Public Suffix List

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

The description discloses that the tool routes questions to specialized tools, fills arguments, and returns structured answers with citations. Annotations already indicate readOnlyHint=true and destructiveHint=false, and the description aligns with these, adding context about the routing and citation behavior without contradiction.

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

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

The description is a single paragraph that front-loads the key directive 'PREFER OVER WEB SEARCH'. While it is somewhat long, every sentence adds value, listing example sources and question types. It could be slightly more concise but is well-structured.

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

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

Given the simple input schema (one parameter) and no output schema, the description is complete. It explains the tool's purpose, usage guidelines, and behavioral details (routing, citations), leaving no significant gaps for the agent to infer.

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 description is adequate. The description adds meaning by specifying the scope of questions (factual, authoritative data) and the routing behavior, which goes beyond the simple schema description of 'Your question or request in natural language'.

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: answering factual questions about current/historical data from authoritative sources. It distinguishes itself from web search with the explicit 'PREFER OVER WEB SEARCH' directive and provides specific examples like SEC filings, FDA data, and economic statistics.

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 directly advises when to use this tool ('PREFER OVER WEB SEARCH') and gives a comprehensive list of question types and examples. It does not explicitly state when not to use it, but the alternative (web search) is implicit. The guidance is strong but could be more precise about exclusions.

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 behavior beyond annotations: fan-out logic based on classification (e.g., crypto+fred+gdelt for Bitcoin bets), and return of an evidence packet plus market-model comparison. It aligns with annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) and adds valuable 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?

The description is front-loaded with the main action and packs significant information into a few sentences without redundancy. Each sentence adds value, and the structure is logical.

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 adequately explains the return value: 'evidence packet plus a simple market-vs-model comparison.' It also contextualizes the tool as the core demo product, giving the agent a clear picture of when to invoke it.

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 meaning: it explains that 'market' can be a slug, URL, or question text, and 'depth' defaults to thorough. This goes beyond the schema's enum and string descriptions, providing practical usage guidance.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies actions (resolve market, classify bet, fan out to packs) and distinguishes from siblings like 'polymarket_arbitrage' and 'polymarket_edges' by positioning itself as the comprehensive 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?

Explicit use cases are provided: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' It implicitly contrasts with sibling tools by stating agents convert better with this tool than discovering packs themselves. However, it does not 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 indicate readOnlyHint=true and destructiveHint=false, which are consistent. The description adds transparency by detailing data sources (SEC EDGAR/XBRL, FAERS, FDA, clinicaltrials.gov) and the return format (paired data with citation URIs). 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 informative but somewhat lengthy. However, each sentence serves a purpose: defining the tool, giving usage examples, explaining data sources, and noting its efficiency. It earns a 4 for being thorough without being overly 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?

Despite lacking an output schema, the description mentions that the tool returns 'paired data + pipeworx:// citation URIs', which gives the agent a clear expectation of the output format. For a comparison tool, this is sufficient context.

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

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

Schema coverage is 100%, and the description adds significant context: it explains that 'values' are tickers/CIKs for company type and drug names for drug type, and reinforces min/max constraints. This helps the agent understand parameter semantics 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 that the tool compares 2-5 companies or drugs side by side, and provides specific examples of user queries that trigger its use. It distinguishes between company and drug types, making the purpose unambiguous.

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

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

The description explicitly lists use cases ('compare X and Y', 'X vs Y', etc.) and mentions that it replaces multiple sequential agent calls, implying efficiency. However, it does not explicitly state when not to use this tool, but the examples provide clear 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds that it returns 'the top-N most relevant tools with names + descriptions', but does not disclose additional behavioral traits like rate limits, authentication requirements, or performance characteristics. With annotations covering safety, a score of 3 is appropriate as the description adds some value but not rich 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?

The description is front-loaded with the core purpose and includes necessary context about usage and return type. It is slightly verbose with the list of domains, but each sentence serves a purpose. It could be trimmed slightly without losing meaning, but overall it is well-structured and clear.

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 this is a simple search/discovery tool with no output schema, the description adequately explains what the tool does and what it returns (top-N relevant tools with names and descriptions). It provides enough context for an agent to decide when to use it. No additional information is necessary for completeness.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds examples of natural language queries (e.g., 'analyze housing market trends') which adds some value beyond the parameter descriptions. However, it does not provide additional semantics such as formatting or constraints beyond what the schema already states. Hence, a score of 3 is justified.

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 find tools by describing a data or task. It uses specific verbs like 'browse, search, look up, or discover' and specifies the resource as tools. It distinguishes itself from siblings by advising to call it first for option set exploration, making its unique role explicit.

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

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

The description provides clear usage context: use when browsing or discovering tools for specific domains. It advises calling this tool 'FIRST when you have many tools available and want to see the option set', implicitly suggesting when not to use it (when you know the specific tool). However, it does not explicitly name alternative sibling tools for specific tasks, which would have elevated the score to 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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context by listing the specific data returned (SEC filings, financials, patents, news, LEI, citation URIs) and implies comprehensiveness. 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 each sentence adds value. It front-loads the main purpose, provides usage context, and then details the return data. It could be slightly more concise, but it is well-structured.

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

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

Given no output schema, the description fully explains the return values (SEC filings, financials, patents, news, LEI, citation URIs). It also provides context on when to use this tool versus alternatives. All necessary information for an agent is present.

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% for both parameters, and the description adds significant meaning beyond the schema: it specifies that only 'company' type is supported, that value must be a ticker or zero-padded CIK, and that names are not supported (directing to resolve_entity).

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: 'Get everything about a company in one call.' It uses a specific verb ('get') and resource ('everything about a company'), and explicitly distinguishes it from siblings by noting it replaces 10+ other pack 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?

The description provides clear usage examples ('tell me about X', 'give me a profile of Acme', etc.) and gives guidance on when to use an alternative tool (use resolve_entity if only have a name). However, it does not explicitly state when not to use the tool beyond the name resolution 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 mark the tool as destructive (destructiveHint=true). The description adds behavioral context by specifying the conditions for deletion (stale context, sensitive data). While it does not contradict annotations, it provides additional rationale beyond the hint.

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, each serving a purpose: the first states the action, the second gives usage guidance. No redundant or extraneous information, highly 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?

For a simple deletion tool with good annotations and schema, the description covers purpose, usage, and sibling relationships. It does not specify the return value or confirm that deletion is irreversible, but given the destructive hint and context, it is mostly complete.

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

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

The input schema has 100% coverage with a description for the 'key' parameter. The tool description does not add any extra meaning or constraints beyond 'by key', so it meets the baseline but does not exceed it.

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

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

The description clearly states the action 'Delete' and the resource 'memory by key'. It distinguishes itself from sibling tools (remember and recall) by specifying deletion, and even suggests pairing with them, making its role unambiguous.

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

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

The description provides explicit scenarios for use: when context is stale, task is done, or clearing sensitive data. It mentions pairing with remember and recall, but does not explicitly state when not to use it, which is a minor gap.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe. The description adds that the output contains 'last refresh + rule count', but does not explain openWorldHint or other behaviors beyond what annotations provide.

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 short phrase), which is efficient, but the lack of clarity reduces its effectiveness. It earns its place but could be more informative.

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

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

For a zero-parameter tool without output schema, the description is minimally adequate. However, it could specify what 'last refresh' refers to (e.g., timestamp) and what 'rule count' means, to ensure complete understanding.

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?

There are zero parameters, so baseline is 4 as per guidelines. The description adds no parameter info because none exist.

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 'Last refresh + rule count' vaguely indicates that the tool returns information about the last refresh and a rule count, but lacks a clear verb+resource structure. It does not explicitly say 'list' or 'get', which may confuse the agent.

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 its siblings (e.g., 'ask_pipeworx', 'bet_research'). The agent receives no context about appropriate use cases.

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

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

Annotations already mark the tool as readOnlyHint=true and destructiveHint=false, so the description is not burdened with safety disclosure. It adds value by specifying the output structure (three components). However, it does not discuss behavior for invalid domains or edge cases.

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, concise sentence that efficiently communicates the tool's purpose and output. No unnecessary words or 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 domain parsing tool with one parameter and no output schema, the description sufficiently explains the transformation. It could mention input format (e.g., whether protocol is accepted) or error handling, but the core functionality is clear.

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

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

The input schema declares a required parameter 'domain' of type string with no description. The tool description states 'Split a domain', which indirectly clarifies the parameter's role. With 0% schema description coverage, the description compensates minimally. It does not add format constraints or examples.

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

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

The description uses a specific verb 'split' and resource 'domain', and clearly lists the three output components: subdomain, registrable_domain, public_suffix. This distinguishes it from sibling tools like 'public_suffix' and 'registrable_domain' which only return single components.

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 the tool is for decomposing a domain into all its parts, but it does not explicitly state when to use it vs. alternatives. For example, it could note that if you need only the public suffix, use 'public_suffix' instead. Lacks exclusionary 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 significant behavioral context beyond annotations: it discloses rate limits (5 per identifier per day), that it's free and doesn't count against quota, and that the team reads digests daily with impact on roadmap. No contradiction with annotations.

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

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

The description is concise yet comprehensive, front-loading the purpose and then efficiently covering usage guidelines, behavioral notes, and parameter hints. 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 (3 params, no output schema), the description fully equips an agent to select and invoke it correctly. It covers when to use, how to format input, limitations, and expected impact, making it self-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?

Schema coverage is 100%, with each parameter well-described. The description adds value by elaborating on the 'type' enum meanings and advising on message content (be specific, 1-2 sentences, 2000 chars max), which augments the schema without redundancy.

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: reporting broken, missing, or needed items to the Pipeworx team. It enumerates specific use cases (bug, feature/data_gap, praise) and distinguishes itself from siblings like ask_pipeworx and discover_tools by focusing on feedback rather than queries.

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

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

The description explicitly states when to use the tool (for bugs, feature requests, data gaps, praise) and provides guidance on how to frame the feedback (describe in terms of tools/packs, avoid pasting end-user prompts). It also mentions rate limits and queuing behavior, giving clear operational context.

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

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

Annotations already indicate read-only, open-world, non-destructive. The description adds significant behavioral detail: how each mode works (walks child markets vs. searches across events), grouping logic, and output (ranked opportunities with trade direction and reasoning). No contradictions.

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

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

The description is well-structured with a clear first sentence, then TWO MODES in a list-like format (though in prose). It is front-loaded and each clause adds value, though slightly verbose. Still effective.

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

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

Given the complexity (two modes, no output schema), the description fully explains what the tool does, what inputs it expects, how it behaves, and what outputs it returns (ranked opportunities with reasoning). No 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?

Schema coverage is 100% with basic descriptions. The tool description enriches both parameters: for event it specifies slug or full URL, for topic it provides an example and explains the cross-event search behavior. This adds meaningful context beyond the schema.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity checks. It distinguishes two modes (event and topic) with specific use cases, making the purpose highly specific and actionable.

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 tool provides explicit guidance on when to use event vs topic mode, including a concrete example of cross-event scenarios. It does not explicitly mention alternatives but the context signals show distinct sibling tools; the guidance is clear and practical.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable context beyond annotations, such as the process (groups by asset, fetches price history once, computes model probability, ranks by |edge|) and scope (covers crypto-price bets). It does not mention potential limitations like model assumptions or error cases, but is largely 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 concise yet comprehensive, with each sentence serving a purpose: main action, model explanation, process, and use case. It is front-loaded and free of 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 the tool's complexity, the description explains the underlying model, process, and output (returns top N with trade direction). While no output schema exists, the description gives sufficient context for an agent to understand the tool's behavior. Minor omission: explicit return field structure is missing, but overall 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?

Input schema coverage is 100% and each parameter is described in both schema and description, but the description merely repeats the schema descriptions without adding new meaning. Baseline for high coverage is 3, and the description does not elevate beyond that.

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 to find discrepancies between market price and Pipeworx model probability, with a specific verb 'scan and return' and resource 'Polymarket markets'. It distinguishes from siblings by focusing on model-based edge discovery rather than arbitrage or general queries.

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 ties the tool to the 'what should I bet on today' question, providing clear context for when to use it. However, it does not explicitly state when not to use it or mention alternatives, so it lacks exclusions.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context. It does not mention return format, side effects, or constraints, leaving the agent uninformed about runtime behavior.

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

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

At four words, the description is excessively terse. While brevity is valued, it sacrifices essential clarity and functional details, making it under-specified rather than 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?

Even for a simple tool with one parameter and no output schema, the description should clarify what the tool returns or does. The complete lack of such information leaves the tool fundamentally incomplete for agent decision-making.

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 a single parameter 'domain' with no description, and the tool description provides no explanation of what the parameter expects (e.g., format, examples). With 0% schema coverage, the description fails to compensate.

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 'Just the public suffix' is vague and lacks a verb or clear action. It does not specify whether the tool extracts, validates, or returns the public suffix, making it difficult for an agent to understand the tool's function.

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

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

The description provides no guidance on when to use this tool versus alternatives. Given the sibling tool 'registrable_domain', an explicit distinction would be helpful, but none is offered.

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, destructiveHint=false. Description adds scoping to identifier and behavior when key omitted, providing extra context without 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?

All sentences serve clear purpose; front-loaded with main action. No redundant 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 simple read tool with good annotations, description covers return behavior, scoping, and sibling pairing adequately.

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 100% and schema already states 'omit to list all keys'. Description adds scoping context but not much beyond schema for the parameter itself.

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

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

Description clearly states the tool retrieves a saved value or lists all keys, using specific verbs 'Retrieve' and 'list'. It distinguishes itself from siblings 'remember' and 'forget'.

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

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

Explicitly tells when to use: 'look up context the agent stored earlier' and mentions pairing with remember/forget. Lacks explicit when-not scenarios, 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?

Annotations already provide readOnlyHint (true), openWorldHint (true), destructiveHint (false). The description adds behavioral details: parallel fan-out to three sources (SEC, GDELT, USPTO), accepted date formats, and returned fields. No contradictions.

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

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

The description is a single paragraph that efficiently conveys purpose, usage examples, technical details, and return format without wasted words. Every sentence contributes meaningfully.

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

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

The description covers the tool's core functionality, data sources, parameter formats, and return structure. It is adequate for an agent to understand selection and invocation, though it omits edge cases like empty results or pagination. Overall, it is sufficient given the complexity.

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

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

Schema description coverage is 100% with good descriptions. The description adds value by clarifying date format acceptance (ISO or relative shorthand) and the value parameter (ticker or CIK), providing examples that enhance usability 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 retrieves recent changes for a company, with specific use case examples and explicit mention of fanning out to SEC EDGAR, GDELT, and USPTO. It distinguishes from siblings like entity_profile by focusing on dynamic 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 provides numerous example queries implying when to use the tool ('what's happening with X?', 'any updates on Y?'), but does not explicitly mention when not to use it or suggest alternative tools among the siblings. The context is clear but lacks exclusions.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which are helpful but the description adds minimal behavioral context. It doesn't explain the return format, side effects, or external dependencies beyond the cryptic hint of combining domain and suffix.

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 short (one phrase), but brevity sacrifices clarity. It is not an example of concise communication; rather it is under-specified and cryptic.

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 lack of output schema and minimal description, the tool's behavior is underdetermined. It does not explain what the tool returns, how it processes the domain, or how it relates to registration status. This is insufficient 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 0% schema coverage, the single 'domain' parameter lacks any description in the schema. The tool description does not compensate, failing to specify expected format (e.g., full URL, bare domain) or valid inputs. The description only implies it's the input but gives no guidance.

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

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

The description 'Domain + nearest suffix (the site)' is vague and does not clearly state the action or output. It reads like a definition rather than a tool's purpose, and does not differentiate from similar sibling tools like 'public_suffix'.

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 like 'public_suffix' or other domain-related tools. Context signals show sibling tools exist but description gives no comparative 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 only provide readOnlyHint=false and destructiveHint=false. Description adds key behavioral traits: scoping by identifier, persistence for authenticated vs anonymous users (24 hours), and pairing with recall/forget.

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

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

Three sentences, front-loaded with purpose, then usage guidance, then storage details. 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?

No output schema, but description explains storage for later retrieval, scoping, and retention policy. Pairs with recall and forget, covering the full workflow.

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 has 100% coverage with descriptions. Description adds context about key-value pair scoping and examples, improving clarity 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 'Save data the agent will need to reuse later' — a specific verb+resource. Distinguishes from siblings recall and forget.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and gives examples. Does not state when not to use, but pairing with recall and forget provides context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it returns IDs plus citation URIs. No contradictions; adds value beyond annotations by clarifying output content.

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, front-loaded with core action. Each sentence serves a purpose: define, example, output, usage advice. No redundancy or filler.

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 explains what is returned (IDs + citation URIs). Covers purpose, examples, and workflow position. Lacks details on response format or error handling, but sufficient for a lookup tool with strong annotations.

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

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

Schema coverage is 100%, baseline 3. Description adds examples ('Apple' → AAPL/CIK, 'Ozempic' → RxCUI 1991306) and clarifies that 'value' accepts ticker, CIK, or name for company; brand or generic for drug. Enriches parameter meaning beyond schema.

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

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

The description clearly states it resolves canonical identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It distinguishes itself from sibling tools by stating to use it before tools that need official identifiers.

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

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

Explicitly says when to use (when needing official identifiers) and positions itself as a prerequisite for other tools ('Use this BEFORE calling other tools'). Does not explicitly state when not to use, but context implies it's for identifier resolution.

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, indicating safe, read-only, open-world behavior. The description adds value by detailing the return format (verdict, structured form, actual value with citation, percent delta) and notes that it replaces 4-6 sequential calls, providing context on efficiency. 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 (4 sentences) and well-structured: it starts with a clear purpose, then usage guidelines, domain details, and output summary. 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?

Given the single parameter, full schema coverage, and presence of annotations, the description sufficiently covers the tool's domain, usage, returned values, and competitive advantage. No output schema exists, but the description compensates by listing return components. It is complete for an agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 100% with a clear description of the 'claim' parameter and example formats. The tool's description adds examples but does not significantly enhance understanding beyond the schema. The baseline of 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR+XBRL. It uses a specific verb ('fact-check, verify, validate') and resource, and distinguishes itself from sibling tools that are not claim-verification focused.

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 provides when-to-use guidance with example queries like 'Is it true that…?' and 'Validate this statement…'. It also specifies the supported domain (company-financial claims). However, it does not explicitly state when not to use the tool or provide alternative tools for out-of-scope claims, which would improve clarity.

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