Anilist

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

Discloses that it picks the right tool and fills arguments, abstracting away tool selection and schema learning. With no annotations provided, the description adequately conveys 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?

Description is concise (4 sentences), front-loaded with purpose, and includes examples. One sentence on behavior could be slightly tightened.

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 single parameter and no output schema, the description is sufficient to understand tool functionality. Provides clear examples for common use cases.

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

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

Schema coverage is 100% and the description elaborates on the 'question' parameter, explaining it should be a natural language request. Adds value beyond schema with examples.

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 accepts natural language questions and returns answers by selecting the best data source. Distinguishes itself from siblings by its role as a universal query tool, not limited to anime or memory operations.

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 to 'just describe what you need' and provides examples. However, does not specify when NOT to use this tool or mention alternatives for specific domains.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true. The description adds significant behavioral context: it resolves the market, classifies the bet, fans out to appropriate data packs, and returns a comparison. It also states this is the core demo product for efficient context retrieval, which aligns with annotations and provides extra insight.

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 five sentences, front-loading the purpose, then input formats, then process and usage. It is dense but not verbose, with each sentence adding value. Slightly verbose in the last sentence about conversion, 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?

Given the tool's complexity (no output schema, 2 params, no nested objects), the description sufficiently explains what it does and returns. It covers the adaptive fan-out based on bet type and the market-vs-model comparison. While exact output structure isn't detailed, the description is complete enough for an AI agent to understand its functionality.

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 schema (100% coverage). The description adds meaning by explaining how market input is resolved (slug, URL, or question text) and that depth controls evidence sources (quick vs thorough). This enhances understanding beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: researching Polymarket bets by pulling Pipeworx data in one call. It specifies inputs (slug, URL, question text) and outputs (evidence packet with comparison). It distinguishes from sibling tools by being specific to Polymarket bets, and it's noted as the 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?

The description gives explicit usage guidance: 'Use for 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. While it doesn't explicitly state when not to use, the context is clear that it's only for Polymarket bets, and no sibling tool directly overlaps.

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?

No annotations are provided, so the description must convey behavioral traits. It states the tool returns paired data and resource URIs, but does not explicitly state it is read-only or free of side effects. The description implies a safe query operation, but could be more 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?

The description is two concise sentences. The first sentence states the core purpose, and the second adds essential details. No unnecessary words, and it is front-loaded with the most important 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 absence of an output schema, the description adequately explains the return data (paired data + resource URIs) and covers both entity types. It could mention examples or pagination, but for a simple comparison tool, it is fairly 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?

The input schema has 100% coverage with descriptions for both parameters. The description enriches by explaining the meaning of 'values' for each entity type and the range constraints. This adds value beyond the schema alone.

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

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

The description explicitly states the tool compares 2-5 entities side by side, with specific details per entity type (company vs. drug). It highlights efficiency by replacing multiple sequential calls, clearly distinguishing it from alternative approaches.

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 (for comparison of companies or drugs) and notes it replaces 8-15 sequential calls. While it does not explicitly state when not to use it, the context is sufficient for an agent to decide.

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?

No annotations are provided, so the description must cover behavioral traits. It states the tool returns 'the most relevant tools with names and descriptions,' which is adequate. However, it does not disclose if the tool has side effects, requires authentication, or any rate limits. A score of 3 is appropriate as it gives basic behavioral info but lacks depth.

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, each serving a purpose: stating the action, describing the return, and providing usage guidance. It is front-loaded and efficient with 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 tool's complexity (simple search with 2 params, no output schema), the description covers the core functionality and usage context. However, it could mention whether the search is fuzzy or exact, or if results are ranked, but the existing description is sufficient for an agent to use it effectively. Score 4 for being mostly 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?

The input schema has 100% coverage: both parameters have descriptions. The tool description adds context about the query parameter ('Natural language description...') and the limit parameter (default and max values), reinforcing schema info. However, the description doesn't provide additional meaning beyond what's in the schema, so score is 4.

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

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

The description clearly states the tool's purpose: 'Search the Pipeworx tool catalog by describing what you need.' It uses a specific verb ('search') and resource ('tool catalog'), and distinguishes itself from siblings by being a discovery tool for a large catalog, unlike specific data tools like search_anime or get_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 explicitly advises: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and implies when not to use (when you already know the tool), setting it apart from sibling 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?

With no annotations, the description carries full responsibility. It discloses that only 'company' type is currently supported, aggregates multiple data sources, and returns pipeworx:// URIs. However, it does not explicitly state read-only behavior or potential side effects, though the context implies it's a read operation.

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 core purpose, and uses bullet-like listing for clarity without verbosity. Each sentence serves a distinct purpose: definition, content details, and usage caveat.

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

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

Given no output schema, the description sufficiently explains the returned data (URIs and content types). It covers key functional aspects but could slightly improve by hinting at the structure or format of the response, though the example data types are clear.

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

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

Schema coverage is 100%, and the description reinforces the parameter semantics by specifying accepted values (ticker or CIK for value, only 'company' for type). It adds valuable context that names are not supported and recommends resolve_entity as an alternative.

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 'full profile of an entity' and enumerates specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from siblings by mentioning it replaces 10-15 sequential calls and is the go-to for comprehensive company 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 explicitly tells when to use this tool (for comprehensive profiles) and when not to (for federal contracts, direct to usa_recipient_profile). It also advises using resolve_entity first if only a name is available, providing clear decision 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?

No annotations are provided, so the description must carry the full behavioral burden. It states the action 'delete' which implies mutability, but doesn't disclose any side effects, authorization needs, or whether deletion is permanent. A score of 3 is given as it's minimally adequate but lacks depth.

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

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

The description is a single sentence with no wasted words. It is front-loaded with the action and resource, meeting the need for conciseness.

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

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

Given the tool is simple (1 param, no output schema, no annotations), the description is adequate but not thorough. It covers the basic purpose but lacks guidance on usage and behavioral details. Could be improved by mentioning that deletion is permanent or that the key must exist.

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 the baseline is 3. The description adds minimal value beyond the schema; it repeats 'memory key to delete' essentially. No additional context on the key format or constraints is provided.

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

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

The description uses a clear verb 'Delete' and identifies the resource 'stored memory' and the parameter 'key'. It distinguishes itself from sibling tools like 'remember' (create) and 'recall' (retrieve) by specifying the delete action.

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 given on when to use this tool versus alternatives. For example, it doesn't mention that deletion is irreversible or that it should be used only when the key is known. With siblings like 'recall' and 'remember', explicit usage context would be beneficial.

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?

No annotations provided, so description carries full burden. It discloses the tool is a read operation that returns specific fields, but lacks details on error behavior, rate limits, or whether ID must be valid.

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

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

Description is concise with two sentences. First sentence states action and input, second lists output. Efficient but could be slightly more structured.

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

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

Given the tool has only one parameter and no output schema, the description provides enough information for basic use. It lists returned fields but could mention pagination or error states.

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

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

Schema coverage is 100%, and the description does not add significant meaning beyond the schema. The schema already describes 'id' as 'AniList media ID' with examples. Description lists returned fields but not parameter details.

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 retrieves full details for an anime by its AniList ID. The verb 'Get' and specific resource 'anime' combined with the list of returned fields (title, synopsis, etc.) make the purpose unambiguous.

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

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

Description implies usage when you need detailed info for a known ID, but does not explicitly mention when to use alternatives like 'search_anime' or 'trending_anime'. No guidance on when not to use.

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

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

Since no annotations are provided, the description carries the full burden. It discloses rate limiting (5 per identifier per day) and states it's 'Free'. It does not detail the return behavior or side effects, but for a feedback tool, this is reasonable.

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

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

The description is three sentences with no waste: first sentence states purpose, second gives usage guidance and constraint, third mentions rate limit and cost. Each sentence is essential.

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

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

Given the tool's simplicity and lack of output schema, the description plus input schema provide complete context for using the tool. No sibling tool overlaps with this feedback purpose.

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 advising users to describe what they tried in terms of Pipeworx tools/data and to avoid including the end-user's prompt verbatim, which enriches the 'message' parameter guidance.

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

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

The description clearly states 'Send feedback to the Pipeworx team' with specific use cases (bug reports, feature requests, missing data, praise). It distinguishes this tool from siblings like 'ask_pipeworx' or 'discover_tools' by being the dedicated feedback channel.

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

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

The description lists appropriate use cases and includes a negative instruction ('do not include the end-user's prompt verbatim'). It also mentions rate limits. It does not explicitly exclude other scenarios, but the purpose is clear enough to guide 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 declare readOnlyHint=true, so the tool is safe. The description adds transparency by explaining the algorithm (sorting, detecting violations) and return structure. No contradictions with annotations.

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

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

The description is front-loaded with the purpose and includes necessary details like input format and return structure. It is slightly verbose but every sentence adds context. Could be trimmed slightly without losing meaning.

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, but the description lists return fields (market_a, market_b, gap_pp, suggested_trade). It also explains the algorithm and use case. For an arbitrage detection tool, this is complete and sufficient for an AI agent to invoke 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 covers 100% of parameters with description. The description adds value by specifying that the event parameter accepts a slug or full URL, providing a concrete example. This goes beyond the schema's generic 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 in Polymarket events by checking monotonicity violations. It specifies the verb (find), resource (Polymarket event), and unique behavior (detecting pricing order violations). No sibling tool does this, so it's well-distinguished.

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: for events with multiple 'by [date]' or 'by [threshold]' markets. It provides logical justification and input format. However, it does not explicitly state when not to use it or provide alternatives, though siblings don't overlap.

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 valuable details: uses a lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, computes model probability, and ranks by |edge|. This provides behavioral context beyond what annotations offer.

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 moderately sized and front-loaded with the main action. Each sentence contributes information about purpose, scope, algorithm, and output. It is not overly verbose and maintains 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 moderate complexity and absence of an output schema, the description fully informs the agent about what the tool does, how it works, and what it returns (top N by edge magnitude with trade direction). It covers the use case and limitations (V1 covers crypto-price bets).

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% description coverage, with each parameter's description clearly stating its purpose, defaults, and constraints. The tool description does not add additional meaning beyond the schema, so baseline score 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 purpose: scanning high-volume Polymarket markets and returning those where Pipeworx data disagrees most with market price. It specifies the scope (crypto-price bets, top markets, model ranking) and distinguishes it from siblings like 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 explicitly frames the tool for the 'what should I bet on today' question, providing clear context for when to use it. It implies it's for opportunity discovery without paging through hundreds of markets, though it does not explicitly mention when not to use it or list 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?

No annotations provided, so description carries full burden. Describes dual behavior (by key vs list all) and session persistence. Could mention idempotency or side effects, but sufficient for a read operation.

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

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

Two sentences, front-loaded with action and conditions. No redundancy, 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 simplicity (1 param, no output schema, no nested objects), description covers purpose and usage. Could mention return format or error handling, but adequate for a straightforward recall 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% (single parameter 'key' described). Description adds context: omit to list all. Baseline 3 appropriate as schema already explains 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?

Clearly states it retrieves a memory by key or lists all if key omitted. Specific verb ('Retrieve') and resource ('memory') with distinct behaviors.

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

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

Explicitly says when to use (to retrieve context saved earlier) and how to list all (omit key). Does not explicitly mention when not to use or alternative tools, 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?

With no annotations, the description fully bears the burden. It discloses that for type=company, it fans out to SEC EDGAR, GDELT, and USPTO in parallel. It also states the return format (structured changes + total_changes + URIs). No destructive or authentication details are needed for this read-only tool.

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 packs essential information: purpose, behavior, parameter details, and usage suggestions. It is concise without being terse, though a bulleted structure could slightly improve readability.

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

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

Given three required parameters, no output schema, and no annotations, the description covers all necessary aspects: input format, tool behavior (fan-out), return structure, and typical use cases. No obvious gaps remain.

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. The description adds meaning beyond the schema: it explains the parallel fan-out behavior for type=company, the available relative time formats for since, and that value accepts tickers or CIK numbers. This enriches parameter understanding.

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

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

The description starts with 'What's new about an entity since a given point in time,' which clearly states the tool's purpose. It specifies the verb ('brief me', 'change-monitoring'), resource (entity changes over time), and distinguishes it from siblings like entity_profile (current state) or compare_entities (comparison).

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 recommends using it for 'brief me on what happened with X' or change-monitoring workflows. It provides guidance on the since parameter (ISO dates or relative formats) and suggests typical values like '30d' or '1m'. However, it does not contrast with siblings or specify when not to use it.

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

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

Without annotations, the description discloses key behavioral traits: persistent memory for authenticated users and 24-hour expiration for anonymous sessions. It also implies data storage but does not mention any limitations on storage size or potential overwrite behavior.

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

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

The description is two sentences, front-loads the core action, and provides additional context without redundancy. 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 no output schema and simple input, the description adequately explains the tool's purpose, persistence behavior, and example uses. It lacks mention of return value or confirmation, but this is minor for a store operation.

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

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

Schema coverage is 100%, and the description adds meaning by explaining the purpose of key-value pairs (e.g., 'subject_property', 'target_ticker') and that value is any text. The examples in the schema further clarify usage, so the description complements it well.

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

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

The description clearly states the verb 'store' and resource 'key-value pair in your session memory'. It distinguishes from siblings like 'forget' and 'recall' by specifying that it stores data for later retrieval, but does not explicitly contrast with them.

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

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

The description provides a general usage context ('save intermediate findings, user preferences, or context across tool calls'), but does not specify when not to use it or explicitly compare with alternatives like 'recall' for retrieval.

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?

No annotations are provided, so the description carries the full burden. It discloses the return values (ticker, CIK, company name, URIs) and notes that v1 only supports type='company', which is transparent about limitations. No destructive behaviors are implied.

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

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

The description is two sentences, front-loaded with the purpose and supported by specific examples. Every sentence adds value, with 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 description is fairly complete for a lookup tool: it explains what the tool does, what inputs are accepted, and what outputs to expect. It omits error handling or edge cases but adequately covers the main use case given the schema coverage and lack of output schema.

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

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

The input schema has 100% coverage, so the baseline is 3. The description repeats information already in the schema (e.g., accepted value formats) without adding significant new meaning beyond what the schema properties provide.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources, specifying the entity type (company) and acceptable inputs (ticker, CIK, name). It distinguishes from sibling tools, none of which perform entity resolution.

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

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

It indicates this tool replaces 2-3 lookup calls, suggesting when to use it for efficiency. However, it does not explicitly state when not to use it or provide alternatives, though the context is clear.

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

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

No annotations provided, so description carries full burden. It states it returns specific fields (title, episodes, etc.) but does not disclose rate limits, data freshness, or behavior on no results. The return format is implicit but not detailed (no output schema).

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

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

Single sentence front-loads purpose, followed by what it returns. No wasted words. Under 20 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?

For a simple search tool with 2 params and no output schema, the description covers basics. Missing: pagination behavior, error cases, but acceptable for this 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% with descriptions for both params (query and limit). Description adds no extra parameter meaning beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'search' and resource 'anime by title using AniList'. It distinguishes from sibling 'get_anime' (likely single result) and 'trending_anime' (trending vs search). However, it doesn't explicitly contrast with these siblings.

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?

Implies use when needing to find anime by title. No explicit when-not-to-use or alternatives given, though siblings exist. Context signals show only one required param (query) with a default limit, so usage is straightforward.

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

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

Annotations are empty, so description carries full burden. States it returns specific fields (title, status, score, episodes, genres) and ordering (by trending score). Does not mention rate limits, pagination, or caching behavior.

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

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

Two concise sentences: first states purpose and ranking, second lists returned fields. No redundancy or 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 no output schema, description adequately covers purpose and return fields. No explicit mention of default limit or range, but schema handles that. Overall sufficient for a simple list 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 description need not add parameter details. The description does not mention the limit parameter, but schema already documents it adequately.

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

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

Description clearly states 'Get currently trending anime on AniList, ranked by trending score', specifying verb 'Get', resource 'currently trending anime', and ranking metric. Distinct from sibling tools like search_anime (search) and get_anime (single 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?

Explicitly mentions what the tool does (trending) and what it returns. No explicit when-not-to-use or alternatives, but sibling differentiation is clear from 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?

With no annotations, the description fully explains the tool's behavior: it returns a verdict, structured form, actual value, citation, and percent delta. It also mentions the underlying data sources (SEC EDGAR + XBRL). It doesn't discuss errors or rate limits, but for a single-parameter tool, this is sufficient.

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: purpose, scope, and output details. It is front-loaded with the core action, uses no filler, 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 covers input format, supported domain, and output items. It could mention limitations or version applicability, but it is largely 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?

The only parameter 'claim' is described in the schema with a generic description. The tool description adds critical context about the accepted claim types (company-financial), improving upon the schema alone.

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

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

The description clearly states the tool fact-checks claims against authoritative sources, specifies the supported domain (company-financial claims for US public companies), and lists the return types. It distinguishes itself from sibling tools like ask_pipeworx by its specialized function.

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 limits scope to company-financial claims and notes it replaces multiple agent calls, implying efficiency. However, it doesn't explicitly state when not to use it or provide alternatives, leaving some ambiguity.

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