Kitsu

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safe, read-only nature is clear. The description adds no new behavioral context (e.g., error handling on invalid ID, rate limits). With annotations present, the bar is lower, but additional info would help.

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 words is very concise, but it sacrifices clarity and completeness. Every word earns its place, but the tool name 'anime' already implies the subject. A bit more context would improve without becoming verbose.

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

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

Given one parameter, no output schema, and annotations, the description is minimally adequate but does not mention what the tool returns (anime object) or error behavior (e.g., 404). For a retrieval tool, this is incomplete.

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

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

The sole parameter 'id' lacks description in the schema (0% coverage). The description only mentions 'by id' without specifying format, source, or example. This adds minimal value beyond the schema's existence of a string 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 'Anime entry by id' clearly indicates that the tool retrieves a specific anime resource by its ID. It distinguishes from sibling tools like search_anime (search) and top_anime (list) by implying a direct lookup. However, lacking a verb like 'Get' makes it slightly less explicit.

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 like search_anime for fuzzy lookup or top_anime for listings. The description does not mention prerequisites, such as knowing a valid ID, nor does it suggest scenarios for 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 indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context by explaining the routing mechanism (chooses from 1,423+ tools) and output format (structured answer with stable pipeworx:// citation URIs). This is valuable additional transparency beyond annotations.

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

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

The description is informative and front-loaded with the priority statement. It is well-structured with clear sections for scope and examples. While a few sentences could be trimmed, it remains concise and every sentence adds value.

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

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

Given the tool's simplicity (one parameter, no output schema), the description provides sufficient context: its purpose, when to use, and examples. It doesn't detail return structure beyond citations, but this is acceptable for a general query 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?

The input schema has one parameter 'question' with a clear natural language description. Schema coverage is 100%, so the description adds little beyond examples of acceptable questions. Baseline 3 is appropriate given that the schema already adequately defines the 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 routes questions to appropriate verified sources and returns structured answers with citations. It explicitly positions itself as a preferred alternative to web search for factual queries, distinguishing it from sibling tools like anime or manga which are domain-specific.

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

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

The description gives explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' and lists specific domains and example queries. It also provides linguistic cues like 'what is', 'look up', etc., and examples to reinforce usage. This helps an agent decide when to invoke this tool over others.

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

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

Annotations (readOnlyHint, openWorldHint, destructiveHint) are consistent. Description adds behavioral details: resolves market, classifies bet, fans out to appropriate data packs, and returns comparison. No contradictions; full transparency on what the tool does.

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 verbose but front-loaded with core purpose. Each sentence adds value (input types, classification, fan-out, output). Could be shortened slightly, but overall 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?

No output schema, but description adequately explains output (evidence packet + comparison). Covers input flexibility and tool's role as a composite research tool. Sufficient for an AI agent to understand what it returns without examples.

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%. Both parameters ('market', 'depth') have clear descriptions in schema. Description adds context about 'market' accepting slug, URL, or question text, and 'depth' with quick/thorough options, enhancing understanding beyond schema.

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

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

Description clearly states the tool researches Polymarket bets by pulling relevant Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet plus market-vs-model comparison). This is distinct from siblings like ask_pipeworx or validate_claim, positioning it as a core demo product.

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 use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. While it doesn't state when not to use, the context is clear and sufficient for an AI agent to understand appropriate scenarios. No direct alternative siblings exist.

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 annotations already declare this tool as read-only and non-destructive. The description adds no additional behavioral context (e.g., pagination, output format). Given strong annotations, the description is adequate but not additive.

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

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

The description is a single, front-loaded sentence with no fluff. It conveys the essential purpose efficiently, though it could be slightly more informative.

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

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

For a simple list tool with one optional parameter and no output schema, the description is reasonably complete. It clarifies the domain of 'genres/themes', which is helpful, though it omits details like ordering or default limit.

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 'limit' with 0% schema description coverage. The description does not mention 'limit' or its semantics, failing to compensate for the lack of schema documentation. The parameter's meaning remains ambiguous.

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 'List categories' with a clarifying parenthesis '(genres / themes)', making the action and resource clear. While it does not explicitly differentiate from sibling tools, none of the siblings directly overlap, so the purpose is well-understood.

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, nor any prerequisites or context. The description is minimal and does not help the agent decide between this and 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 provide readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and return format (paired data + citation URIs), providing rich 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?

A single paragraph that starts with the core purpose, then provides clear usage triggers, data details, and a value proposition. No superfluous sentences; every line earns its place.

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

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

Given the tool's complexity (two entity types, multiple data sources, replaces many calls) and good annotations + schema, the description covers major behavioral aspects. Missing details like rate limits or pagination, but the output is described (paired data + URIs) and no output schema exists.

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 reinforces usage examples (e.g., tickers vs drug names) and explains what each 'type' retrieves, adding value by clarifying expected input formats and data scope.

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

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

Description clearly states it compares 2-5 companies or drugs side by side, with specific verb+resource. Unlike siblings such as entity_profile (single entity) or search tools, this tool is uniquely positioned for multi-entity comparison tasks.

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 user intents that trigger usage ('compare X and Y', 'X vs Y', etc.) and examples. Could be improved by contrasting with alternatives like entity_profile for single entities, but the guidance is clear and actionable.

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 safety is covered. Description adds that it returns top-N most relevant tools with names+descriptions, which is useful behavioral context. No contradictions. Could mention edge cases like empty results, but overall good.

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, no fluff. Front-loaded with purpose and examples. Every sentence adds value. Excellent 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 complexity is low (two simple parameters), schema richness covers parameters, annotations present, and no output schema, the description is nearly complete. It explains what the tool returns (names+descriptions) and when to use it. Slight gap: doesn't mention whether tool IDs or other metadata are returned, but sufficient for a discovery tool.

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

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

Schema description coverage is 100%: both query and limit have clear descriptions. The description reinforces that query is natural language and limit controls number but does not add new semantic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool finds tools by describing data or task, listing specific domains (SEC filings, financials, FDA drugs, etc.). The verb 'find tools' and resource 'tools by describing data/task' are specific and distinguish it from sibling tools that serve individual domains.

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

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

Explicit guidance to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)'. This tells when to use it and implies when not to (if you already know the tool). It also lists example queries, helping the agent understand 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 declare readOnlyHint, openWorldHint, destructiveHint. The description adds detail: returns specific data types (SEC filings, financials, patents, news, LEI) and mentions citation URIs. No contradiction.

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

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

The description is a single paragraph but is front-loaded with purpose. Every sentence is informative, though it could be slightly more structured (e.g., bullet points). Still, no wasted words.

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

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

Given no output schema, the description adequately lists what is returned. It covers limitations (only company type, no name support) and provides fallback tool. Complete for a comprehensive 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%. Description adds meaning: explains type only supports 'company', value can be ticker or CIK, and notes name resolution must be done via resolve_entity. This adds value 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 starts with 'Get everything about a company in one call', clearly stating the verb and resource. It distinguishes from siblings by mentioning it replaces calling 10+ pack tools, and provides specific use case examples.

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

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

It explicitly says when to use (when user asks 'tell me about X' etc.) and when not to use (when only a name is provided, advising to use resolve_entity first). This gives clear guidance on 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 destructiveHint=true, and the description aligns by describing deletion. The description adds behavioral context by explaining use cases (clearing sensitive data, stale context), going 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 redundancy. The key action is front-loaded in the first sentence, and guidance is provided succinctly in the second. 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 low complexity (1 parameter, no output schema), the description adequately covers purpose, usage, and pairing with siblings. It could mention that deletion is irreversible, but annotations already signal destructiveness.

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

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

Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. The description does not add additional meaning beyond what the schema provides, so it meets the baseline for high coverage.

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

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

The description explicitly states 'Delete a previously stored memory by key.' This clearly identifies the verb (delete) and resource (memory by key). It distinguishes from sibling tools 'remember' and 'recall' by indicating deletion specifically.

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 scenarios for use: 'when context is stale, the task is done, or you want to clear sensitive data.' It also instructs to pair with 'remember' and 'recall', giving clear guidance on when to use this tool versus alternatives.

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

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

Annotations already provide readOnlyHint, openWorldHint, and destructiveHint. The description adds no behavioral traits beyond stating the use of an ID, which is already in the schema. It fails to disclose any additional context like rate limits, data completeness, or return format.

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 at one sentence and four words. It front-loads the purpose effectively, but the extreme brevity sacrifices necessary detail 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?

For a simple lookup tool with annotations and no output schema, the description is insufficient. It does not explain the nature of the return value (e.g., full manga object with fields), nor does it address the openWorldHint meaning incomplete data. More context is needed for an agent to use it correctly.

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

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

With 0% schema description coverage, the description adds minimal meaning by stating 'by id', but it merely reiterates the parameter name. It does not explain what constitutes a valid ID or how to obtain 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 'Manga entry by id.' clearly states the verb (retrieve a manga entry) and the resource (manga) with a specific identifier. It distinguishes from sibling tools like search_manga and top_manga which have different purposes.

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

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

No explicit guidance on when to use this tool versus alternatives. The implied usage is fetching a single manga by ID, but no when-not-to-use or alternative suggestions are provided.

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

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

Annotations indicate readOnlyHint=false and destructiveHint=false, meaning it's a write but non-destructive operation. The description adds important behavior: it's free, doesn't count against tool-call quota, and rate-limited to 5 per identifier per day. 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 (about 60 words) but packs in purpose, usage guidelines, behavioral details, and content rules. Every sentence adds value 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?

For a simple feedback tool with 3 parameters (one optional nested object), the description covers all necessary aspects: purpose, expected content, behavior, and constraints. There is no output schema, but that is appropriate for a feedback endpoint.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value beyond the schema by providing usage context (e.g., 'don't paste the end-user's prompt') and clarifying the type enum with practical examples. The nested context object is well-explained in both schema and 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's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback types (bug, feature, data_gap, praise) and distinguishes from sibling tools by focusing on feedback instead of queries or data retrieval.

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

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

Explicitly tells when to use the tool: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also provides a clear exclusion: 'don't paste the end-user's prompt.' Rate limits and quota impact are mentioned.

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 (readOnlyHint=true) and non-destructive (destructiveHint=false) behavior. The description adds value by explaining the internal logic (walking child markets, extracting dates/thresholds, sorting, and reporting violations). This goes beyond the annotations, though it does not cover edge cases like invalid events.

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 efficiently communicates the tool's purpose, mechanics, and output format. Every sentence adds value, and the most critical information (purpose and example) is front-loaded.

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

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

Given the low complexity (one parameter, no output schema), the description adequately explains the tool's function and return structure. It partially describes the output format (list of entries with fields). However, it omits details about error handling and the exact structure of market objects, leaving minor ambiguity.

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 schema description covers the single parameter 'event' with 100% coverage, including an example. The description repeats this information but does not add additional meaning beyond what the schema already provides. Per the rule, baseline is 3 when coverage is high.

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 in Polymarket events. It provides a concrete example, making the purpose specific and understandable. However, it does not distinguish this tool from sibling tools like 'polymarket_edges' or 'validate_claim', so a perfect score is not warranted.

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 only tells the user to pass a Polymarket event slug or URL. It gives no guidance on when to use this tool versus alternative tools for similar tasks (e.g., checking market edges or validating claims). No when-not-to-use or alternative tool references are provided.

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

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

Annotations already indicate read-only, non-destructive behavior. The description adds rich detail on the tool's operations: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge magnitude. 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 appropriately sized, with three sentences that each serve a purpose: purpose, technical detail, and use case. It front-loads the main function. Slightly dense but not verbose.

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

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

Given the tool's complexity (multiple markets, models, ranking), the description covers logic and data sources well. However, without an output schema, it would benefit from specifying the return structure (e.g., fields like market name, edge, direction). Adequate but not fully 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% and schema descriptions are clear for all three parameters. The description does not add new parameter semantics beyond what the schema provides, so baseline score of 3 applies.

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

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

The description precisely states it scans high-volume Polymarket markets and returns markets with largest disagreement between Pipeworx data and market price, using a specific model. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on single-market edge discovery and the 'what should I bet on today' 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 clearly positions the tool for discovering betting opportunities without manual browsing. It implies usage context but does not explicitly mention when not to use it or contrast with alternatives like 'polymarket_arbitrage' or 'bet_research'.

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. Description adds scoping detail (by identifier) and the listing behavior when key 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?

Three sentences, front-loaded with purpose, no waste. Each sentence adds value: function, use case, pairing/scoping.

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 optional param and no output schema, the description covers return behavior (value or list), scoping, and sibling relationships. 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% with key description. Description reinforces behavior but adds no new semantic detail beyond what schema already provides.

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

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

The description clearly states the tool retrieves a saved value or lists keys, with concrete examples like user's target ticker, address, research notes. It distinguishes from siblings 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?

The description explains when to use (look up stored context) and mentions pairing with remember/forget. It lacks explicit 'when not to use' guidance, but context is clear.

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

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

Describes parallel fan-out to three data sources (SEC EDGAR, GDELT, USPTO) and mentions return fields (structured changes, total changes count, citation URIs), which adds value beyond annotations that already indicate read-only and open-world behavior. 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 a single coherent paragraph with a logical flow (purpose, usage, mechanics, returns). Avoids redundancy, but could be more scannable with bullet points or shorter sentences. Still effective and not overly long.

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. Covers purpose, parameter details, data sources, and typical use cases. Feels complete for the tool's complexity and high schema coverage.

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 all parameters (100% coverage). Description adds useful context: accepted 'since' formats (ISO date or relative shorthand like '7d', '30d', '1y'), and that 'value' can be ticker or CIK, which enhances schema semantics.

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

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

Clearly states the tool retrieves recent changes for a company, with explicit examples of user queries it handles, and distinguishes from siblings like entity_profile or compare_entities by focusing on temporal updates.

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

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

Provides concrete usage examples ('what's happening with X?', 'any updates on Y?') and mentions monitoring scenarios, but does not explicitly exclude cases where it should not be used or mention alternatives.

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

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

Discloses key-value pair storage, scoping by identifier, and persistence details. Annotations indicate write operation (readOnlyHint=false) with no destructive behavior, consistent with 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?

Description is efficient, providing all necessary information in a few sentences. Could be slightly more concise but still effective.

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

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

With simple parameters, good annotations, and clear purpose, the description fully equips an agent to use the tool correctly.

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

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

Schema provides 100% coverage with descriptions for key and value. The description adds examples but does not significantly enhance meaning beyond schema.

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

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

The description clearly identifies the tool's purpose: 'Save data the agent will need to reuse later...' and provides specific examples of what to store. It differentiates from siblings like recall and forget.

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

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

Explicitly explains when to use: 'when you discover something worth carrying forward' and how it pairs with recall/forget. Also notes session vs persistent storage.

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 it as read-only and non-destructive. The description adds that it returns IDs plus pipeworx:// citation URIs, which is useful behavioral context beyond annotations. Does not mention any side effects or required permissions, but annotations cover safety.

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

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

Description is a single paragraph of ~100 words, front-loaded with the main action. Each sentence adds value (purpose, usage timing, examples, output). Could be slightly more structured but remains concise and informative.

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

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

For a simple lookup tool with no output schema, the description fully covers input semantics, output type (IDs + URIs), and usage context. No gaps remain given the tool's complexity.

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

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

Schema coverage is 100%, baseline 3. Description enriches by explaining acceptable values (ticker, CIK, name for company; brand/generic name for drug) and providing examples. Adds meaning beyond the enum and field 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 specifies 'Look up the canonical/official identifier for a company or drug' with concrete examples (Apple → AAPL/CIK, Ozempic → RxCUI). It clearly distinguishes from sibling tools by focusing on ID resolution needed by other tools.

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

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

Explicitly states 'Use when a user mentions a name and you need ...' and 'Use this BEFORE calling other tools that need official identifiers.' Implicitly defines when not to use (when you already have the ID), but doesn't explicitly list exclusions. Good guidance overall.

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 reiterate safety. However, the description adds no behavioral details beyond annotations (e.g., pagination, result format, or sorting). It is adequate but minimal.

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, efficient sentence with no extraneous information. It is front-loaded and directly states the tool's purpose.

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

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

The tool has no output schema and the description does not explain what the return value is (e.g., a list of anime results). Given the simplicity of the tool and the presence of sibling search tools, the description lacks completeness for an agent to fully understand the tool's 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?

With 50% schema description coverage, the description should compensate for the undocumented 'query' parameter, but it does not add any meaning beyond the schema. The schema already documents 'limit' with a range, but 'query' lacks description in both schema and 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 action (search) and the resource (anime), specifying the search criterion (by name). It effectively differentiates from siblings like 'anime' (likely for fetching details) and 'search_manga' (different 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 is provided on when to use this tool versus alternatives such as 'anime' or 'search_manga'. There are no exclusions, prerequisites, or contextual hints for the agent to decide between 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, which cover the safety profile. The description adds minimal behavioral context beyond the search action, which is consistent.

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

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

The description is extremely brief (5 words), but this is under-specification rather than conciseness. It sacrifices completeness for brevity.

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

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

Given 2 parameters, no output schema, and only basic annotations, the description is incomplete. It lacks usage notes on pagination, result format, or how the limit parameter behaves.

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 any parameters. 'query' is intuitive but 'limit' is not described; no default, range, or behavior (e.g., whether it's max results or page size).

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 searches manga by name, using verb 'search' and resource 'manga'. It distinguishes from siblings like 'manga' (likely get by ID) and 'top_manga' (ranking). However, it lacks specifics on scope (e.g., full manga list vs popular).

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 like 'manga' or 'top_manga'. No exclusion criteria or context provided.

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

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

The description adds no behavioral information beyond what the annotations already provide (readOnlyHint, openWorldHint). It does not explain that the tool returns a list or any additional behavior like sorting or default parameters.

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 too short; while concise, it omits essential details. It does not earn its place by providing useful information beyond the tool name.

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 that there is no output schema, the description should explain what the tool returns. It does not. The tool lacks necessary context about the result shape, and with many similar siblings, it is incomplete.

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

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

The description does not mention any parameters. With 50% schema coverage (only 'by' has a description), the description fails to compensate for the undocumented 'limit' parameter. It adds no 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 'Top anime list.' is vague and barely more than a tautology of the name. It does not specify what 'top' means (e.g., by popularity or rating) and fails to distinguish the tool from siblings like 'top_manga' or 'search_anime'.

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 no guidance on when to use this tool versus alternative sibling tools. It does not mention any context, prerequisites, or scenarios where this tool is preferred.

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 no behavioral context beyond what annotations already provide (readOnlyHint, openWorldHint, destructiveHint). It does not disclose sorting, pagination, or what 'top' means, leaving significant gaps for an AI agent.

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 brief (two words: 'Top manga list.'), but this brevity results in under-specification rather than effective conciseness. It lacks essential details that a longer but still concise description could provide.

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 two parameters with no descriptions and no output schema, the description is incomplete. An agent cannot determine valid values for 'by' or constraints on 'limit', making the tool difficult to use correctly.

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

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

Both parameters in the schema ('by', 'limit') have no descriptions, and the tool description does not mention or explain them at all. With 0% schema coverage, the description fails to add any semantic meaning for parameter usage.

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

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

The description 'Top manga list' essentially restates the tool name 'top_manga', providing minimal additional meaning. It fails to specify what 'top' criteria is used or differentiate from sibling tools like 'top_anime' or 'manga'.

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. An agent has no context on prerequisites, typical use cases, or when a different sibling tool would be more appropriate.

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 value beyond the annotations (readOnlyHint, openWorldHint). It discloses the return format (verdict types, citation, percent delta) and explains that the tool replaces 4–6 sequential calls, providing insight into its internal complexity. 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 well-structured with front-loaded purpose, usage examples, domain limitations, and output details. While slightly lengthy, every sentence adds value. It could potentially be tightened, but it remains clear and informative.

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

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

Given the tool has one parameter, full schema coverage, and annotations, the description covers purpose, input format, output format, domain, and composite nature. It does not address error cases or out-of-domain claims, but for the intended use it is sufficiently complete.

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

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

Schema description coverage is 100%, so baseline is 3. The tool description adds context about supported claim types (company-financial) and data sources (SEC EDGAR + XBRL), which enriches the parameter understanding but does not substantially increase clarity beyond the schema's example.

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

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

The description clearly states the tool's purpose: to fact-check, verify, validate, or confirm/refute natural-language factual claims against authoritative sources. It distinguishes itself from sibling tools like `compare_entities` or `entity_profile` by focusing on claim verification rather than entity comparisons or profiles.

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

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

The description provides explicit usage examples ('Is it true that…?', 'Was X really…?') and specifies the domain (company-financial claims for public US companies). It does not explicitly state when not to use or mention alternatives, but given the sibling tools are mostly anime/manga or other specialized tools, this is sufficient guidance.

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