Temperature Random

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

Annotations already provide safety hints (read-only, idempotent, non-destructive). The description adds valuable operational details: default model and its free nature, requirement to bring your own API key for Anthropic, and the return structure (per-model score/confidence/signals/raw_response plus combined view). 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 and front-loaded: it states the main action first, then provides key details (default, payment model, return structure). Every sentence adds unique value, with no redundant or extraneous information.

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

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

Given the tool's complexity (4 parameters, no output schema), the description fully covers what an agent needs: expected output format, cost implications, and disambiguation guidance. It is complete for its domain, with no missing critical behavioral information.

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

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

Schema coverage is 100%, so all parameters have good descriptions. The description adds context beyond schema, e.g., default model for the 'models' parameter, BYO key for '_apiKey', and disambiguation use for 'context'. This adds meaningful guidance but is not essential since schema already covers basics.

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: probing LLMs for knowledge about a brand/product/topic and scoring visibility (0-100) per model. It uses specific verbs and distinguishes this tool from siblings like 'scan_competitor_ai_presence' by focusing on multi-model scoring and raw response retrieval.

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

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

The description explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It implies appropriate contexts but does not explicitly state when not to use it or contrast with sibling tools, which would have earned a 5.

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 reveals key behavioral traits: Pipeworx selects the right tool and fills arguments, abstracting complexity. With no annotations provided, this transparency is valuable, though it doesn't detail failure modes or limitations.

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 at three sentences, front-loading the core capability and supporting with examples. Minor improvement could be more direct structure, but it's efficient.

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

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

Given the simplicity of the tool (single parameter, no output schema), the description adequately covers what users need to know. Examples provide context, and no output schema reduces the need for return value details.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the parameter 'question' should be a natural language request, with examples. This goes beyond the schema's 'Your question or request in natural language' by illustrating usage.

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

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

The description clearly states the tool's purpose: to answer natural language questions by intelligently routing to the best data source. It provides specific examples like trade deficits and SEC filings, making the verb+resource relationship unambiguous and distinct from 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?

The description explicitly advises users to 'just describe what you need' and avoid browsing tools or schemas, which implies when to use it. However, it doesn't explicitly mention when not to use it or provide alternative tool names.

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

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

Annotations already provide readOnlyHint, openWorldHint, and destructiveHint. The description adds substantial behavioral context: market resolution, bet classification, fan-out to packs, and output structure. There is no contradiction with annotations.

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

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

The description is moderately long but well-organized: main action first, then input details, process, output, and use cases. Every sentence adds value. Could be slightly trimmed, but effective.

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

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

Given the tool's complexity (Polymarket research with multiple data sources), the description covers inputs, processing logic, output (evidence packet + comparison), and use cases. No output schema exists, but the description adequately describes return values. Slight omission of exact fields in evidence packet.

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 concrete usage examples (slug, URL, question text) and explains the depth parameter with default behavior ('quick = 2-3 sources, thorough = full fan-out, Default thorough'). This meaningfully extends 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 states a specific verb-resource combination ('Research a Polymarket bet by pulling Pipeworx data'), lists input types (slug, URL, question text), and details outputs (evidence packet, market-vs-model comparison). It distinguishes from siblings like ask_pipeworx or compare_entities by positioning as the core bet research tool.

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 mentions three use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and contrasts with alternative approaches. However, it does not specify when not to use this tool or mention sibling alternatives for related tasks.

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

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

The description discloses that it returns paired data and pipeworx URIs, and replaces many calls. With no annotations, it carries the burden well, though it omits error handling or rate limits.

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

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

The description is concise with four sentences, each adding unique value: main action, type-specific data, return format, and efficiency benefit. No fluff.

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

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

Given no output schema or annotations, the description covers purpose, parameters, return value, and usage context. It could be more complete on data freshness or limitations, but overall sufficient.

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

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

The input schema has 100% description coverage, but the description adds valuable context for each entity type (e.g., revenue for companies, adverse-event counts for drugs), enriching 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 explicitly states the tool compares 2–5 entities side by side, with separate handling for companies and drugs. It distinguishes itself from siblings like resolve_entity by focusing on batch 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 implies usage for multi-entity comparison, noting it replaces 8–15 sequential calls. However, it does not explicitly state when not to use or mention alternatives for single-entity queries.

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 explains that the tool searches by 'describing what you need' and 'Returns the most relevant tools with names and descriptions.' It does not mention any side effects or limitations like pagination, but given the simple read-only nature, the description is adequate.

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 long with no filler. The first sentence states the purpose, the second describes the output, and the third provides usage guidance. Slightly more concise could be achieved, but it is well-structured and 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 that the tool has only 2 simple parameters and no output schema, the description sufficiently covers its purpose and usage. It could mention that the output is a list of tool names and descriptions, which it does, and explains when to call it. No major gaps are evident.

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%: both parameters (query and limit) have descriptions in the schema. The description adds context by providing example queries for the 'query' parameter, which enhances understanding but is not strictly necessary. Baseline 3 is appropriate since the schema already documents both parameters.

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 the resource 'Pipeworx tool catalog', and specifies that it returns tool names and descriptions. It distinguishes this tool from siblings by stating it should be called 'FIRST' to find relevant tools among 500+ available.

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 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on when to use this tool and implies it is a preliminary step before using other tools.

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

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

With no annotations provided, the description fully discloses the tool's behavior: it aggregates data from multiple sources, returns citation URIs, and only supports the 'company' type for now. It also clarifies that names are not supported and advises using resolve_entity first. Missing details on side effects or rate limits, but overall transparent.

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

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

The description is extremely concise, using three sentences to convey purpose, data coverage, output format, and usage guidance. Every sentence adds value with no redundancy. The structure is front-loaded with the main purpose.

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

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

Given the tool's complexity (aggregating multiple data sources) and the absence of an output schema and annotations, the description covers key aspects: input format, included data, output as URIs, and a clear alternative. Could mention output structure more, but sufficient for an agent to decide.

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 meaningful context beyond the schema by explaining that only 'company' is supported, interpreting the 'value' field as a ticker or CIK, and recommending resolve_entity for names. This provides practical usage insights.

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 entity profile across multiple Pipeworx packs in a single call, listing specific data types (SEC filings, revenue, patents, news, LEI) and explicitly distinguishing it from sequential calls. This is specific and leaves no ambiguity about its purpose.

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 not to use this tool by stating 'For federal contracts call usa_recipient_profile directly (too slow to bundle).' It also implies appropriate usage contexts like broad entity lookups, effectively differentiating from 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 must disclose behaviors. It says 'delete' but doesn't specify if operation is permanent, whether confirmation is needed, or any side effects.

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

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

Short and front-loaded, but could be slightly more informative without losing 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?

Tool is simple but missing output schema and behavioral details. For a deletion operation, more context about return value or error handling would be helpful.

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

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

Schema coverage is 100% and parameter description is clear ('Memory key to delete'). Description adds no extra meaning beyond schema.

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

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

Description clearly states verb 'Delete' and resource 'a stored memory by key', which distinguishes it from siblings like 'recall' (read) and 'remember' (write).

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

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

No guidance on when to use this tool vs alternatives. Could mention that it's irreversible or provide context about key validity.

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 explains the process (fetches page, extracts content, emits markdown) and output format, adding substantial context beyond the annotations (readOnly, openWorld, idempotent, non-destructive). No contradictions with annotations.

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

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

The description is concise (three sentences), front-loaded with the core action, and every sentence provides necessary information without redundancy.

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

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

Given the tool's simplicity (2 params, no output schema), the description fully covers purpose, process, output format, and use cases. It does not leave significant gaps.

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

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

With 100% schema coverage, the description adds meaning by explaining the purpose of the URL parameter ('for any URL') and the max_links parameter ('Maximum number of link entries'). The baseline is 3, and the description provides additional context on how these parameters are used in the extraction process.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a given URL, specifying the extraction of title/description/key links and output format. It distinguishes from siblings by its unique purpose, which is not shared by any listed sibling.

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 three concrete use cases: indexing a client's site, drafting for own project, and auditing competitor visibility. However, it does not explicitly mention when not to use the tool or compare it to alternatives, slightly reducing the score from 5.

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 must convey behavioral traits. It discloses the rate limit (5/day) and that it is 'free', but does not detail what happens after feedback is sent (e.g., whether a response is generated or if it's logged). The description is adequate but could be more transparent about internal processing.

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 short sentences, each conveying essential information: purpose, usage guidelines, and rate limit. It is front-loaded, succinct, and contains no fluff. 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, no output schema, and lack of annotations, the description covers the key aspects: what to use it for, how to format feedback, and limits. It could mention that no confirmation or reply is expected, but it is largely complete for a feedback submission tool.

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

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

Schema coverage is 100%, so each parameter is already described. The description adds value by guiding the user to 'describe what you tried in terms of Pipeworx tools/data' and explicitly forbids including the end-user's prompt verbatim, which is a meaningful semantic constraint beyond the schema's placeholder 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 explicitly states the purpose: 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise). This clearly differentiates from sibling tools like ask_pipeworx or discover_tools, which serve different functions.

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 practical guidelines: 'Use for bug reports, feature requests, missing data, or praise' and instructs to 'Describe what you tried... do not include the end-user's prompt verbatim.' It also mentions a rate limit of 5 messages per day, giving the agent contextual constraints. However, it does not contrast with alternatives or explicitly state 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?

Annotations already provide safety profile. The description adds that data is self-aggregating from CF analytics-engine, no PII, and cached 5min-1h, which enriches behavioral understanding beyond annotations.

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

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

The description is concise, front-loaded with the main purpose, and every sentence adds value. 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?

For a simple tool with 1 parameter and no output schema, the description is complete enough: it explains what is returned, caching, and privacy. Minor missing detail on exact response structure, but acceptable.

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

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

Schema covers the single parameter fully (100% coverage). The description adds context about window purpose (short vs long). Extra value is present, so above baseline 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 returns top tools, packs, and call volume over a window, with specific use cases. It differentiates from siblings like discover_tools by focusing on trending activity.

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 (discovering hot data sources, confirming canonical choice, seeing alignment) and explains window implications. It does not explicitly mention when not to use, but context is clear.

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

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

The description reveals behavioral details beyond annotations: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. This adds context about the tool's internal process and output format.

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

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

The description is concise and well-structured: it starts with the purpose, explains the rule, then usage, and ends with return format. Every sentence adds value without redundancy.

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

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

The tool has one required parameter and no output schema. The description covers input, behavior, and output (list of entries), making it complete and self-contained for an agent to use correctly.

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

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

With 100% schema coverage, the parameter 'event' is already described. The description adds practical context (slug or URL) and explains how the parameter is used, complementing the schema effectively.

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

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

The description clearly defines the tool's purpose: finding arbitrage opportunities in Polymarket events via monotonicity violations. It uses specific verbs and resources, distinguishing it from siblings like bet_research or validate_claim.

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 (pass a Polymarket event slug or URL) and the underlying logic. However, it does not explicitly mention alternatives or when not to use it, though the unique function is clear.

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

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

Annotations provide readOnlyHint and openWorldHint, but the description adds significant behavioral detail: it scans top markets, groups by asset, fetches price history once, uses a lognormal model from FRED and coinpaprika, computes model probability, ranks by edge, and returns top N with suggested direction. No contradiction with annotations.

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

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

The description is well-structured, front-loading the core purpose and then detailing the process and output. It is concise but slightly verbose with minor repetition (e.g., 'V1 covers crypto-price bets' could be shorter). Still, each sentence adds value.

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

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

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description is highly complete. It explains the model (lognormal from FRED + coinpaprika), the ranking algorithm, input defaults, and output format (top N ranked by edge with direction). No 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 description coverage is 100% and each parameter (limit, window, min_edge_pp) already has clear descriptions including defaults (e.g., 'Default 10, max 25'). The tool description does not add substantial meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifying the verb (scan/return), resource (Polymarket markets), and outcome (disagreement). It distinguishes from siblings like 'polymarket_arbitrage' and 'bet_research' by focusing on mispricing detection, and explicitly addresses the use case 'what should I bet on today'.

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 discovering mispriced markets without manual paging, but lacks explicit guidance on when not to use it or how it differs from siblings like 'bet_research' or 'polymarket_arbitrage'. It does mention V1 covers crypto-price bets, providing partial scope limitation.

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, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: the tool is non-destructive, returns raw probabilities (0-1) and spreads in percentage points, explains the two modes, and clarifies that explicit tickers override topic-mapped ones. 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 well-structured with the main purpose stated first, then details on modes and return format. It is informative but slightly verbose; each sentence earns its place, though some phrasing could be 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 tool's complexity (cross-venue, two modes, no output schema), the description covers inputs, modes, and output format (raw probabilities and percentage spreads). It explains behavior when both topic and explicit tickers are provided. This is sufficient 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?

Schema description coverage is 100% (all three parameters have descriptions). The description adds value by explaining the two modes (topic vs explicit), providing example values for explicit tickers, and clarifying that explicit parameters override topic mappings. This goes beyond the schema's simple field descriptions.

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

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

The description clearly defines the tool as a cross-venue spread calculator between Kalshi and Polymarket, specifying it returns leg-by-leg prices and spreads. It explicitly distinguishes two usage modes (topic shortcuts and explicit tickers) and mentions the real arb signal, setting it apart 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 explains when to use the tool (to capture arbitrage spreads due to different participant pools) and describes both modes with examples. However, it does not explicitly mention alternatives or when not to use it, though the context is clear enough 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?

The description mentions behavioral aspects like retrieving from 'this session or in previous sessions', implying persistence across sessions. No annotations exist to contradict or supplement. It does not detail what happens if the key does not exist or if listing returns many items. This is adequate but not thorough.

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, each serving a clear purpose: first states functionality, second provides usage guidance. No fluff, front-loaded with the key action.

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 (1 optional parameter, no output schema, no nested objects), the description is complete enough. It explains both retrieval and listing, and mentions cross-session memory. Could note return format, but not critical for a simple key-value retrieval.

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

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

Schema coverage is 100%, so the schema already documents the 'key' parameter well. The description adds value by explaining the two use cases (retrieve vs list) and the consequence of omitting the key. This enriches 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 verb ('Retrieve' / 'list') and resource ('previously stored memory by key'), and distinguishes between retrieving a specific memory vs listing all. It is explicit and 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 explains when to use the tool ('retrieve context you saved earlier') and how to choose between retrieving by key vs omitting key to list all. However, it does not explicitly state when not to use it or compare with siblings like 'forget' or 'remember', though the context is clear enough.

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 reveals that the tool fans out to multiple sources in parallel, accepts specific date formats, and returns structured changes, a count, and URIs. It does not mention read-only nature or any side effects, but the behavioral description is sufficient for an agent to understand what happens upon invocation.

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

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

The description is four sentences long and front-loaded with the core purpose. Each sentence adds essential information: purpose, fan-out behavior, parameter details, return format, and use cases. There is no wasted text.

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 describes the return structure (structured changes, count, URIs). It covers all parameters and usage. However, it could mention potential limitations like result size or time window constraints, which would improve completeness for 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 input schema has 100% description coverage for all three parameters. The description adds significant value by explaining that 'since' accepts ISO or relative formats with examples, that 'type' is limited to 'company', and that 'value' can be a ticker or CIK. This goes well beyond the schema's own 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: finding what's new about an entity since a given time. It specifies the entity type (company) and details the parallel fan-out to SEC EDGAR, GDELT, and USPTO. It distinguishes itself from sibling tools like entity_profile or compare_entities, which serve different functions.

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 using this tool for 'brief me on what happened with X' or change-monitoring workflows, providing clear usage context. It does not mention alternatives or exclusions, but given the sibling list, no direct competitor exists for this specific change-tracking functionality.

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 memory is persistent for authenticated users and lasts 24 hours for anonymous sessions, which is useful behavioral context beyond the schema. It does not mention any limits on number of keys or size, but overall transparency is good.

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

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

Three sentences, each serving a distinct purpose: action statement, use-case guidance, and persistence detail. No redundant or extraneous text.

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 is simple (2 required string params, no output schema, no nested objects). The description covers purpose, usage, and behavioral notes. Missing potential details like max key length or whether values can be overwritten, but for the complexity, it is reasonably complete.

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

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

Schema description coverage is 100% (both parameters have descriptions). The tool description adds context about the value being any text and provides example keys (like 'subject_property'), adding meaningful usage guidance beyond the schema. Score 4 because it adds some value but schema already covers basics.

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'), the resource ('key-value pair in session memory'), and the purpose ('save intermediate findings, user preferences, or context across tool calls'). It distinguishes from siblings like 'forget' and 'recall' by focusing on storage.

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 (to save findings, preferences, context across calls). It mentions persistence differences between authenticated users and anonymous sessions, but does not explicitly say when not to use it or name 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 full burden. It discloses the return format (ticker, CIK, company name, pipeworx:// URIs) and acceptable input formats, but does not mention idempotency, safety, authorization, or rate limits. For a data retrieval tool, this is adequate but not thorough.

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, with the first sentence stating the core purpose and the second providing specifics. It is tightly written with no extraneous information.

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

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

Given the simple tool (2 params, no output schema, no annotations), the description covers purpose, inputs, outputs, and rationale (replaces 2-3 calls). It is sufficiently complete for an agent to select and 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 coverage is 100%, providing baseline 3. The description adds value by clarifying 'v1 type='company'', specifying that 'value' can be ticker, CIK, or name, and giving examples like 'AAPL' and 'Apple'. This exceeds schema documentation.

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. It specifies the verb 'resolve' and the resource (entity), and distinguishes from siblings by noting it replaces 2-3 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 provides usage context: 'in a single call' and 'replaces 2-3 lookup calls'. It also notes v1 supports type='company'. However, it does not explicitly mention when not to use it or alternative tools, though it implies a use case.

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

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is clear. The description adds behavioral details: probes each entity with ai_visibility_check, ranks by score, and returns score/confidence/signal density per entity. It also explains the optional _apiKey for Anthropic model. This adds value beyond annotations, though it could mention potential rate limits or execution time implications.

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 plus a parenthetical use case. Every sentence adds value: first defines the action, second elaborates on method and output. The use case example is concise and illustrative. No unnecessary words or repetition.

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 tool with 4 parameters, no output schema, but rich annotations, the description adequately covers the tool's purpose, method, and output. It mentions the ranked list with score/confidence/signal density, and explains the context parameter for disambiguation. It is sufficient for an agent to understand usage, though the exact output structure could be more detailed.

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 each parameter having a description in the schema. The tool description does not add any new parameter information beyond what is already in the schema. Baseline for high coverage is 3, and the description does not provide extra semantics.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, uses ai_visibility_check, ranks, and returns a ranked list with score/confidence/signal density. It distinguishes itself from the sibling ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the AI visibility focus and multi-entity batch probe.

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 suggests use for 'competitive AI-marketing audits' and gives an example question ('does Claude know about us as well as our competitors?'). It implies when to use (multi-entity comparison) but does not explicitly state when not to use or list alternative tools for single-entity checks, 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?

The description outlines the main steps but lacks details on behavior such as error handling, reliability, or limitations. The phrase 'Rigorous methodology' is too vague to provide meaningful transparency.

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

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

The description is two sentences and front-loads the core functionality. However, the second sentence 'Rigorous methodology.' adds little value and could be removed for better 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 simple behavior and one parameter, the description is mostly adequate. However, it omits details about the output format (beyond 'last two digits'), city selection methodology, and potential failure modes.

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 already fully describes the 'unit' parameter with an enum and description. The description adds no additional meaning about the parameter beyond what the schema provides.

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

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

The description clearly states the specific action: fetches current temperature from 10 cities worldwide, sums them, and returns the last two digits. This unique process distinguishes it from all sibling tools.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or conditions for use.

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

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

No annotations provided, so the description carries the full burden. It lists the return values (verdict, structured form, actual value, citation) but does not explicitly state whether the tool is read-only or has side effects. It lacks explicit safety or permission details.

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, concise and front-loaded. Every sentence adds meaningful information without redundancy.

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

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

For a simple tool with one parameter and no output schema, the description covers input format, domain, output components, and efficiency benefit. It lacks only minor details like error handling or limitations.

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

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

The single parameter 'claim' is fully described in the schema with examples. The description adds value by specifying the domain scope (company-financial claims), which the schema alone does not 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's purpose: fact-check a natural-language claim against authoritative sources. It specifies the domain (company-financial claims) and data sources (SEC EDGAR + XBRL). This distinguishes it from sibling tools like ask_pipeworx or compare_entities.

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

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

The description implies when to use (for verifying financial claims) and mentions it replaces multiple sequential agent calls, suggesting efficiency. However, it does not explicitly state when not to use it or mention alternatives for non-financial claims.

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