etc. fall under deps.dev:version). Includes graceful degradation info.

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, and idempotentHint=true, so the description does not need to reiterate these. It adds no further behavioral context such as pagination or result format, 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?

The description is very concise with a single sentence. However, it is under-specified for the tool's complexity; conciseness should not sacrifice necessary detail.

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

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

Given 5 parameters, a general-purpose search tool, and an output schema, the description is too sparse. It does not explain what 'instruments' are, how to use optional filters effectively, or what the output contains.

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 0% schema description coverage and no parameter details in the description, the agent receives no additional meaning beyond the schema property names. The examples in the schema are not part of the 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 performs text search across instruments, which is specific and action-oriented. However, it does not differentiate from sibling tools like 'filter' or 'resolve_entity' and could be more precise about the domain of instruments.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'filter' or 'resolve_entity'. The description lacks context about appropriate scenarios, prerequisites, or exclusions.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral context: it details the output (verdict, structured form, actual value with citation, percent delta), the data source (SEC EDGAR + XBRL), and efficiency gains. 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 a single paragraph of moderate length, front-loaded with trigger phrases and clear statements. It includes all necessary information without excessive verbosity, though it could be slightly more streamlined.

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 details the return value (verdicts, extracted form, actual value, citation, percent delta). It covers purpose, usage, domain, source, and limitations (v1 supports company-financial claims). This is comprehensive for a single-parameter tool with rich 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?

Input schema already provides a good description for 'claim' with examples (100% coverage). The tool description adds the domain (company financials) and more examples, reinforcing the expected input format and scope, going slightly 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?

Description clearly states the tool does natural-language claim verification against authoritative sources, specifically for company-financial claims (revenue, net income, cash position). It includes trigger phrases and explicitly distinguishes the tool's composite nature from sequential alternatives.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct.' It lists example trigger phrases and contrasts with the alternative of 4-6 sequential calls, making the usage context clear.

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