Askdata Xk

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by mentioning BYO key for Anthropic and that user pays Anthropic directly. Also describes return structure (per-model fields + combined view). 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 covering purpose, usage, and return value. Front-loaded with action. No unnecessary words. Every sentence earns its place.

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

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

No output schema, but description adequately describes return values. Covers all 4 parameters and default behavior. Contextualizes use cases. Complete for moderate complexity tool.

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

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

Schema description coverage is 100% so baseline is 3. Description adds meaning: entity (with examples), models (supported values), _apiKey (purpose and format), context (disambiguation). Goes beyond schema descriptions by explaining usage scenarios and defaults.

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 probes LLMs for knowledge and scores visibility (0-100) per model. It specifies the verb 'probe', resource 'LLMs', and outcome 'score visibility'. Distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on multi-model probing and scoring.

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

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

Explicitly states use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains default vs. optional behavior (default model, _apiKey for Anthropic). Lacks explicit when-not-to-use or alternatives but provides sufficient 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?

Annotations already indicate safe, idempotent, read-only behavior. The description adds value by explaining the routing mechanism across 3,300 tools, argument filling, and the structured citation URI output. This goes beyond annotations and provides useful behavioral context.

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

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

The description is effective but verbose, listing many domains and query examples. It front-loads the key message but could be trimmed. While not excessively long, a more concise version would improve readability.

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

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

Given the tool's broad scope (3,300 tools) and no output schema, the description adequately covers what the tool does, the type of questions to ask, and the format of responses (structured with citations). It provides enough context for correct invocation.

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

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

Schema coverage is 100% with thorough descriptions for question and its aliases. The description does not add new parameter details; it only reiterates examples. Baseline 3 is appropriate as schema does the heavy lifting.

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

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

The description starts with 'PREFER OVER WEB SEARCH' and clearly states the tool answers factual questions using structured data from 3,300 tools across 737 sources. It lists specific domains (SEC filings, FDA data, etc.) and gives example queries, making the purpose highly specific and distinguishable from siblings like web search.

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

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

The description explicitly advises preferring this tool over web search for current or historical structured data questions. It provides a heuristic: use when user asks 'what is', 'look up', etc. It does not explicitly list alternative tools but gives strong positive guidance and implies when not to use (e.g., open-ended opinion).

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

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

The description goes far beyond the annotations (readOnlyHint, idempotentHint, etc.) by detailing internal behavior: market resolution, classification, fan-out logic, response shapes, resolver contract (match confidence, alternatives), parent event extraction, news fallback, safety suppression, and edge cases like illiquid spreads. All disclosed behaviors are consistent with the annotations, fully informing the agent of what to expect.

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

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

The description is front-loaded with the purpose and then covers a large amount of detail in a single block. It uses labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.) which aid parsing, but the length could be slightly reduced by consolidating repeated patterns. Still, every sentence adds value, and the structure is logically organized.

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 (classifiers, fan-out, multiple data sources, edge cases), the description covers all critical aspects: input, internal logic, response fields, resolver behavior, parent events, news handling, and safety mechanisms. Without an output schema, it effectively documents the return structure through examples. Minor omissions like exact model probability formulas are acceptable given the breadth covered.

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

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

The description adds substantial meaning beyond the input schema. For 'market', it explains accepted formats (slug, URL, question text) with examples. For 'depth', it clarifies the difference between quick and thorough with source counts. For 'include_raw', it describes default behavior, when to set true, and the expected response size impact. These details are not present in the schema's property 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 opening sentence clearly states the tool researches Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text) and gives explicit use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). The extensive examples and classifiers leave no ambiguity about the tool's purpose and distinguish it from sibling tools like polymarket_edges or polymarket_arbitrage.

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

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

The description provides clear usage contexts and scenarios, including when to expect certain behaviors (e.g., inspecting market_match_confidence before trusting analysis). It explains safety mechanisms like low-confidence short-circuits and closed market handling. However, it does not explicitly state when to prefer this tool over its siblings or when not to use it, leaving room for minor 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 indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds valuable behavioral context: for company type it pulls LATEST 10-K data and mentions a fix for off-calendar fiscal years; for drug type it specifies adverse-event reports, FDA approval counts, and active trial counts. Also notes return of paired data and citation URIs.

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 dense block of text that could be more structured with bullet points or sections. It front-loads the core purpose but includes technical details about the off-calendar fiscal year fix and post-Run-6 fix, which could be placed separately or omitted for conciseness.

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

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

Given 2 simple parameters with 100% schema coverage and no output schema, the description explains data sources, behavior (sorting, citation URIs), and example use cases. It is fairly complete, though could mention that output is read-only and idempotent (implied by 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 both parameters described. Description adds meaning beyond enum: gives examples for values (tickers/CIKs for company, drug names for drug) and explains behavior in context (e.g., results sorted by primary metric). This enhances 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?

Description clearly states the tool compares 2-5 companies or drugs side by side, lists specific use cases like 'compare X and Y' and 'which is bigger', and specifies distinct data sources for each type (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs). It distinguishes itself from siblings by noting it replaces 8-15 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?

Description provides explicit when-to-use examples (e.g., 'compare X and Y', 'which is bigger', rank-by-metric questions) and notes results are sorted by primary metric so 'largest' reads off top. However, it lacks explicit when-not-to-use statements or direct alternatives among sibling tools like entity_profile for single entities.

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

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

Annotations already indicate readOnly and idempotent. Description adds that it returns top-N tools with schemas and examples, which is helpful context. 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 about 5 sentences, starts with core purpose, then lists domains, then explains output. Efficient but 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?

For a discovery tool with complex sibling set, the description fully covers what it does, when to use it, and what it returns (no output schema needed). Complements annotations and schema well.

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

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

Schema coverage is 100% with good descriptions and examples. The description only mentions the query parameter usage and limit default/max, adding little beyond what the schema provides. 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 finds tools by describing the data or task, lists many domains, and explains it returns top-N tools with schemas. It distinguishes itself from sibling tools as a meta-discovery tool to be called first.

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 'Call this FIRST when you have many tools available' and that results are ready to call without a second schema lookup. Does not explicitly state when not to use it, but the context makes it 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 indicate safe read operation. Description adds significant behavioral context: returns fields with details (e.g., fundamentals from latest 10-K, patents soft-fail due to API sunset, news via fallback). It discloses limitations clearly. Slight deduction for not explaining error handling or rate 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?

Description is front-loaded with purpose and usage, then lists returns in a structured bullet-like format. It is somewhat long but every sentence adds value. Could be slightly tighter (e.g., remove internal note about 'Run 6 fix').

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 comprehensively covers return fields with caveats (e.g., patents soft-fail, fundamentals from latest 10-K). Specifies limits (up to 5 filings) and fallbacks. Slight gap: no mention of response format or error codes. Overall well-rounded.

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. Description adds meaning: explains type is only 'company' for now, and value must be ticker or zero-padded CIK, not names. Also mentions resolve_entity as prerequisite. Provides context beyond schema, but could be more precise (e.g., validation rules).

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 'Get everything about a US public company in one call' with specific verb and resource. It distinguishes from siblings like resolve_entity by noting names not supported, and from compare_entities by focusing on a single entity. The use cases are concrete ('tell me about X', 'research Acme').

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 (when a user asks for company info or to consolidate many pack tools) and provides exclusions ('names not supported, use resolve_entity first'). This guides the agent away from misuse and towards alternatives.

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

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

Annotations already indicate destructive behavior, but description adds contextual use cases for deletion, such as clearing sensitive data. 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?

Three concise sentences, straight to the point, front-loaded with action, no wasted words.

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

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

Given simple single-parameter tool with no output schema, description covers purpose, usage, and relationships fully.

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 provides 100% coverage with description 'Memory key to delete'. Description adds no extra 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 uses specific verb 'Delete' and resource 'memory by key', clearly distinguishing it from siblings like 'remember' and 'recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data' and mentions pairing with 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 readOnlyHint and idempotentHint are true, and destructiveHint false. The description adds process details (fetching page, extracting info) and output format, but does not elaborate on error handling or rate limits. Still, 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 concise and well-structured: three sentences plus a bullet list of use cases. All information is relevant and front-loaded 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 sufficiently explains the output (single text blob in standard llms.txt markdown). It covers use cases and tool behavior, making it complete for a simple tool with two parameters.

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 parameters are already well-described. The description does not add additional meaning beyond what the schema provides for each parameter.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, listing its actions (fetch, extract title/description/key links) and output format. It distinguishes itself from sibling tools by being the only tool specifically for generating llms.txt files.

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 such as getting a client's site indexed, drafting for your own project, or auditing a competitor. It does not explicitly state when not to use this tool, but the context makes it 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 are minimal (no readOnly, idempotent, destructive hints). The description adds valuable behavioral details: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. This goes beyond what annotations provide.

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

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

The description is concise and well-structured: it starts with the main purpose, then gives usage guidelines, and ends with constraints (rate limit, quota). 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 feedback tool with 3 parameters (2 required, one nested object) and no output schema, the description fully covers what the agent needs: when to use, what to include, restrictions, and non-usage of quotas. 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?

The input schema has 100% coverage with descriptions for each parameter, including an enum for type. The description adds extra guidance: 'Be specific', '1-2 sentences typical, 2000 chars max', and 'Don't paste the end-user's prompt', which helps correct usage.

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

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

The description clearly states the tool is for providing feedback to the Pipeworx team, including specific types (bug, feature, data_gap, praise). It distinguishes itself from sibling tools by focusing on feedback rather than queries or 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 tells when to use the tool (bug, feature/data_gap, praise) and what not to do (don't paste the end-user's prompt). It also provides context about being free and not counting against 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 indicate safe, read-only behavior. Description adds data source (CF analytics-engine), privacy (no PII), and caching duration (5min-1h), providing behavioral context beyond structured fields.

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

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

Four sentences, front-loaded with purpose, no excessive detail. Every sentence adds value. 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?

For a simple tool with one param and no output schema, description covers purpose, usage, behavior, and result format completely. 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?

Single parameter 'window' fully documented in schema (100% coverage). Description adds semantic guidance on short vs. long windows, enhancing agent understanding beyond enum values.

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

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

Description clearly states the tool returns trending tools, packs, and call volume over time windows, using specific verbs and resource. It distinguishes from siblings like 'discover_tools' by emphasizing social/aggregate signal.

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

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

Explicitly lists three concrete use cases (discovering hot data, confirming canonical choice, assessing alignment). Does not provide explicit exclusions or alternatives, but context makes the tool's niche 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 indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by explaining the internal algorithm (e.g., monotonicity checks, partition sum, similarity threshold) and the response structure, giving agents a clear understanding of behavior without contradictions.

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

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

The description is comprehensive but somewhat verbose; however, it is well-structured with a clear introduction, mode breakdown, and technical details. It is front-loaded with the core purpose, and each sentence adds necessary information for a tool with complex logic.

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

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

Given the lack of an output schema, the description compensates by listing response fields (opportunities[], partition_check) and explaining the logic. It covers input, modes, filters, and outcomes, but could be slightly more explicit about edge cases or limitations.

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

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

The input schema has full coverage (100%) with descriptions for both parameters (event and topic). The description enhances this by adding extensive context, such as how each mode works, the search process, and the conditions for pairing. This goes beyond the schema's brief 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: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies two distinct modes (event and topic) and distinguishes itself from sibling tools (e.g., polymarket_edges, polymarket_kalshi_spread) by detailing its unique logic.

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 vs topic) with examples and explains that cross-event mode 'catches patterns that single-event misses.' It also describes filters like Jaccard similarity and partition filter, but does not explicitly compare to sibling tools or state when not to use.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavioral details: cached 1h at KV level, 'Market moved X.Xpp in 24h' warnings, Fed bet unreliability, and edge calculation components. No contradiction with annotations.

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

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

The description is verbose and dense, containing extensive detail in a single block. While informative, it lacks bullet points or structured sections that would aid quick scanning. Conciseness is sacrificed for 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?

Despite having no output schema and 9 parameters, the description covers all essential aspects: segments, edge components, knobs, caching, warnings, and diagnostics. An agent can reliably use this tool with the provided information.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how parameters like min_partition_leg_kelly interact and providing usage guidance for each knob, elevating it above a simple schema repeat.

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 and returns opportunities where Pipeworx data disagrees with market price, explicitly targeting discovery for agents. It distinguishes from siblings like polymarket_arbitrage by focusing on model-driven and structural 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 provides clear usage context for discovering betting opportunities and details knobs for filtering. However, it does not explicitly state when to use alternatives like polymarket_arbitrage or bet_research, leaving some room for confusion.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavioral context: response structure with leg-by-leg prices, matched spreads, safety fields (compatibility_warning, temporal_alignment), and skipped counters. It discloses when spreads are meaningless due to mismatched bet shapes or temporal gaps.

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 every sentence adds value. It is front-loaded with the main purpose and structured with two modes and safety fields. Slight lack of conciseness; could be more streamlined without losing 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?

No output schema exists, so the description must explain the response. It does so comprehensively: leg-by-leg prices, matched spreads, compatibility_warning, temporal_alignment, and skipped counters. For 3 parameters with 0 required, the description is complete.

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

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

Schema coverage is 100%. The description enhances understanding by explaining each parameter's role: topic as pre-mapped shortcuts, and override parameters for explicit pairings. It also provides examples 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It uses specific verbs and distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue comparisons.

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 or explicit tickers) and provides context on when each is appropriate. It warns that most pre-mapped topics return compatibility_warning and are not tradeable. However, it does not explicitly contrast with sibling tools or state when not to use this tool.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds a crucial constraint (10,000-cell limit) and clarifies the query object type, providing 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?

Two sentences, no wasted words, front-loaded with purpose and immediate constraint. 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?

The description covers the basics but is brief for a complex nested-query tool. No explanation of query construction, error handling, or pagination, though schema describes parameters sufficiently.

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 detailed descriptions. The description adds 'body is a PxWeb query object', which is helpful but does not significantly expand on the schema's provided format.

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 'Pull data' and resource 'table', and specifies the path format ending in '.px', distinguishing it from sibling tools like table_meta.

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

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

The description implies usage for pulling table data with .px paths and warns about a cell limit, but does not explicitly state when not to use or mention alternatives like table_meta for metadata.

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

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

Adds scoping detail (anonymous IP, BYO key hash, account ID) beyond annotations. Annotations already cover idempotency and non-destructiveness, so description enhances transparency.

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

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

Three focused sentences with no wasted words. Each sentence earns its place: purpose, usage, and pairing with siblings.

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 implies return value (retrieved value or list of keys). Could explicitly state output format, but simple tool reduces need.

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

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

Schema already describes key as omittable; description adds real-world examples of what keys may represent (ticker, address, notes), adding 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?

Clearly states it retrieves a saved value or lists all keys, with specific verb-resource pairing. References sibling tools remember and forget, distinguishing its role.

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

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

Gives explicit use cases (look up target ticker, address, research notes) and mentions scoping. Lacks explicit when-not-to-use, 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 mark readOnlyHint=true and destructiveHint=false. Description adds critical behavioral details: parallel fan-out to multiple sources, GDELT→GNews fallback on rate limits, USPTO soft-fail with future sunset date. 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 dense paragraph but well-organized with front-loaded purpose. Every sentence adds value. However, it could be slightly more concise without losing clarity.

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

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

Given no output schema, description fully explains return structure: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' Covers multiple data sources, fallbacks, and edge cases (API sunset). 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 coverage is 100%, so baseline is 3. Description enhances understanding by explaining since parameter format (ISO date or relative shorthand like '7d', '30d', '1y') and clarifying value can be ticker or CIK. Adds helpful examples 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's purpose: 'What's new with a company in the last N days/months?' and gives explicit use cases like 'what's happening with X', 'updates on Y'. It distinguishes from sibling tool entity_profile by specifying when to use each.

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 and when-not-to-use guidance: 'Use entity_profile instead when you want the static profile… regardless of window.' Also explains fallback behavior between GDELT and GNews, and soft-fail for USPTO. Gives concrete query examples.

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 idempotentHint=true and destructiveHint=false. The description adds valuable context: scoped by identifier, persistence differences for auth vs anonymous, and 24-hour retention for anonymous sessions. No contradictions.

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

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

Three sentences, each carrying weight: first states purpose, second gives use case, third explains scope and pairing. Front-loaded and efficient.

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

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

No output schema, but description explains what happens on write (stored, scoped, persistence). Complete for a simple key-value storage tool, especially with sibling references.

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 key and value. The description enriches by providing examples of key patterns (subject_property, target_ticker) and value types (findings, addresses), adding meaning beyond the schema alone.

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

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

Description starts with 'Save data the agent will need to reuse later', clearly stating the verb (save) and resource (data for reuse). It distinguishes from sibling tools recall and forget, specifying the tool's role in the memory lifecycle.

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

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

Provides explicit usage context: 'when you discover something worth carrying forward' and pairs with recall/forget. Lacks explicit negative guidance but still effectively directs when to use.

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

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

Annotations already declare idempotent and read-only. Description adds internal cascading lookups, auto-disambiguation, and detailed return fields for each type, providing valuable context beyond annotations.

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

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

The description is well-structured with a clear purpose statement, usage guideline, and type-specific details. It is informative without being verbose, though slightly long for some scenarios.

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 comprehensively details return values for both entity types, including identifiers and citation URIs. It also explains internal cascading, making the tool's behavior fully understandable.

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

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

Schema covers 100% of parameters with descriptions. The description adds examples (e.g., 'AAPL', 'ozempic') and explains auto-disambiguation and return mapping for each 'type', enhancing 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 clearly states the tool resolves user-spoken names to canonical identifiers, with a specific verb 'Resolve' and specific resource 'entity name'. It distinguishes itself from siblings like 'entity_profile' and 'compare_entities' by focusing on ID lookup.

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

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

Explicitly says 'Use FIRST when you have a name but need an ID', providing clear when-to-use guidance. It also mentions efficiency gains. No explicit when-not-to-use, 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?

Beyond annotations (readOnlyHint, idempotentHint), the description details the internal behavior: probes each entity with ai_visibility_check, ranks results, and returns score, confidence, signal density. 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 (3 sentences) and front-loaded with the core purpose. Every sentence contributes value: purpose, mechanism, example use case, and output 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?

With no output schema, the description adequately describes the output (ranked list with metrics). It covers purpose, input requirements, and behavior. Minor omission: no mention of error handling or prerequisites for anthropic model, 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%, but the description adds meaningful context: groups entities into subject vs. competitors, clarifies default model (workers-ai) and optional nature of models and _apiKey. This adds value beyond the schema.

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

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

The description clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools (e.g., ai_visibility_check for single entity) and includes a concrete use case.

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

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

The description provides clear context for when to use this tool (competitive AI-marketing audits) and gives an example question. However, it does not explicitly state when not to use it or mention alternative tools among siblings.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable behavioral details beyond annotations: it fans out across two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. 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 long but each sentence adds value. It front-loads the composite purpose and then details the fan-out, usage guidance, and error handling. Could be slightly trimmed, but overall efficient.

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

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

Given the tool's complexity (composite with two external services, partial failures, timeout behavior), the description covers all key aspects: ecosystem scope, return fields, graceful degradation, and alternatives. No output schema exists, but the description explains the return structure sufficiently.

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 both parameters are already documented in the input schema. The description redundantly mentions scoped packages and version defaults, but does not add significant new meaning. 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's composite purpose: a single call to check npm packages via deps.dev and bundlephobia. It specifies the return fields and distinguishes from siblings by being the only tool that aggregates license, advisory, bundle size, etc., while other tools like scan_competitor_ai_presence serve a different domain.

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

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

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and provides alternatives for non-NPM ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This gives clear when-to-use and when-not-to-use guidance.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds minimal behavioral context beyond 'navigate' and 'list', which is already implied. 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?

The description is two sentences, front-loaded, and contains no filler. Every sentence adds value, making it highly concise and well-structured.

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

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

Given the tool's simplicity (single parameter, no output schema, strong annotations), the description is complete. It provides enough information for an agent to understand the tree navigation and 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 description coverage is 100% with a clear explanation of the 'path' parameter. The description adds extra meaning by explaining the tree structure ('Root lists databases') and the default starting point, enhancing understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Navigate the subject tree' and specifies that it lists databases starting at 'ASKdata'. It is distinct from sibling tools which cover other domains like betting, entities, or queries.

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

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

The description provides an example path and suggests starting at 'ASKdata', but it does not explicitly specify when to use this tool versus alternatives or when not to use it. It lacks explicit usage context 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 declare read-only, idempotent, and non-destructive. The description adds that the path must end with '.px' and hints at output content (dimensions, valid values), which is useful beyond the structured annotations.

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

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

Two sentences, no fluff. Front-loaded with the primary purpose, then a critical constraint. Every word 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 no output schema, the description hints at what is returned (dimensions, valid values). It covers the input constraint and general purpose adequately for a simple metadata tool with strong 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?

With 100% schema coverage, baseline is 3. The description adds the constraint that path must end with '.px', which is not in the schema description (which only gives an example). This adds meaningful guidance.

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

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

The description clearly states it returns table metadata (dimensions, valid values). It distinguishes from siblings like query_table (which returns data) and discover_tools (which lists tools). The constraint 'Path must end in .px' 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?

No explicit guidance on when to use this tool vs alternatives. The purpose is implied as a prerequisite for understanding table structure before querying, but no exclusions or when-not-to-use are given.

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, idempotent, and non-destructive. The description adds valuable behavioral context: returns a verdict with natural language, actual value with citation, percent delta, and replaces multiple sequential calls.

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

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

Description is concise and well-structured: starts with purpose, then usage, scope, output, and benefit. 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 the tool's complexity and lack of output schema, the description covers purpose, usage, scope, output format, and efficiency advantage. It is complete enough for correct selection and invocation.

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

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

Schema covers the only parameter 'claim' 100%. The description provides examples and clarifies that it expects a natural-language factual claim, adding value beyond the schema.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically for company-financial claims via SEC EDGAR. It distinguishes from siblings by specifying the domain and functionality.

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

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

Explicitly says 'Use when an agent needs to check whether something a user said is true' with example queries. It defines scope (company-financial claims) but doesn't mention when not to use or alternative tools.

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