Data360

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

Discloses return structure (per-model fields + combined view), Anthropic pricing implication (BYO key, pay directly), and model default. Annotations already indicate read-only, idempotent, safe; description adds concrete behavioral details.

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 paragraph, 4 sentences, front-loaded with core purpose. Every sentence adds value; no 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?

With no output schema, description fully outlines return fields and combined view. Covers all 4 parameters, pricing nuance. Adequate for 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. Description adds: default model for 'models' is workers-ai, '_apiKey' only needed for anthropic, 'context' disambiguates. 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?

Clear verb (Probe), resource (LLMs for visibility), and output (score 0-100 per model). Distinguishes from sibling 'scan_competitor_ai_presence' by specifying per-model scoring and BYO key for Anthropic.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Default model and optional Anthropic with BYO key explained. Lacks explicit when-not-to-use or comparison to siblings.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. Description adds behavioral context: routes to one of 3,300 tools, fills arguments, returns structured answer with stable citation URIs. Does not detail failure modes or timeouts, which would be beneficial.

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 'PREFER OVER WEB SEARCH', followed by categories and examples. Some redundancy (repeated examples of query patterns), but overall well-organized 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?

Given the broad scope and no output schema, the description adequately covers usage, examples, and output format (citations). Could mention expected response shape or handling of unanswered questions, but it's sufficient for effective agent usage.

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

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

Schema covers 100% of parameters with descriptions of aliases. Description adds value by explaining how the question is processed (routed, arguments filled) and providing domain examples, which helps agents formulate effective queries beyond schema syntax.

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 routes factual questions to authoritative sources, preferring over web search. It lists specific resource categories (SEC filings, FDA data, etc.) and distinguishes from sibling tools like 'discover_tools' or 'validate_claim'.

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 guidance to prefer over web search for data queries. Lists trigger phrases ('what is', 'look up', 'find') and provides examples. Implicitly excludes creative or subjective questions by focusing on factual, structured data.

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?

Extremely detailed description of resolver contract, parent event extractor, news fields, safety mechanisms (low-confidence short-circuit, closed market handling, wide spread notice). Annotations (readOnlyHint, idempotentHint) are consistent and description adds significant context beyond them. 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?

Long but well-structured: starts with purpose, then classifiers, fan-out examples, response shapes, resolver contract, parent event, news, safety. Every section adds necessary detail. Could be slightly trimmed but justified by tool 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 no output schema, description fully compensates by detailing response fields (market, analysis, evidence, resolver details, parent event, news). Covers edge cases (low confidence, closed markets, wide spreads). Highly complete for a complex tool.

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

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

Schema covers 100% of parameters with descriptions. Description adds valuable context: examples of market_input, depth behavior (quick vs thorough), include_raw effect (summarized vs full payloads), and handling of various 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?

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet + comparison). Implicitly distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage which focus on edge/arbitrage rather than single bet research.

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 ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). Includes classifiers, fan-out examples, and behavior for low-confidence/closed markets. Lacks explicit when-not-to-use 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?

Annotations declare readOnlyHint, idempotentHint, destructiveHint; description adds detailed behavioral context: data sources (SEC EDGAR/XBRL, FAERS, FDA), fix for fiscal year handling, return of paired data with citation URIs. 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 well-structured, starting with the main purpose then providing specifics. It is detailed but not verbose; 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 comparative tool with two entity types and multiple data sources, the description covers key aspects: input constraints, data sources, behavior (sorting, fiscal year fix), and return type. No output schema but return described adequately.

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

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

Schema coverage is 100% for both parameters (type and values). Description adds value by explaining the data fetched per type and specifying format for values (tickers/CIKs for company, names for drug) and count 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?

Description clearly states the tool compares 2-5 companies or drugs side by side, listing specific data sources and metrics. Distinguishes from siblings by noting it replaces multiple 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?

Provides explicit example queries ('compare X and Y', 'X vs Y', 'which is bigger') and implies when to use over single lookups. No explicit alternatives given 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 denote read-only, idempotent, non-destructive. The description adds that it returns SDMX-style records with specified fields and disaggregation attributes, and behavior of TIME_PERIOD parameter.

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

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

The description is focused and each sentence contributes unique information. It could be slightly more compact but is effective.

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

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

The description covers the purpose, parameter usage, and return format adequately. For a tool with 6 params and no output schema, it offers sufficient context.

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 meaningful examples (WB_WDI, BRA, USA) and explains the role of each parameter in context, which aids agent understanding 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 'Fetch observations for one Data360 series' with a specific verb and resource. It provides parameter examples and distinguishes from sibling tools like data360_search_indicators by explaining that INDICATOR comes from there.

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 advises to get INDICATOR from data360_search_indicators and notes that omitting TIME_PERIOD returns full series. It doesn't explicitly mention when not to use this tool, 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds value by describing the output structure (DATABASE_ID, full name, indicator count) and the default for the 'top' parameter, which is not fully covered by 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 two concise sentences that front-load the purpose, provide concrete examples, and specify the output and usage guidance. Every sentence adds value 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 there is no output schema, the description adequately explains the return structure and connects the tool to its siblings. The single optional parameter is well-documented in the schema. The description is complete for a list tool that serves as a reference for other tools.

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

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

Input schema coverage is 100% with a clear description for the 'top' parameter. The description does not add additional parameter semantics beyond what the schema provides. Baseline 3 is appropriate as no extra value is contributed.

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 lists source databases from World Bank Data360, provides concrete examples (WB_WDI, IMF_BOP, WB_EDSTATS), and specifies the return fields (DATABASE_ID, full name, indicator count). It also distinguishes from sibling tools by explaining that DATABASE_ID is used to scope data360_search_indicators and data360_get_data.

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

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

The description explicitly mentions how to use the result (DATABASE_ID) with other tools, giving clear usage context. While it doesn't include explicit 'when not to use' instructions, the purpose is straightforward and the sibling tools are sufficiently differentiated.

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

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

Annotations already indicate readOnlyHint and idempotentHint, so the description's job is lighter. It adds value by stating the tool returns specific fields (codes, names, database IDs, units, definitions) and mentions cross-reference to other tools. 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?

Three sentences, each serving a distinct purpose: purpose, output description, optional parameter. Front-loaded with core functionality, no redundant or vague statements. Every sentence is essential.

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 explicitly enumerates return fields (indicator codes, names, database ID/name, units, definitions) and explains their use in data360_get_data. This fully covers what an agent needs to understand the tool's output and integration.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning by explaining that query is for keywords and supports '*' for all, and that database_id refers to sources from data360_list_databases. This connects parameters to real-world usage 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 action: 'Full-text search across all Data360 indicators'. It identifies the specific resources (WDI/IMF/WHO/ILO/education series) and distinguishes itself from sibling tools like data360_get_data and data360_list_databases by focusing on search and metadata retrieval.

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

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

The description provides usage context by explaining that returned indicator codes can be used in data360_get_data and that database_id optionally restricts search. It implicitly guides when to use this tool (for exploratory search) versus others, but could explicitly state when not to use it or mention alternatives like data360_list_databases for listing without search.

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 idempotentHint=true. The description adds that it returns top-N relevant tools with full schemas and examples, ready to call directly, and accepts multiple query aliases. No contradictions.

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

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

Two paragraphs: first explains purpose and domains, second details output and usage advice. Front-loaded with verb and resource. Minor redundancy but no wasted sentences.

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

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

Given 6 params (mostly aliases) and no output schema, the description covers what is returned (tools with schemas and examples), that results are callable directly, and usage advice. Sufficient for a discovery tool.

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

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

Schema coverage is 100%, but description adds value by noting multiple aliases for query (task, q, description, search) and specifying default/max limit (20, 50). Gives real examples, reducing ambiguity 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 "Find tools by describing the data or task" and lists many specific data domains (SEC filings, FDA drugs, etc.), distinguishing it from sibling tools that are more specific (e.g., bet_research, data360_*).

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 "Call this FIRST when you have many tools available and want to see the option set (not just one answer)", providing clear context for when to use. Lacks explicit 'when not to use' but the guidance is strong.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), description details return fields with caveats: recent_filings limited to 5, fundamentals note about FY2025 data, patents sunset, and GDELT→GNews fallback. 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?

Single paragraph with dense information; could benefit from more structured format like bullets, but every sentence adds unique value and the core purpose is front-loaded.

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

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

Given no output schema, the description comprehensively lists return fields and their statuses, covers limitations and fallbacks, and differentiates from sibling tools. Adequate for complex aggregation 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?

Even though schema descriptions cover parameters, the description adds crucial context: value accepts ticker or zero-padded CIK, explicitly excludes names, and references resolve_entity. Type limited to 'company'.

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

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

The description clearly states the tool aggregates all key data about a US public company in one call, with specific use cases like 'tell me about X' or 'brief me on Tesla'. It distinguishes from sibling tools like resolve_entity for name resolution.

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 (user asks for company overview, would otherwise call many tools) and when not to use (names not supported, use resolve_entity first). Provides alternative action.

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 aligns with annotations: destructiveHint=true is supported by 'Delete'. However, it does not mention the idempotentHint=true from annotations, which would be useful to note that deleting a non-existent key is safe. The description adds some context on sensitive data but misses explaining idempotency.

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 concise: first sentence states the action, second provides usage guidance. It is front-loaded and 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 simple tool with one parameter, destructive and idempotent, the description adequately covers purpose and usage. It is missing clarification on idempotent behavior, but overall it is sufficient given 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 a single parameter 'key' described as 'Memory key to delete'. The description repeats 'by key' but adds no additional 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 action (Delete) and resource (memory by key). It distinguishes from siblings like 'remember' and 'recall' by explicitly mentioning them, indicating this is the deletion counterpart.

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 when-to-use scenarios: stale context, task done, or clearing sensitive data. It also references alternative tools (remember, recall), though it does not explicitly state when not to use it.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds details about fetching, extracting, and emitting markdown, which aligns 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?

Single paragraph, front-loaded with purpose, no redundant sentences. Every sentence adds value: purpose, process, output format, use cases.

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 explains output format. Inputs, process, and use cases are covered. Could mention error handling or assumptions but sufficient for a straightforward tool.

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

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

Schema coverage is 100% with descriptions for both 'url' and 'max_links'. Description mentions URL and links but doesn't add new semantic meaning beyond schema; baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for AI crawlers, explicitly naming the verb 'generate' and the resource 'llms.txt file'. It distinguishes from siblings by focusing on llms.txt production, unlike other tools like ai_visibility_check or scan_competitor_ai_presence.

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 (client indexing, drafting, auditing) and mention of intended users (AI crawlers). It doesn't give when-not-to-use instructions, but the sibling context and clear purpose make alternatives apparent.

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 the annotations: rate-limited to 5 per identifier per day, free, and not counting against tool-call quota. It does not contradict the annotations (readOnlyHint=false, etc.) and adds valuable context about usage limits.

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

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

The description is concise at about six sentences, front-loads the purpose, and uses a clear structure: purpose, use cases, guidelines, and key highlights (rate limit, free). Every sentence earns its place.

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

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

Given the tool's simplicity (feedback submission), the description is complete: it covers purpose, usage guidelines, parameter details, and behavioral constraints. No output schema is needed, and the description provides sufficient context for an AI agent to use the tool correctly.

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

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

Although the input schema has 100% description coverage, the description adds extra meaning: it elaborates on the enum values (e.g., 'bug = something broke or returned wrong data') and clarifies the context object fields (pack, tool, vertical) and message constraints (be specific, 1-2 sentences, 2000 chars max).

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 send feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It uses specific verbs and resources, and distinguishes itself from sibling tools (none of which are feedback-oriented).

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 explains when to use the tool (for bugs, feature requests, data gaps, praise) and provides clear exclusions: 'don't paste the end-user's prompt'. It also mentions rate limits (5 per identifier per day) and that it's free and doesn't count against tool-call quota.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: self-aggregating from CF analytics-engine, no PII, cached 5min-1h depending on window. No contradictions, and this goes 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 sentences long, front-loaded with the main purpose, and each sentence adds value: what it returns, use cases, data source and caching. 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 one optional parameter and no output schema, the description covers the output (top tools, packs, volume), data source, and caching. It does not detail response format but that is acceptable for a simple tool; slightly incomplete but 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% with full description of the 'window' parameter (enum, meaning). The description mentions window in context of use cases (shorter for hot, longer for steady-state) but does not add new semantic information 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?

The description explicitly states the tool returns top tools, top packs, and total call volume over a recent window, with three concrete use cases (discovering hot data sources, confirming canonical tools, aligning use case). This clearly distinguishes it from siblings like ask_pipeworx.

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

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

The description lists three specific use cases (discovering hot data, confirming canonical tool, aligning use case) but does not explicitly mention when not to use it or suggest alternative siblings, so it provides clear context without 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?

The description adds substantial behavioral context beyond the annotations. It explains the monotonicity and partition-check algorithms, the Jaccard similarity threshold (0.30), the placeholder filter, and the response structure. There is no contradiction with annotations (readOnlyHint=true, etc.).

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

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

The description is comprehensive but somewhat lengthy. It front-loads the core purpose and modes, then dives into details. It could be slightly more structured with bullet points, but it is still clear and every sentence adds value.

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

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

Given the tool's complexity (two modes, algorithmic checks, threshold values, response format), the description is complete. It explains all necessary details for an agent to invoke the tool correctly, despite lacking an output schema the response structure is described.

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

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

Schema description coverage is 100%, and the description enriches each parameter with detailed usage: 'event' is for single-event mode with child market walking, 'topic' is for cross-event mode with similarity checks. It also describes the partition_check sub-parameter in context.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and explains the underlying logic. It is distinct from sibling tools like bet_research or polymarket_edges.

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

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

The description explicitly details when to use each mode: 'pass a single Polymarket event slug' for event mode and 'pass a seed question' for topic mode. It explains cross-event mode catches patterns missed by single-event mode. It could improve by stating when not to use the tool, but it still provides 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?

The description extensively explains behavioral traits beyond annotations: it details the three segments with formulas (lognormal barrier, GDELT, partition_overround with sport-specific alpha), caching (1h KV-level), output structure (by_segment, diagnostics), and tradeable-edge knobs. Annotations already indicate readOnly, openWorld, idempotent, and non-destructive, and the description aligns without contradiction.

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

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

The description is thorough but very dense, containing many technical details and nested explanations. While well-structured with bullet-like segments and knobs, it could be more concise. Some sentences are long and packed with multiple concepts, making it harder to parse quickly.

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

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

Given the tool's complexity (9 parameters, no output schema, sophisticated edge calculations), the description is remarkably complete. It explains the output structure (by_segment, diagnostics, fed_candidates), why segments might be empty (stale gates, knob drops), and provides actionable guidance on filtering. No output schema is needed because the description fully specifies the return fields.

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

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

All 9 parameters have descriptions in the input schema (100% coverage), but the description adds significant value beyond schema. For example, it explains the default slippage (0.3pp), suggests bumps for thin partitions, clarifies min_partition_leg_kelly's role for partition arbs, and provides context for min_liquidity and max_spread_pp as tradeable-edge filters.

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

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

The description clearly states the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price. It explicitly mentions the use case 'what should I bet on today' and differentiates from siblings like polymarket_arbitrage by explaining the three segments (model-driven, structural arbitrage, concentrated longshot).

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 strong context on when to use the tool (discovering opportunities without paging hundreds of markets) and gives detailed rationale for exclusion (e.g., Fed bets excluded from ranking). However, it does not explicitly mention when not to use it or directly compare to sibling tools like polymarket_arbitrage or polymarket_kalshi_spread.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds rich behavioral context: compatibility warnings (non-equivalent bet shapes, semantically unrelated events), temporal alignment fields, and skipped cross-type/subtype counters. This goes far beyond the annotations and fully discloses the tool's limitations.

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

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

The description is long but well-structured with sections (TWO MODES, RESPONSE, SAFETY FIELDS) and bullet points. Every sentence adds value given the complexity of the tool, though some redundancy could be trimmed (e.g., repeated warnings about pre-mapped topics).

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 the response structure thoroughly: leg-by-leg prices, matched spreads in pp, compatibility_warning, temporal_alignment, and skipped cross-type counters. This ensures the agent knows exactly what to expect and how to interpret the 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%, and the description adds value by explaining the topic shortcuts and the effect of explicit overrides (e.g., 'Overrides the topic-mapped Kalshi side'). It provides examples and clarifies how the two modes interact, which is not in the schema.

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

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

The description clearly states the tool's purpose: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It uses specific verbs and resources, distinguishing it from all 23 sibling tools (e.g., polymarket_arbitrage, compare_entities).

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

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

The description explains two modes (topic shortcuts and explicit parameters) and provides guidance on when each is appropriate. It warns that pre-mapped topics often return compatibility warnings, helping the agent set expectations. However, it does not explicitly state when not to use the tool versus alternatives, though the sibling list suggests no other tool does cross-venue spreads.

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 behavior. Description adds value by explaining scoping by identifier and listing behavior on omitted key, which goes 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?

Concise, front-loaded with main action, then use case, then scoping and pairing. Every sentence adds value 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?

For a simple tool with good annotations, the description covers modes, use case, scope, and pairing. Lacks return format details but is sufficient for agent usage.

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

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

Schema coverage is 100% and description adds extra meaning: explains omitting key triggers listing. This adds clarity beyond the schema's 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 retrieves a stored value or lists all keys, using specific verbs and resource types. It differentiates itself from siblings 'remember' and 'forget' by mentioning pairing.

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 (to look up context without re-derivation) and mentions alternatives (remember to save, forget to delete). Provides scoping context.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral context: fan-out to multiple sources, GDELT→GNews fallback with rate-limiting, USPTO PatentsView API sunset soft-fail, and date format details. 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 concise but information-dense. It starts with the core question, then lists usage examples, details behavior, and ends with alternative recommendation. Each sentence serves a purpose 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 tool's complexity (multiple sources, fallbacks, date formats) and no output schema, the description covers return structure (changes[], total_changes, citation URIs), tells when to use alternative, and provides complete parameter details. Annotations cover safety. The description is fully 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 coverage is 100%, so baseline is 3. The description adds value beyond schema by providing concrete examples for `since` ('7d', '30d', '3m', '1y'), recommending typical monitoring values, and clarifying that `type` only supports 'company'. This extra guidance justifies 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 the tool's purpose with a question ('What's new with a company in the last N days/months?') and provides concrete use cases ('what's happening with X', 'updates on Y', etc.). It also distinguishes from the sibling tool entity_profile by specifying when to use the alternative.

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

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

The description gives explicit usage guidance with examples and clearly states when not to use the tool ('Use entity_profile instead when you want the static profile...'). It also explains parallel fan-out behavior and fallback logic, helping the agent decide appropriately.

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 (authenticated vs anonymous, 24-hour retention), which enriches 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 three sentences with no wasted words. It front-loads the core purpose and efficiently covers usage, pairing, and persistence details.

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

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

Given no output schema, the description adequately explains the tool's behavior, pairing, and persistence rules. It is complete for a simple key-value store tool with 2 parameters and clear annotations.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description provides example keys like 'subject_property' and 'target_ticker' but does not add significant meaning beyond the schema, earning a baseline 3.

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

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

The description clearly states 'Save data the agent will need to reuse later' with a specific verb and resource, and distinguishes itself from sibling tools 'recall' and 'forget' by explicitly pairing with them.

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 when-to-use guidance: 'when you discover something worth carrying forward' and lists use cases like resolved ticker, address, preference. It also mentions persistence rules and pairing with recall/forget.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that the tool cascades through several lookup endpoints internally, which is behavioral context beyond annotations. It also mentions citation URIs and auto-disambiguation, adding value without contradicting annotations.

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

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

The description is a single paragraph but well-structured: first sentence states purpose and usage, followed by bullet-like details for each supported type. Every sentence adds value, though it could be slightly more concise. The key information is front-loaded.

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

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

Given no output schema, the description compensates by explicitly stating what is returned for each type (ticker + CIK + company_name + URI for company; RxCUI + ingredient + brand + URI for drug). It also explains auto-disambiguation and cascading behavior. This provides complete context for an agent to understand the tool's inputs and outputs.

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

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

With 100% schema coverage, the description still adds significant meaning: for 'type' it enumerates supported types with details; for 'value' it provides multiple input formats and examples (ticker, CIK, name for company; brand/generic for drug) and mentions auto-disambiguation. It also describes return values for each type, which is not in the schema.

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

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

The description clearly states the verb 'resolve' and the resource: user-spoken names to canonical/official identifiers. It distinguishes from siblings by specifying that it provides IDs required by other tools. Supported types (company, drug) are explicitly listed with examples.

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

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

It explicitly says 'Use FIRST when you have a name but need an ID.' This provides clear context for when to use the tool. It also notes that each call replaces 2-3 manual lookups. While it doesn't say when not to use, the purpose is clear enough to differentiate from siblings.

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

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

Annotations declare readOnly, idempotent, openWorld, non-destructive. Description adds that it probes each entity via ai_visibility_check, ranks by score, and returns 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?

Two sentences, front-loaded with purpose, no wasted words. Efficient and clear structure.

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

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

No output schema, but description fully explains return value (ranked list with score, confidence, signal density). Covers mechanism, use case, and constraints. Complete for 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 describes all 4 parameters (100% coverage). Description adds that first entity is treated as 'subject' for narrative, rest as competitors, adding context beyond schema.

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

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

Description clearly states it compares AI visibility across multiple entities side-by-side, probes with ai_visibility_check, ranks by score, and surfaces most/least recognized. Distinguishes from sibling tools like 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 concrete use case: competitive AI-marketing audits with example question. Lacks explicit when-not-to-use, but context implies it's for comparing multiple entities, not single entity queries.

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 declare read-only, idempotent, non-destructive behavior. Description adds valuable behavioral context: composite calls to two services, graceful degradation with sources_failed, and first measurement delay for bundlephobia. 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?

Single paragraph efficiently packs essential information with clear front-loading of purpose. Slightly dense but avoids redundancy. Could benefit from minor structural improvements like bullet points for return values.

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 structure (summary block, advisories, links, alternative versions), mentions partial failures and timing caveats, and clearly scopes to npm v1. Complete coverage for a complex tool.

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

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

Schema covers both parameters with descriptions. The description adds value by clarifying scoped packages accepted for 'package' and defaulting behavior for 'version', but does not significantly extend beyond schema's existing 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 defines the tool as a composite check for npm package dependency analysis, covering safety, size, and popularity. It distinguishes itself from siblings by focusing on package scanning rather than entity resolution or validation.

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: when an agent asks about safety/popularity/size or cost of adding a package. Also notes exclusivity to npm ecosystem and instructs to use deps.dev:version for other ecosystems. Provides partial failure handling 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that it returns a structured verdict with citation and percent delta, and replaces multiple calls. No contradiction; adds meaningful 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?

Two paragraphs are front-loaded with purpose and usage, followed by technical details. 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 one parameter, rich annotations, and no output schema, the description completely conveys what the tool does, its scope (company-financial), return structure, and how it improves over sequential alternatives.

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 clear description and examples. The description adds no new parameter-level information beyond reinforcing the same example.

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 that the tool fact-checks a natural-language factual claim, with specific verb+resource (validate/verify). It distinguishes from siblings by noting it replaces 4–6 sequential calls, and provides example queries.

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

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

Explicitly says to use when checking if something a user said is true, with example query patterns. Implicitly limits to when the claim is factual, but lacks explicit when-not-to-use or alternatives among siblings.

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