Urlscan Io

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds significant context: it routes questions, fills arguments, returns structured answers with stable pipeworx:// citation URIs. Does not contradict annotations. However, it does not detail potential latency or edge cases, so not a 5.

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

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

Description is a single paragraph but well-structured: starts with directive, lists domains and usage cues, ends with examples. Every sentence adds value. Could be slightly more concise but remains clear and 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 (routing to 2,520 tools) and no output schema, description fully explains expected behavior (structured answers with citations), when to use, and the type of queries it handles. Complete for an agent to decide invocation.

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

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

Input schema has one parameter 'question' with generic description. The tool description compensates richly by explaining what kinds of questions are suitable, listing domains, and giving examples. This adds high value beyond the schema.

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

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

Description clearly states the tool routes factual questions to over 2,500 tools and returns structured answers with citations. It distinguishes itself from siblings like web search by explicitly saying 'PREFER OVER WEB SEARCH' and provides specific domains and examples.

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

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

Explicitly tells when to use (factual questions about real-world entities, current/historical data, specific query types like 'what is', 'look up') and implies when not to use (non-factual or vague queries). Provides examples to guide the agent effectively.

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

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

Annotations already mark readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds key behavioral details: it resolves the market, classifies the bet, fans out to appropriate packs based on bet type, and returns a 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?

Six sentences, each providing essential information. The first sentence immediately states the core function. 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?

No output schema, but description adequately explains outputs (evidence packet + market-vs-model comparison). Covers input formats, processing logic, use cases, and bets types. Given the complexity of fan-out based on bet type, the description is complete.

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

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

Schema coverage is 100%, baseline 3. The description adds meaning to both parameters: market parameter explains it accepts slug, URL, or question text (beyond schema), and depth parameter explains quick vs thorough with specific source counts and default (thorough). This enriches understanding.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data, specifying inputs (slug, URL, or question) and outputs (evidence packet and comparison). It distinguishes itself from sibling tools like ask_pipeworx by positioning as a higher-level 'core demo product' that automates pack discovery.

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 use cases are given ('should I bet on X?', 'what does the data say...', 'is there edge...'). It implies this is the go-to tool for bet research but does not explicitly state when not to use it 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?

Annotations already indicate read-only, non-destructive behavior. Description adds context on specific data sources (SEC EDGAR/XBRL, FAERS) and return format (paired data + pipeworx:// URIs). No contradictions found.

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

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

Single dense paragraph with no wasted words. Starts with core purpose, then usage guidance, then detailed per-type behavior, then return format.

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 lacking an output schema, the description thoroughly explains what data is returned per type and includes data sources. For a tool with two parameters and clear purpose, this is complete.

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

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

Schema coverage is 100%, and the description adds significant meaning: explains each enum value's specific data (revenue etc. for company, adverse events etc. for drug) and clarifies the format of the values array (tickers vs drug names).

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?

States specific verb 'compare', resource '2–5 companies or drugs', and distinguishes from siblings by noting it replaces 8–15 sequential agent calls. Provides concrete user query examples.

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

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

Explicitly states when to use with example queries ('compare X and Y', 'X vs Y', etc.) and what data is pulled for each type. Does not explicitly mention when not to use, but the differentiation from siblings 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 already mark as read-only and non-destructive. The description adds context about returning top-N relevant tools and being a meta-tool, but does not disclose any additional behavioral traits beyond that.

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

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

The description is a single well-organized paragraph that starts with the core purpose, then provides usage guidance and examples, with no wasted words.

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

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

No output schema exists, but the description explains that it returns 'top-N most relevant tools with names + descriptions', which is sufficient given the tool's simplicity (2 params, no nested objects).

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 have schema descriptions (100% coverage). The description reinforces that 'query' is natural language and 'limit' controls the number of results, adding value beyond the schema.

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

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

The description clearly states 'Find tools by describing the data or task' and provides specific examples of use cases (SEC filings, FDA drugs, etc.), making it distinct from sibling tools like search or entity_profile.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and advises 'Call this FIRST when you have many tools available and want to see the option set', providing clear when-to-use guidance.

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

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

The description adds no behavioral context beyond what annotations already provide (readOnlyHint, openWorldHint, destructiveHint). Since annotations already convey read-only and non-destructive nature, the description misses an opportunity to explain aspects like result variability (open world) or expected data 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?

At three words, the description is extremely concise but fails to earn its place: it essentially restates the parameter and does not provide enough information to justify its brevity. A bare minimum description for a single-parameter tool could include usage hints.

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 a simple input schema and no output schema, the description should explain what type of information is returned or how the domain parameter is processed. It is incomplete, leaving the agent uninformed about the tool's behavior and results.

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 0% description coverage for the required 'domain' parameter, and the description only repeats the parameter name without adding meaning (format, example, constraints). The tool's name and description together imply a web domain, but no explicit guidance is given.

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 'Search by domain' clearly states the action (search) and the resource (domain), making the tool's purpose understandable. However, it does not differentiate from sibling tools like 'search' or 'entity_profile' which might also perform domain searches, so it lacks explicit sibling differentiation.

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 (e.g., 'search' or 'resolve_entity'). There are no exclusions or context given for appropriate usage, leaving the agent without decision support.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: it returns specific data types (SEC filings, revenue, patents, news, LEI) with pipeworx:// citation URIs, and notes that person/place types are coming soon. However, the description doesn't elaborate on rate limits or pagination, but overall it enhances transparency beyond annotations.

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

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

The description is efficient, with two sentences that front-load purpose and usage. The second sentence packs a lot of detail (outputs, citation URIs) without being overly verbose. It could be slightly more concise by breaking into bullet points, but it remains clear and purposeful.

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, the description thoroughly explains what the tool returns (SEC filings, fundamentals, patents, news, LEI). It covers the main use case and provides enough context for an AI agent to understand the tool's capabilities.

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%, but the description adds critical meaning: for 'type', it clarifies only 'company' is supported; for 'value', it specifies ticker or zero-padded CIK, and importantly notes that names are not accepted and suggests using resolve_entity first. This prevents the agent from passing unsupported inputs.

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 a specific verb-resource combination ('Get everything about a company') and distinguishes it from siblings by noting it replaces calls to 10+ other tools. It also lists concrete query examples and the domains covered (SEC, USPTO, news, etc.).

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 specifies when to use the tool (user asks for company profile, research) and when not to (if only a name is provided, use resolve_entity first). It also implicitly contrasts with many other tools by listing the data sources it consolidates.

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 include destructiveHint=true, and the description adds context about clearing sensitive data and stale context, without contradicting annotations.

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

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

Two sentences with immediate action verb, no unnecessary words, perfectly 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?

Simple tool with one parameter and no output schema; description covers purpose, usage, and sibling pairing completely.

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 merely echoes 'by key' without adding new constraints or format details 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?

Clearly states 'Delete a previously stored memory by key', specifying the action and resource. Distinguishes from siblings 'remember' and 'recall'.

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

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

Provides explicit when-to-use scenarios ('when context is stale, the task is done, or you want to clear sensitive data') and suggests pairing with other tools.

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

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

Annotations already provide readOnlyHint and openWorldHint, but the description adds no behavioral context (e.g., what results to expect, 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 extremely concise (one sentence), but it fails to provide necessary information, making it under-informative rather than efficiently concise.

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

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

Given no output schema, the description should explain what 'search' returns; it does not, leaving the tool incomplete for an agent to use confidently.

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 'ip' has no schema description and the tool description provides no additional details about format or constraints, leaving the agent uninformed.

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 'Search by IP' clearly states the verb (Search) and resource (IP), but it does not differentiate from sibling tools like 'domain' or 'search'.

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

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

No guidance on when to use this tool versus alternatives, no context about prerequisites or limitations.

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

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

The description adds valuable behavioral context beyond annotations: it discloses rate limiting (5 per identifier per day), that it's free and doesn't count against quota, and that feedback is read daily and affects roadmap. Annotations are minimal (readOnlyHint, openWorldHint, destructiveHint all false), so description carries the burden well.

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

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

The description is a single paragraph but well-structured: opening sentence states purpose, then categorizes use cases, then gives guidance on description format, then impacts. It's not overly long for the information conveyed, though it could be split into bullet points for easier reading.

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 (3 params, no output schema), the description covers purpose, usage scenarios, parameter guidance, rate limits, and note on impact. It is nearly complete, though a brief note on expected response (e.g., 'no direct response') would make it fully complete.

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

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

Schema coverage is 100%, but the description adds usage guidance: it explains each enum value for 'type', gives tips for 'message' (be specific, length limit), and clarifies that 'context' is optional and what it contains. This adds meaning beyond the schema's basic descriptions.

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

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

The description clearly states the tool is for providing feedback (bug, feature, data_gap, praise) to the Pipeworx team. It distinguishes from sibling tools like ask_pipeworx and discover_tools by focusing on reporting issues or suggestions.

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

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

It explicitly describes when to use (for bugs, features, data gaps, praise) and provides a brief exclusion (don't paste end-user's prompt). However, it doesn't explicitly mention when not to use or provide alternatives beyond the context of feedback.

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 internal behavior: walking child markets, searching across events, grouping, and checking monotonicity. Returns ranked opportunities with trade direction and reasoning. Annotations confirm readOnly and non-destructive, so no contradiction.

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

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

The description is concise and well-structured: first states the high-level purpose, then details the two modes with examples, explains the advantage of cross-event mode, and ends with output format. Every sentence adds value.

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

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

Given the tool's complexity and lack of output schema, the description covers all necessary aspects: purpose, modes, input parameters, behavioral details, and output (ranked opportunities with reasoning). 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?

Both parameters ('event' and 'topic') are described in the schema with examples. The description adds value by explaining the modes and providing concrete examples like 'when-will-bitcoin-hit-150k' and 'Strait of Hormuz traffic returns to normal', 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 verb (find arbitrage), resource (Polymarket markets), and method (monotonicity violations). It distinguishes two modes (event and topic), making its purpose distinct from siblings like 'polymarket_edges' and '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?

The description explicitly explains when to use each mode: 'event' for a single Polymarket event slug and 'topic' for cross-event searches. It highlights that cross-event mode catches cases that single-event mode misses, providing clear context for 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?

Annotations already declare readOnlyHint and non-destructive nature. The description adds significant behavioral context: scanning top markets, grouping by asset, fetching price history once, using a lognormal model from FRED and live coinpaprika price, computing model probability, and ranking by edge. No contradictions with annotations.

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

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

The description is front-loaded with the main action and then details model, process, and output. It is slightly verbose but each sentence contributes useful information; could be tightened 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?

Despite no output schema, the description adequately explains the output (top N ranked by edge magnitude with suggested trade direction). It covers complexity, model details, data sources, and aggregation strategy, making the tool fully understandable for correct invocation.

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

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

Schema coverage is 100% with descriptions for all three parameters (limit, window, min_edge_pp). The description merely repeats defaults already in schema, adding no new semantic meaning beyond what is already structured.

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, contrasts with market price using Pipeworx data, and returns top edges with trade direction. It distinguishes from siblings like bet_research and polymarket_arbitrage by focusing on edge discovery for betting opportunities.

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

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

The description explicitly says it's built for the 'what should I bet on today' question, indicating when to use. However, it does not explicitly mention when not to use or list alternatives beyond implied differentiation from sibling 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 mark readOnlyHint=true and destructiveHint=false. Description adds scoping ('anonymous IP, BYO key hash, or account ID') and behavior when key omitted ('list all keys'). 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?

Four sentences: first sentence states core action, second gives use case, third explains scoping, fourth mentions related tools. Every sentence adds value, 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?

No output schema exists, but description omits return format (string vs list) and error handling (missing key). Annotations cover safety, but return value information would help.

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 key parameter with description. Description adds crucial context: 'omit to list all keys', explaining the effect of omitting the parameter beyond schema definitions.

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

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

The description specifies the verb 'retrieve' or 'list' and the resource 'value previously saved' or 'all saved keys'. It distinguishes from siblings like 'remember' and 'forget'.

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

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

Explicitly states when to use: 'to look up context the agent stored earlier... without re-deriving it from scratch'. It also mentions scoping and related tools, but lacks explicit when-not-to-use scenarios.

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

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

The description adds valuable behavioral context beyond annotations: it explains that it fans out to three specific sources (SEC, GDELT, USPTO) in parallel, and describes the return format. Annotations already indicate read-only and non-destructive nature, so no contradiction.

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

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

The description is concise (4-5 sentences), front-loaded with purpose and examples, and every sentence adds value. 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?

Given no output schema, the description partially compensates by stating return format (structured changes, count, URIs). However, it could detail the structure of changes or how results from multiple sources are combined.

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

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

Schema coverage is 100%, so baseline is 3. The description adds useful examples for 'since' (ISO date and relative shorthand) and clarifies 'value' can be ticker or CIK, 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's purpose: finding recent changes for a company. It provides explicit example queries and differentiates from siblings by focusing on time-windowed updates.

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

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

The description gives clear when-to-use guidance through examples. However, it does not explicitly state when not to use it or mention alternatives among siblings, leaving some ambiguity.

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

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

Annotations indicate readOnlyHint=false (write operation) and destructiveHint=false. Description adds that storage is key-value pair, scoped by identifier, with persistence (authenticated persistent, anonymous 24-hour). No contradictions, and provides useful behavioral context beyond annotations.

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

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

The description is well-structured: a clear first sentence stating purpose, then usage guidance, then behavioral details. Every sentence contributes value, 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?

For a simple tool with 2 required params and no output schema, the description covers purpose, usage, behavioral transparency, and parameter semantics thoroughly. It is complete and self-contained.

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

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

Schema coverage is 100%, so baseline is 3. Description mentions key-value pair and gives usage examples, but the schema descriptions for key and value are already clear. Description adds marginal extra meaning.

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 'Save data the agent will need to reuse later', specifying the action and resource. It distinguishes from sibling tools recall and forget by mentioning them, and provides examples of what to store (resolved ticker, target address, etc.).

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

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

Explicitly says 'Use when you discover something worth carrying forward', and pairs with recall and forget for retrieval and deletion. It also details scoping by user identifier and persistence differences between authenticated and anonymous sessions.

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

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

Annotations already indicate read-only and non-destructive behavior. Description adds that it returns IDs plus citation URIs, which is extra behavioral context. No contradictions and no misleading statements.

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?

Four sentences with no redundancy. Purpose first, then usage, then examples, then benefit. Every sentence earns its place; 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?

Comprehensive for a lookup tool with two simple params and no output schema. Minor gap: doesn't mention behavior on unresolvable entities or error handling, but otherwise 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 covers 100% of parameters with descriptions, but the description adds significant meaning with examples and acceptable formats (e.g., ticker, CIK, name for company). This goes beyond schema.

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

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

Description clearly states the tool looks up canonical/official identifiers for companies or drugs, providing specific examples (Apple to AAPL, Ozempic to RxCUI) and listing ID systems (CIK, ticker, RxCUI, LEI). It differentiates from siblings by focusing on identifier resolution for other tools.

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

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

Explicitly tells when to use (when needing official identifiers) and directs to use before other tools. States it replaces 2-3 lookup calls, giving clear context and alternatives.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds little beyond stating 'keyless'. No behavioral traits beyond safety are disclosed, such as pagination, performance, or data freshness.

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

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

Single sentence, no fluff, front-loaded with key idea. However, it sacrifices completeness for brevity.

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

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

No output schema, no parameter details beyond schema, and no explanation of 'Full result' or 'keyless'. The agent cannot reliably interpret the result structure or 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?

Parameter schema has 0% coverage (no description for 'uuid'), and the description only repeats 'by scan uuid' without adding format, source, or constraints. The agent gains no extra understanding of what 'uuid' represents.

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

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

Description states 'Full result by scan uuid (keyless)', indicating retrieval of a result based on a scan UUID. However, it lacks a clear verb (e.g., 'get' or 'retrieve') and does not differentiate from sibling tools like 'domain' or 'entity_profile', making the purpose somewhat ambiguous.

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

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

No guidance on when to use this tool versus alternatives, nor any context about prerequisites or exclusions. The description is purely functional without usage advice.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds 'keyless' which implies no authentication required, a behavioral trait beyond the annotations. However, it does not discuss pagination, result format, or other behavioral aspects.

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

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

The description is a single, front-loaded sentence with no redundant words. It is concise, though it could benefit from a brief expansion on parameter usage without becoming verbose.

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

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

The tool has three parameters and no output schema, but the description does not explain pagination (size, search_after) or the nature of 'past scans'. This is insufficient for an agent to invoke the tool correctly without external knowledge.

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

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

With 0% schema description coverage and no parameter descriptions in the text, the description adds zero meaning to the three parameters (query, size, search_after). The user must infer their usage from the tool name alone.

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

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

The description 'Search past scans (keyless)' clearly identifies the action (search) and resource (past scans), and adds the qualifier 'keyless' to indicate no API key required. However, it does not distinguish this tool from siblings like 'domain' or 'ip' which also perform searches.

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 'bet_research' or 'compare_entities'. There are no explicit when/when-not conditions or references to sibling tools.

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

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

Description says 'submit' and 'scanning', implying a write operation, contradicting readOnlyHint: true annotation. No additional behavioral context.

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

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

Single sentence is concise but lacks necessary detail on authentication and behavior.

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

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

With 7 parameters, low schema coverage, no output schema, and annotation contradiction, the description is insufficient.

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 only 29%; description does not add meaning for the 5 undocumented 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?

Description clearly states it submits a URL for scanning, with a specific verb and resource, distinguishing it from 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?

Mentions 'requires key' but provides no guidance on when to use versus alternatives, or when not to use.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral details: returns verdict types, structured form, actual value with citation, and percent delta. Also notes performance improvement over sequential calls. No contradiction.

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

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

Concise single paragraph containing all key information, but could be better structured (e.g., bullet points for returned values). 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?

No output schema, but the description explains return types, citation, and delta. Covers use case, domain, and limitations. Lacks explicit error handling or out-of-scope 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%. The description adds domain context and examples but does not significantly expand beyond the schema parameter description. 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 identifies the tool as a fact-checker for natural-language claims, specifies the domain (company-financial claims for US public companies), and distinguishes it from siblings by noting it replaces 4-6 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?

Explicitly states when to use ('when an agent needs to check whether something a user said is true') and provides example queries. Implicitly limits scope to v1 company-financial claims, but does not explicitly list when not to use or alternatives.

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