uooks

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a query tool that interprets natural language, selects data sources automatically, and returns results. However, it doesn't mention potential limitations like response time, accuracy, or data source constraints, which would be helpful for a tool with no annotation coverage.

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 efficiently structured: it starts with the core functionality, explains the mechanism, provides usage guidance, and includes relevant examples. Every sentence adds value without redundancy, making it easy to understand quickly. The length is appropriate for a tool with this complexity.

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

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

Given the tool's complexity (natural language processing, automatic tool selection) and lack of annotations or output schema, the description does a good job covering purpose, usage, and parameters. However, it doesn't detail output format or potential error conditions, which would help an agent handle responses better. It's mostly complete but has minor gaps.

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

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

The schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds value by explaining the parameter's semantics: it should be a 'question or request in natural language' and provides examples like 'Look up adverse events for ozempic'. This goes beyond the schema's basic description, earning a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'), distinguishing it from sibling tools like search_books or discover_tools which have more specific scopes.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly, use other tools if you want to browse or learn schemas) and includes concrete examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.

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

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

Annotations already show readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds value by explaining the internal process: market resolution, bet classification, fan-out to specific packs (e.g., crypto+fred+gdelt), and the nature of the return (evidence packet + market-vs-model comparison). 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 a single paragraph of about 5 sentences, front-loaded with purpose and followed by process, use cases, and value. It is appropriately detailed without being verbose, though it includes a slightly promotional line ('core demo product').

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 complex tool with no output schema, the description adequately explains the return (evidence packet plus comparison) and the effect of the depth parameter. It covers the resolution, classification, and fan-out logic, giving the agent a solid mental model of 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?

Schema coverage is 100% with descriptions for both 'market' and 'depth' (with enum values). The description reinforces meaning by providing examples for 'market' and clarifying that 'quick = 2-3 evidence sources, thorough = full fan-out' and default 'thorough', adding useful context beyond the schema.

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

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

The description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call', specifying the verb 'Research', the resource 'Polymarket bet', and the method. It also explains the process (resolve, classify, fan-out, return comparison) and distinguishes from siblings by positioning as a one-call solution.

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

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

The description provides explicit use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"'. It contrasts with having to discover packs manually, but does not explicitly mention when not to use or alternative sibling tools like ask_pipeworx.

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 must convey behavior. It mentions return includes paired data and resource URIs, but lacks details on side effects, data freshness, or whether it's read-only.

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

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

Description is concise and front-loaded with key action, but could be more structured (e.g., bullet points for metrics). 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, description partially addresses return (paired data + URIs). Lacks exact output structure or example, but sufficiently complete for a comparison 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%, baseline 3. Description adds meaningful context: explains 'type' enum values and specifies that 'values' should be tickers/CIKs for company or drug names, going beyond schema.

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

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

Description clearly states it compares 2-5 entities side by side, specifies two entity types (company and drug) with distinct metrics, and highlights efficiency gains over sequential calls.

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

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

Description explains when to use (comparing 2-5 entities of given types) and implies it reduces multiple calls, but does not explicitly mention when not to use or suggest 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools based on natural language queries, with implied read-only functionality. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be helpful for a discovery 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 perfectly concise with two sentences that each serve distinct purposes: the first explains what the tool does, and the second provides crucial usage guidance. There's no wasted language, and the most important information (to call this first in specific scenarios) is appropriately 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 tool's moderate complexity (search functionality with natural language queries) and the absence of both annotations and output schema, the description does a good job of explaining the tool's purpose and primary use case. However, it doesn't describe what the output looks like (beyond mentioning it returns 'tools with names and descriptions'), which would be helpful since there's no output schema provided.

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 has 100% description coverage, so the parameters are well-documented in the structured schema. The description doesn't add significant semantic value beyond what's already in the schema descriptions, though it reinforces the natural language query concept. This meets the baseline expectation when schema 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's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It explicitly distinguishes this from sibling tools by emphasizing its role in discovering tools rather than directly accessing specific resources like authors or books.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and includes a clear alternative scenario (when you don't have many tools or already know which ones to use). This gives strong contextual direction for tool selection.

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 burden. It discloses that it returns Pipeworx citation URIs and replaces many calls, but does not mention whether the operation is read-only, any authorization requirements, rate limits, or performance characteristics. This is a moderate gap for a complex aggregation 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 concise (3 sentences) and well-structured, using bullet-point style to list returned data types. No wasted words; every sentence adds unique value.

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

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

Given the tool's complexity and lack of output schema, the description explains what is returned (citation URIs) and covers the main data categories. However, it does not describe the structure of the response, pagination, or maximum result size. Slightly incomplete but still strong.

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. The description adds valuable context beyond the schema by providing concrete examples (ticker 'AAPL', zero-padded CIK) and clarifying that names are not supported, which helps avoid incorrect usage.

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

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

The description clearly states it returns a full profile of an entity across multiple Pipeworx packs, with specific details for type='company' (SEC filings, XBRL data, patents, news, LEI). It distinguishes from sibling tools by noting that it replaces 10-15 sequential agent calls.

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

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

Explicitly says when to use (one-call full profile) and when not to (federal contracts → use usa_recipient_profile). Also implies that for names, one should first call resolve_entity, as names are not supported directly.

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 states 'Delete', implying a destructive mutation, but doesn't disclose behavioral traits like whether deletion is permanent, requires specific permissions, has side effects, or provides confirmation. This is a significant gap for a mutation tool with zero annotation coverage.

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 zero waste. It's front-loaded with the core action ('Delete'), making it easy to parse quickly, which is ideal for concise tool definitions.

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 as a destructive operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., permanence, permissions) and what happens post-deletion (e.g., success confirmation or error handling), which are crucial for safe and effective use.

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% description coverage, with the 'key' parameter documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples. With high schema coverage, the baseline is 3, as the schema does the heavy lifting.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory by key'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', which likely handle memory retrieval and storage respectively, so it misses full sibling distinction.

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 alternatives. It doesn't mention prerequisites, such as needing an existing memory key, or contrast with tools like 'recall' (which might retrieve memories) or 'remember' (which might store them), leaving the agent to infer usage context.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information ('Get biography and key info'), which implies a read-only operation, but it does not disclose any behavioral traits such as rate limits, error handling, or response format. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 that front-loads the core purpose and includes necessary details (the parameter and an example) without any wasted words. It is appropriately sized for a simple tool with one parameter, making it easy to understand quickly.

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

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

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameter usage, but lacks details on behavioral aspects like response format or error conditions. For a read-only tool with no annotations, more context would be beneficial to fully guide an agent.

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

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

The schema description coverage is 100%, with the parameter 'author_key' fully documented in the input schema. The description adds minimal value by reiterating the parameter's purpose and providing an example ('e.g., OL23919A'), but it does not offer additional semantics beyond what the schema already provides. This meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Get biography and key info') and the resource ('for an author'), distinguishing it from sibling tools like 'get_book' and 'search_books'. It explicitly mentions the required identifier ('Open Library author key'), making the purpose unambiguous and distinct.

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

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

The description implies usage by specifying the required parameter ('using their Open Library author key'), but it does not explicitly state when to use this tool versus alternatives like 'get_book' or 'search_books'. There is no guidance on exclusions or prerequisites, leaving the context somewhat open-ended.

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 provided, the description carries the full burden of behavioral disclosure. It states the action ('Get full details') but doesn't cover critical aspects like whether this is a read-only operation, potential error conditions (e.g., invalid ISBN), rate limits, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 that directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool, earning the highest score 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's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and output expectations, which are necessary for effective agent operation despite the simple context.

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

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

The schema description coverage is 100%, with the parameter 'isbn' fully documented in the schema as 'ISBN-10 or ISBN-13'. The description adds no additional meaning beyond this, such as format examples or edge cases, so it meets the baseline score for high schema coverage without extra value.

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 ('Get') and resource ('full details for a book'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_books' (which might return multiple results) or 'get_author' (which targets a different resource), so it doesn't reach the highest score.

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 alternatives such as 'search_books' or 'get_author'. It lacks context about prerequisites, exclusions, or specific scenarios for usage, leaving the agent with minimal direction.

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

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

With no annotations, the description bears full responsibility for behavioral disclosure. It reveals the rate limit (5 per identifier per day) and content constraints (no end-user prompts). While it doesn't detail what happens after sending, these disclosures are sufficient for the tool's simplicity.

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—three sentences that are front-loaded: purpose first, then usage guidelines, then rate limit. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity and the absence of an output schema, the description adequately covers what the tool does and how to use it. It might have mentioned that feedback is sent and not responded to directly, but this is minor.

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% description coverage, so the baseline is 3. The description adds only a minor reinforcement ('Describe what you tried...' for the message field). It does not provide additional meaning beyond the schema's parameter 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: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise), which distinguishes it from sibling tools that focus on querying or retrieving data.

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

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

The description provides explicit guidance on when to use the tool (for feedback types) and what content to include or exclude (describe in terms of Pipeworx tools/data, avoid user prompts). It also mentions rate limits. However, it does not explicitly state when not to use it or suggest alternatives, which is acceptable given the tool's distinct purpose.

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 read-only and open-world hints; description confirms no destructive actions. It details the internal logic (monotonicity checks) and output format (ranked opportunities with reasoning). 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 concise yet comprehensive, front-loading the purpose, then logically explaining each mode with an illustrative example. No unnecessary words.

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

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

Covers the main functionality and output broadly, but lacks specific detail about the return structure (e.g., fields in each opportunity). Without an output schema, slightly more detail would improve completeness.

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 well-described in the schema (100% coverage). The description adds significant context by explaining the two modes and providing example values, 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?

The description clearly states the tool finds arbitrage opportunities via monotonicity violations. It explains two modes (event and topic) with examples, distinguishing it from siblings like polymarket_edges or bet_research.

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

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

Explicit guidance on when to use event mode (single event slug) vs topic mode (cross-event search) with concrete examples. Also explains why cross-event is needed for cases like different cutoffs in separate events.

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 details the model (lognormal from FRED + coinpaprika), process (grouping by asset, fetching price history once, computing model probability, ranking by |edge|), and output (top N with suggested trade direction). It adds significant value beyond the annotations (readOnlyHint, openWorldHint) by explaining internal logic and scope.

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 for the tool's complexity, starting with the primary purpose and then detailing the method and output. Every sentence adds value, though it could be slightly more compact without losing clarity.

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

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

The description covers input, process, and output comprehensively despite lacking an output schema. It explains the algorithm steps, constraints (V1 covers crypto-price bets), and expected results (ranked list with suggested direction), making it complete for an 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?

The schema has 100% description coverage, and the description elaborates on each parameter's role (limit for top N, window for volume timeframe, min_edge_pp for filtering). It adds context like defaults and constraints (max 25 for limit), enhancing understanding beyond the schema.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifically for crypto-price bets. It identifies the use case as discovering betting opportunities, distinguishing it from sibling tools 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 implies usage for identifying betting opportunities without manual browsing. It provides clear context but does not explicitly state when not to use this tool or offer direct comparisons to alternatives beyond its purpose.

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 that the tool can retrieve or list memories, including cross-session persistence, but lacks details on error handling (e.g., if key doesn't exist), permissions, rate limits, or return format. It adds some behavioral context but is incomplete for a tool with no annotation coverage.

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 with zero waste: the first states the purpose and parameter usage, the second provides usage context. It's front-loaded and efficiently structured, with every sentence earning 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 no annotations and no output schema, the description is moderately complete for a simple retrieval tool. It covers the basic purpose and parameter behavior but lacks details on return values, error cases, or limitations. It's adequate but has clear gaps in behavioral transparency.

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 schema already documents the optional 'key' parameter. The description adds value by explaining the semantics: omitting the key lists all memories, and the key retrieves a specific memory. This clarifies the dual functionality beyond the schema's basic 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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('memory'), though it doesn't explicitly differentiate from sibling tools like 'remember' or 'forget' beyond mentioning 'saved earlier.'

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

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

The description provides clear context for when to use the tool: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It explains the behavior when the key is omitted (list all keys), but it doesn't explicitly mention when not to use it or name alternatives among siblings.

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

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

The description is very transparent: it explains the parallel fan-out to multiple sources, input formats (ISO date or relative), and return structure. Since no annotations are provided, it carries the full burden, and it does so well. It lacks mention of rate limits or permissions, but the read-only nature is clear.

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

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

The description is concise (3 sentences) and well-structured: purpose first, then details of behavior, then usage suggestion. Every sentence adds value with no repetition 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?

Despite no output schema, the description outlines return structure (structured changes, total_changes count, URIs). It explains parallel execution. It could mention potential limits on time windows or data freshness, but overall it provides sufficient context for correct usage.

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

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

Input schema coverage is 100% with descriptions. The description adds value by providing examples for 'since' (e.g., '30d', '3m'), explaining 'value' formats (ticker or CIK), and clarifying that 'type' is only 'company'. This goes beyond the schema.

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

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

The description clearly states the tool's purpose: retrieving what's new about an entity since a given point in time. It specifies the entity type (company), data sources (SEC EDGAR, GDELT, USPTO), and use cases. This distinguishes it from sibling tools like entity_profile (full profile) 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 says 'Use for "brief me on what happened with X" or change-monitoring workflows.' This provides clear context. However, it does not explicitly state when not to use it or mention alternatives among siblings, but the use case is well-defined.

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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (implied by 'store'), specifies persistence differences for authenticated vs. anonymous users, and mentions the 24-hour limit for anonymous sessions. However, it does not cover potential rate limits, error conditions, or response formats.

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 and front-loaded, with two sentences that efficiently convey purpose, usage, and behavioral details without redundancy. Every sentence adds value, and there is no wasted text, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (write operation with persistence rules), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage, and key behavioral aspects like persistence differences, but lacks details on return values, error handling, or specific constraints, leaving minor gaps.

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

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

The schema description coverage is 100%, so the input schema already fully documents the 'key' and 'value' parameters. The description does not add any additional semantic information about parameters beyond what the schema provides, such as constraints or usage nuances, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It specifies the storage mechanism and typical use cases, making 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?

The description provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives. It distinguishes from siblings by function but lacks explicit exclusions or comparisons.

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 full burden. It states it returns canonical IDs and URIs but does not disclose error handling, authentication needs, or whether it is read-only. The description is minimal but functional.

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 long, front-loaded with the core purpose, and contains no redundant information. 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 simplicity of the tool (2 parameters, no output schema, no annotations), the description covers the essential aspects: purpose, inputs, outputs, and value. It could add error handling but is sufficient for its 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%, and the description adds value by providing concrete examples for each parameter (e.g., 'AAPL' for ticker) and clarifying the output includes pipeworx URIs, exceeding schema detail.

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, specifies the supported type 'company', and lists the inputs and outputs. It also distinguishes itself by noting it replaces multiple lookup calls.

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

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

The description gives examples of valid inputs (ticker, CIK, name) and explains the return values. It does not explicitly state when not to use this tool or compare with siblings, but the context of resolving entities 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the return fields (title, author, year, ISBN, cover image URL), which adds value beyond the input schema. However, it doesn't cover critical behaviors like pagination (implied by 'limit' parameter but not explained), error handling, rate limits, or authentication needs. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 highly concise and front-loaded, consisting of two sentences that efficiently convey the tool's purpose and return values. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy for an agent to parse quickly.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and lack of annotations or output schema, the description is partially complete. It covers the basic purpose and return fields, but misses details on behavioral aspects like result ordering, pagination, or error cases. Without an output schema, the description should ideally explain the return structure more thoroughly, but it does list key fields, providing a minimal foundation.

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

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

The description adds minimal parameter semantics beyond the input schema, which has 100% coverage. It mentions search criteria ('by title, author, or keyword') that aligns with the 'query' parameter's schema description, but doesn't provide additional context like search syntax or ranking. Since the schema already documents both parameters thoroughly, the description meets the baseline without compensating for any gaps.

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

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

The description clearly states the tool's purpose with specific verbs ('Search for books') and resources ('books'), and specifies search criteria ('by title, author, or keyword'). It distinguishes from sibling tools like 'get_author' and 'get_book' by focusing on search functionality rather than direct retrieval. However, it doesn't explicitly contrast with siblings beyond implying different use cases.

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

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

The description implies usage context through the search criteria, suggesting it's for finding books based on textual queries. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_book' (which might retrieve a specific book by ID) or 'get_author'. No exclusions or prerequisites are mentioned, leaving usage decisions to inference.

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 provided, the description carries the full burden. It discloses the data source (SEC EDGAR + XBRL), the output (verdict, structured form, citation, delta), and the replacement of multiple steps. No destructive actions or requirements are 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, no waste. The first sentence states the purpose, followed by scope and then output details. Information is front-loaded and every sentence earns its place.

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

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

Given the tool's simplicity (single parameter, no output schema), the description covers purpose, input, output, and scope completely. An agent can correctly invoke it without additional context.

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

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

Only one parameter 'claim' with good schema description (100% coverage). The description adds value by explaining the supported claim types and providing examples, going beyond the schema.

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

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

The description clearly states the tool's function: fact-check a natural-language claim against authoritative sources. It specifies the domain (company-financial claims via SEC EDGAR + XBRL) and lists the output verdict types and structure. This distinguishes it from sibling tools like 'resolve_entity' or 'ask_pipeworx'.

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

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

The description explains that it replaces 4-6 sequential agent calls, which implies efficiency. It also specifies the supported claim types (company-financial). However, it does not explicitly state when not to use it or suggest alternatives for non-financial claims, though the scope is clear.

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