Datagov Au

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

Annotations indicate it's read-only and idempotent. The description adds behavioral details like return format and cost implication for Anthropic, 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 concise, front-loaded with the purpose, and each sentence adds value. No redundant or vague phrasing.

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

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

Covers main purpose, parameters, return structure, and optional API key usage. Could mention error handling or limits, but overall sufficient for a probing 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 each parameter is documented. The description adds minor usage context (e.g., API key handling) but does not significantly extend 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 probes LLMs to score visibility per model, specifying the verb, resource, and outcome. It distinguishes from siblings by focusing on AI visibility scoring.

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

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

The description explains default vs. optional model usage and provides context for typical use cases (marketing audits, brand checks). It does not explicitly 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.

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

Annotations (readOnlyHint, openWorldHint, destructiveHint) are complemented by description detailing routing mechanics, argument filling, and citation URI return. 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 well-structured: starts with key directive, lists use cases, explains mechanics, gives examples. 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?

With one parameter, rich annotations, and no output schema, the description fully covers what an agent needs: purpose, usage, inputs, and expected output format (citations).

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 'question' parameter is fully described in schema. Description adds context with examples of valid questions, going beyond the basic schema 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?

Description clearly states the tool's purpose: routing questions to over 1,423 tools across 392+ verified sources for factual answers. It provides specific examples and distinguishes from web search with a clear preference directive.

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

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

Explicitly tells when to use ('PREFER OVER WEB SEARCH') and lists numerous example queries and patterns ('what is', 'look up', etc.). Lacks explicit when-not-to-use instructions but provides strong positive 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 indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description aligns by describing a read-only data aggregation operation. It adds behavioral details: resolving market inputs, classifying bet types, and intelligently selecting data packs. These details go beyond annotations to clarify internal logic and data sources.

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 dense paragraph with 4-5 sentences. It front-loads the main action but becomes verbose with detailed explanations of fan-outs and comparisons. Some sentences are long and could be broken up for readability, making the description slightly less concise than ideal.

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 output schema, the description adequately explains the return (evidence packet and market-vs-model comparison). It covers the tool's core functionality and its value as a demo product. However, it does not address error handling, edge cases (e.g., unresolvable market), or performance implications, leaving minor 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?

With 100% schema coverage, the description adds marginal value beyond the schema. It clarifies that 'market' can be a slug, URL, or question text, and explains 'depth' options (quick vs thorough) with a default. While helpful, it doesn't significantly enhance understanding beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: researching a Polymarket bet by pulling Pipeworx data. It details the process (resolving market, classifying bet, fanning out to packs) and distinguishes it from general data querying tools. The verb 'research' paired with specific resources (Polymarket bet, Pipeworx data) makes the function unmistakable.

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 'should I bet on X?', 'what does the data say about this Polymarket market?', and 'is there edge in this bet?'. It contrasts with agents that would need to discover packs themselves, implying this tool automates that. However, it does not explicitly state when not to use the tool or list alternative tools for other scenarios.

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

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

Annotations indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds specific data sources (SEC EDGAR/XBRL, FAERS, FDA) and output format (paired data + citation URIs), providing rich behavioral 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 sentences with front-loaded purpose. Mildly verbose with 'Replaces 8–15 sequential agent calls' but that adds utility. Could tighten phrasing slightly, but overall efficient.

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

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

Given no output schema, description adequately explains return format (paired data + URIs), sources, and behavior for both entity types. Covers input constraints (2-5 items) and use cases comprehensively.

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

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

Schema has 100% coverage but description adds value: explains enum values with examples, clarifies that values are tickers for company and names for drug, and details what data fields are retrieved for each type (revenue, adverse events, etc.).

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 2-5 companies or drugs side by side. It uses specific verbs ('compare', 'stack up') and distinguishes from sibling tools like entity_profile which handles single 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?

Provides explicit user phrases triggering use ('compare X and Y', 'X vs Y', 'how do X, Y, Z stack up'), and contrasts with sequential agent calls it replaces. Also differentiates between company and drug scenarios.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, making safety clear. The description adds that it returns 'the top-N most relevant tools with names + descriptions' and advises calling first, providing behavioral context beyond the annotations.

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

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

The description is a single paragraph that front-loads the purpose and includes a list of examples. While comprehensive, the list is somewhat lengthy; a slightly more concise version could improve readability without losing information.

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

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

For a simple search tool with complete schema descriptions and safety annotations, the description fully covers what the tool does, when to use it, and what it returns (top-N tools with names+descriptions). No output schema exists, but the description hints at the return format 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?

Both parameters are described in the input schema (100% coverage), so the description adds no new parameter semantics. It provides example queries like 'analyze housing market trends', which match the schema's description but don't extend it.

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 'Find tools by describing the data or task' and provides a comprehensive list of use cases (SEC filings, financials, FDA drugs, etc.). It clearly distinguishes from sibling tools by positioning itself as a 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 states 'Use when you need to browse, search, look up, or discover what tools exist' and instructs 'Call this FIRST when you have many tools available'. Lacks explicit when-not to use, but the context strongly implies it's for exploration before other tools.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds that results include citation URIs and aggregates from multiple sources, but does not detail pagination or result limits. No contradiction.

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

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

Single paragraph packs purpose, use cases, return values, and parameter notes. Efficient, though could be slightly more concise. Still well-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?

No output schema, so description covers return values (SEC filings, fundamentals, patents, news, LEI). For a complex aggregation tool, it provides a solid overview, though detail on result format is absent.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds context: type is only 'company' for now, value accepts ticker or CIK, and names require resolve_entity first, which goes 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 states 'Get everything about a company in one call' and enumerates specific resources (SEC filings, fundamentals, patents, news, LEI). This clearly distinguishes it from siblings like compare_entities and resolve_entity.

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

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

Explicitly says 'Use when a user asks...' with concrete query examples, and advises using resolve_entity if only a name is available. This provides clear when-to-use and when-not-to-use guidance.

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

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

While annotations already have destructiveHint true, the description adds context about why to delete (stale context, sensitive data) and pairs with related tools, but doesn't detail irreversible effects beyond the annotation.

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. First sentence states the function, second gives usage guidelines. Efficient and well-structured.

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

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

For a simple tool with one required parameter and no output schema, the description provides purpose, usage guidance, and sibling context. It is complete and sufficient for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% with a clear description for the key parameter. The tool description adds 'by key' but no extra detail beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key,' which is a specific verb and resource. It distinguishes from siblings 'remember' and 'recall' by being the delete operation.

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 provides when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember' and 'recall,' giving context for usage.

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, open-world, and idempotent behavior. The description adds behavioral details: fetches the page, extracts title/description/key links, and emits standard markdown format. 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 a single paragraph of three sentences, each adding value. Front-loaded with the main purpose, no wasted words.

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

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

Given the low complexity (2 params, no output schema) and rich annotations, the description provides sufficient context: output format, use cases, and behavior. No gaps.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description does not add significant extra meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a given URL, specifying the verb 'generate' and the resource 'llms.txt file'. It distinguishes itself from sibling tools by focusing on a specific AI indexing standard.

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 lists three use cases: getting a client's site indexed, drafting for own project, or auditing competitor visibility. It provides clear context for when to use the tool, though it does not mention when not to use or alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds no behavioral context beyond a listing operation. No disclosure of pagination, rate limits, or other behavioral traits.

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?

Extremely concise at 3 words, but under-specification reduces value. Not enough substance to be considered appropriately sized.

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 simplicity (one optional param, no output schema), description is minimally complete but lacks explanation of 'themes' context, parameter behavior, and relationship to 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?

Single parameter 'limit' has no description in schema (0% coverage) and description does not explain its purpose or format. Fails to compensate for schema absence.

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 states the verb 'List' and resource 'themes (groups)', indicating a specific operation. However, it does not differentiate from sibling tools like 'search', which might also return themes. Lacks explicit sibling differentiation, so not a 5.

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

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

No guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or comparative context. Single sentence lacks usage 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 readonly and non-destructive behavior. The description adds no behavioral details beyond this, so it meets the baseline but does not exceed it.

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

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

Single sentence, no wasted words. Perfectly concise for the information it conveys.

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 list tool with no output schema, the description could mention what fields are returned (e.g., organization names and IDs). Currently it only implies a list but lacks specifics.

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 minimal description for the 'limit' parameter. The tool description adds no additional meaning beyond what the schema provides.

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

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

The description 'List publishing organizations' clearly states the action (list) and the resource (publishing organizations), but does not differentiate from sibling tools like 'groups' or 'resource'.

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

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

No guidance on when to use this tool versus alternatives. There is no context about prerequisites, scope (e.g., current workspace), or typical use cases.

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

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

Annotations already declare readOnlyHint true and destructiveHint false, so the description does not need to repeat that. The description adds that the tool retrieves by id or name, but does not disclose further behavioral traits such as error handling, pagination, or limitations. Since annotations cover the safety profile, a score of 3 is appropriate.

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

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

The description is extremely concise (5 words) and front-loaded with essential information. Every word earns its place, with no redundancy or fluff.

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

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

Given the tool's simplicity (one parameter, no output schema, clear annotations), the description is minimally adequate. However, it omits details like return format, error scenarios, and whether the 'name' lookup is supported (given the schema only has 'id'). More completeness would improve 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?

With schema description coverage at 0%, the description must add meaning. It suggests the 'id' parameter can accept either an id or a name, which adds context beyond the schema's type definition. However, this is vague and could confuse agents expecting a strict id format. The description partially compensates but leaves ambiguity.

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

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

The description clearly states the tool returns a single package or dataset by identifier or name, which is specific and distinct from siblings like 'resource' or 'search'. However, it lacks an explicit verb (e.g., 'get', 'retrieve'), which slightly reduces clarity.

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

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

No guidance is provided on when to use this tool versus alternatives like 'resource' or 'search'. There is no mention of prerequisites, context, or when not to use it, leaving the agent without decision support.

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 limit ('5 per identifier per day'), no quota consumption, free to use, and that feedback directly affects roadmap. Annotations provide readOnlyHint=false and destructiveHint=false, which the description does not contradict.

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

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

The description is a single, well-structured paragraph that front-loads the core purpose, then covers categories, usage guidance, and constraints. Every sentence adds value without redundancy. It is concise yet comprehensive.

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

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

Given the tool's simplicity and absence of output schema, the description is complete. It covers what the tool does, when to use it, how to structure feedback, important constraints (rate limit, quota), and the impact of the feedback. No additional context is needed for effective 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?

With 100% schema coverage, the baseline is 3. The description adds value by explaining the categories in plain language and providing examples for the 'message' field (e.g., 'Be specific (which tool, what error, what data was missing)') and the length constraint (2000 chars max). It does not repeat schema but enriches understanding.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It provides explicit categories (bug, feature, data_gap, praise) and explains the intent behind each, making it distinct from sibling tools like ask_pipeworx or discover_tools.

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

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

The description gives detailed when-to-use scenarios for each feedback type and includes a clear instruction on what not to do ('don't paste the end-user's prompt'). While it implicitly distinguishes from siblings, it could be stronger by explicitly naming alternatives for related tasks (e.g., use discover_tools to find existing tools).

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

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

The description goes beyond annotations by explaining the data source (CF analytics-engine), the no-PII nature, and caching behavior (5min-1h). This provides critical context that annotations alone do not cover, such as staleness and privacy.

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, front-loaded with the core purpose, and each sentence serves a distinct role: stating output, listing use cases, explaining data provenance. There is no waste or 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?

The description explains the return format adequately (pack, tool, count) and caching, which compensates for the lack of an output schema. The tool is simple (one parameter, no nested objects), so the description is sufficiently complete for an agent to understand what to expect.

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

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

The single parameter 'window' has a schema description that lists options, but the description adds strategic guidance ('shorter windows surface what's hot right now; longer windows show steady-state demand'). This adds value beyond the schema's enum listing.

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: listing top tools and packs being called on Pipeworx with call volume over time windows. It uses specific verbs and resources, and the scope is well-defined, distinguishing it from siblings like compare_entities or discover_tools.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming canonical choice, alignment checking), which guide the agent effectively. However, it does not explicitly mention when not to use the tool or suggest alternatives, though the context makes the intended usage 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 read-only and non-destructive behavior. The description adds value by detailing the process: walking child markets, extracting dates/thresholds, and returning specific trade suggestions. No behavioral 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 six sentences, with each sentence contributing necessary explanation. It front-loads the core purpose and provides a clear logical flow. Minor verbosity could be trimmed, but it remains efficient.

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

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

Given the simple input and lack of output schema, the description fully covers the tool's behavior, output structure, and usage context. No gaps remain for an agent to make an informed decision.

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

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

The single 'event' parameter is well-documented in the input schema. The description adds context about its usage (slug or URL) and how it drives the tool's logic, going 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 finds arbitrage opportunities via monotonicity violations within a Polymarket event, with a concrete example. It distinguishes itself from siblings like bet_research or polymarket_edges by specifying a unique detection mechanism.

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

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

The description explains when to use the tool (when an event has multiple date/threshold markets) and provides the input format. It does not explicitly list when not to use or point to alternatives, but the context is sufficiently clear for an agent.

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

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

Description adds substantial behavioral context beyond annotations: it explains the V1 scope, models used (lognormal from FRED, live coinpaprika price), scanning logic, grouping by asset, single-fetch price history, and ranking by edge magnitude with suggested direction. No annotation 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 slightly long but well-structured with the purpose front-loaded. Each sentence adds value, but some detail (e.g., specific data sources) could be condensed without losing clarity.

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

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

Given the tool's complexity, annotations, and absence of an output schema, the description covers the algorithm, data sources, and output ranking. It does not detail exact output format but provides sufficient context for an agent to understand behavior.

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

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

Input schema coverage is 100%, with descriptions already providing defaults and constraints for all three parameters. The description does not add additional parameter-level details beyond reinforcing the defaults, so baseline 3 is appropriate.

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

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

The description clearly states it scans Polymarket markets, computes disagreement with Pipeworx data, and returns ranked edges. It distinguishes from sibling tools like 'polymarket_arbitrage' by focusing on edge magnitude for betting opportunities.

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 frames usage for the 'what should I bet on today' question, providing clear context. It does not explicitly state when not to use or list alternatives, but the context is sufficient for an agent to infer appropriate usage.

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, non-destructive. The description adds value by describing output format (leg-by-leg prices, spreads in percentage points) and typical spread range (2–25pp). No contradictions.

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

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

The description is front-loaded with purpose and modes, then details output. At ~150 words it is reasonably concise, though could be slightly tighter; no extraneous information.

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

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

Given no output schema, the description adequately describes the return structure. It covers typical use cases and modes, though it does not explicitly explain behavior when conflicting parameters are provided or mention data freshness.

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 covers all three parameters with descriptions (100% coverage). The description adds significant context by explaining the interplay between topic and explicit tickers, defining two operational modes and how parameters override 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?

The description clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question, with two modes (topic shortcuts and explicit tickers). This differentiates it from sibling tools like polymarket_arbitrage which focus on internal 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 explains when to use it: to capture arbitrage signals from price differences between venues. It details two modes explicitly, but does not provide when-not-to-use or compare to alternatives like other arbitrage tools.

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

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

Annotations already indicate read-only and non-destructive behavior; description adds scoping to user identifier and behavior when key is omitted. 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?

Concise with front-loaded action and context, though slightly verbose with usage examples. 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 low complexity (1 optional param, no output schema, good annotations), the description covers purpose, usage, scoping, and pairing 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 covers the single parameter with 100% coverage. Description adds usage examples but no new semantic meaning beyond the schema.

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

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

The description clearly states it retrieves values saved via 'remember' or lists all keys when omitted, and distinguishes itself from sibling tools like 'remember' and 'forget'.

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

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

It explains when to use (looking up stored context) and implies not to re-derive from scratch. It also mentions pairing with remember/forget, though no explicit when-not.

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 value beyond annotations by detailing the tool's inner workings: it fans out to SEC EDGAR, GDELT, and USPTO in parallel, and returns structured changes with counts and URIs. Annotations already indicate read-only and non-destructive behavior, so the description complements them well.

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

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

The description is a single, information-dense paragraph that front-loads the purpose and usage examples. It avoids redundancy and every sentence adds value. It could be slightly more structured (e.g., bullet points for output), but it remains efficient.

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

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

Given the absence of an output schema, the description adequately covers return values (structured changes, total_changes count, citations). It explains the fan-out behavior and parameter formats, providing sufficient context for an AI agent to use the tool correctly for monitoring tasks.

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 meaning by explaining the 'since' parameter's accepted formats (ISO date or relative shorthand) and recommending a default ('30d' or '1m'). The 'type' and 'value' parameters are clearly described in the schema, so the description's extras are helpful but not essential.

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

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

The description clearly states the tool's purpose: finding recent changes for a company over a time window. It provides example queries, making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like entity_profile or search, though the usage examples imply a specific 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 includes explicit 'Use when...' statements with concrete examples of user queries, guiding when to invoke the tool. It also explains the fan-out behavior to multiple sources. However, it does not mention when not to use it or contrast with alternatives, which would improve clarity.

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

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

The description adds significant behavioral context beyond annotations, including scoping by agent identifier, persistence details (authenticated vs. anonymous sessions with 24-hour retention), and key-value pair storage. Annotations (readOnlyHint=false, destructiveHint=false) are consistent with this description.

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, consisting of three sentences with no wasted words. It front-loads the primary purpose and effectively uses a bullet-like structure (via dashes) to list examples, making it easy 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 simplicity (2 simple params, no output schema), the description covers all necessary aspects: what it does, when to use, persistence behavior, scope, and companion tools. It provides sufficient context for an agent to decide and execute 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?

Input schema already provides 100% coverage with descriptions for both parameters ('key' and 'value'). The description adds value by giving concrete examples of key usage (e.g., 'subject_property', 'target_ticker'), but primarily reinforces rather than extends schema information.

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

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

The description clearly defines the tool's purpose with a specific verb ('Save') and resource ('data'), and distinguishes it from sibling tools ('recall', 'forget') by explicitly stating the use case of carrying forward information across conversations or sessions. Examples like 'resolved ticker, target address, user preference' further clarify scope.

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

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

The description provides clear guidance on when to use the tool ('when you discover something worth carrying forward') and references complementary tools ('recall', 'forget') for retrieval and deletion. However, it lacks explicit 'when not to use' instructions, which would be beneficial for an agent to avoid misuse.

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

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

Description adds value beyond annotations by specifying return format (IDs plus pipeworx:// citation URIs) and efficiency ('replaces 2–3 lookup calls'). Annotations already declare readOnlyHint=true and destructiveHint=false, so no contradictions arise. Some minor omissions (e.g., error handling) but overall transparent.

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 moderately concise with front-loaded purpose. Every sentence adds value, though it could be slightly more structured (e.g., bullet points). Overall efficient communication of key information.

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

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

Given the tool's simplicity (2 params, no output schema), the description adequately covers purpose, usage, and output. It mentions ID systems and use cases. Minor gap: no indication of behavior on no match. Nonetheless, sufficient for an AI agent.

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 params. The description further enriches with concrete examples (e.g., 'Apple' → AAPL, 'Ozempic' → RxCUI 1991306) and clarifies input formats (CIK as string). This adds meaningful context beyond the schema.

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

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

Description clearly states the tool's purpose: looking up canonical/official identifiers for companies or drugs. It lists specific ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples, distinguishing it from sibling tools like entity_profile or search.

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

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

Explicitly tells when to use ('when a user mentions a name and you need the CIK...'), and instructs to use BEFORE other tools that need official identifiers. Also notes it replaces multiple lookup calls, providing clear context for appropriate invocation.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safe read nature is clear. The description adds that the resource is a 'downloadable file', providing behavioral context beyond annotations. However, no further traits (e.g., file format, size limits) are disclosed.

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

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

The description is extremely concise—one short sentence with no unnecessary words. Every word adds value, and it is front-loaded with the key information.

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

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

Given the low complexity (one parameter, no output schema, simple read operation), the description provides the essential purpose. However, it could be more complete by specifying what is returned (e.g., file content, metadata) or potential constraints (e.g., file size).

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 one parameter 'id' with 0% description coverage. The description simply repeats 'by id' without explaining what kind of ID (e.g., string format, source). This adds minimal meaning beyond the parameter name.

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

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

The description states 'Single resource (downloadable file) by id.', clearly indicating the tool retrieves a specific resource by ID. The verb is implied (get/fetch) and the resource type is specified, distinguishing it from siblings like 'search' which returns lists.

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 usage guidelines are provided. The description does not mention when to use this tool vs alternatives (e.g., 'search' or 'package'), nor any prerequisites or limitations.

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 non-destructive behavior. The description adds context: it probes each entity with ai_visibility_check, treats first entity as subject for narrative, and returns rank, score, confidence, and signal density. This goes beyond annotations by detailing the process and output shape.

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

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

The description is four sentences, each serving a purpose: stating the main function, explaining the process, giving a use case, and describing the output. It is front-loaded with the core purpose and avoids unnecessary 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 the tool's complexity (comparing multiple entities via sub-tool calls) and lacking an output schema, the description adequately explains the return structure (ranked list with score, confidence, signal density per entity). It also covers parameter dependencies (e.g., _apiKey needed only for Anthropic) and the special role of the first entity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the meaning of 'entities' (first is subject), the conditional requirement for '_apiKey', and the role of 'context' for disambiguation. This enriches the parameter understanding beyond schema descriptions.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, specifies it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also provides an example use case ('does Claude know about us as well as our competitors?'). This distinguishes it from the sibling tool ai_visibility_check, which is for single entity checks.

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 a concrete use case ('competitive AI-marketing audits') and an example question. It implicitly differentiates from ai_visibility_check by describing this as a multi-entity comparison. However, it does not explicitly state when not to use or compare to other siblings like compare_entities, leaving room for ambiguity.

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

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

Annotations already declare readOnlyHint and idempotentHint. The description adds detailed behavioral context: composite fan-out across two sources, partial failure handling with graceful degradation, bundlephobia first measurement latency (5-30s), and a 'sources_failed' field on timeout. 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 front-loaded with the main purpose, then use cases, return format, limitations, and fallback behavior. Every sentence adds value, though it is slightly verbose for a tool description. Could be slightly trimmed 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 the tool's complexity (composite, two sources, partial failures, timeouts), the description covers all essential aspects: what it does, what it returns (summary block, advisories, links, alternative versions), limitations (NPM only, bundlephobia latency), and error handling (sources_failed). No output schema exists, so the description compensates 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%, so baseline is 3. The description reiterates that 'package' is an npm package name and 'version' defaults to latest, but does not add substantial meaning beyond the schema. It is consistent but not enhanced.

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 evaluating an npm package before adding it, covering license, advisories, version history, and bundle size. It uses specific verbs like 'fans out' and 'returns summary block', and distinguishes from siblings by specifying NPM ecosystem only.

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

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

The description explicitly states when to use the tool: whenever an agent asks about safety, popularity, or size of an npm package. It also notes that other ecosystems are not supported in v1 and suggests deps.dev:version directly as an alternative, providing clear usage boundaries.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds no behavioral information beyond these annotations, such as whether results are paginated, any authentication requirements, or side effects.

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

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

Single sentence is concise, but lacks structure or front-loading. It fits as a one-liner, but for a tool with 5 parameters and complex search behavior, more structure would be beneficial.

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?

Context is limited to 'CKAN package_search' without explaining what fields are searched, how sorting/filtering work, or what the return format is. No output schema exists, so description should clarify output nature.

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

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

Input schema has 100% parameter description coverage, so baseline is 3. The description does not add any parameter-level information beyond what the schema provides, thus no improvement.

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 'CKAN package_search' is nearly a tautology of the tool name 'search'. It specifies the domain (CKAN packages) but lacks a verb or clear purpose statement (e.g., 'Search CKAN datasets'). It does not distinguish from sibling tools like 'package' or 'resource'.

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

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

No guidance on when to use this tool versus alternatives. Sibling tools include specific entities (e.g., 'package', 'resource', 'organizations'), but description provides no context about when to choose 'search' instead.

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 and destructiveHint=false, so the description adds no further behavioral context. While the safety profile is clear, no additional traits (e.g., pagination) are described.

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 concise sentence, front-loading the purpose. It is efficient, though slightly more detail could improve value without significant bloat.

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 is simple with two optional parameters and read-only annotations, the description is minimally adequate. However, it lacks details on return format or behavior, which could be helpful.

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

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

Schema description coverage is 0%, and the description does not explain the meaning of 'limit' or 'query.' The verb 'search' implies query is a search term, but this is not explicit.

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

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

The description states 'List or search tags,' which clearly identifies the verb (list or search) and resource (tags). However, it does not differentiate from sibling tools like 'search,' which might have overlapping 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of context or exclusions, leaving the agent without direction.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by describing the return format (verdict, actual value with citation, percent delta) and noting it replaces multiple calls. No behavioral contradictions; the description enriches the annotation 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 concise (5 sentences), well-structured, and front-loaded with purpose. Every sentence adds value: usage guidance, scope, return details, and benefits. 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?

The tool has one required parameter and no output schema, but the description details the return fields (verdict, extracted form, actual value, citation, percent delta). It covers the core behavior and output, though edge cases or error handling are not mentioned.

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

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

Schema coverage is 100%, so the schema fully documents the 'claim' parameter. The description adds examples of natural-language claims but does not significantly exceed the schema's explanation. 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 action (fact-check, verify, validate) and resource (natural-language factual claim). It specifies the supported domain (company-financial claims via SEC EDGAR + XBRL) and contrasts with siblings by noting it replaces multiple sequential calls.

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

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

The description explicitly advises when to use the tool ('Use when an agent needs to check whether something a user said is true') and gives concrete examples. However, it does not explicitly state when not to use it, though the support scope is implied by the version note.

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