Ietf Datatracker

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. Description adds that the default model is Workers AI Llama-3.3-70b (free), that _apiKey is passed directly to Anthropic, and that results include per-model data. 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?

Description is two sentences with all core info: purpose, default model, optional key, and return structure. No redundancy.

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

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

Given 4 parameters, no output schema, and no nested objects, the description covers entity, models, API key, context, and return format. It fully explains usage.

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

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

Schema coverage is 100% with descriptions. Description adds: default model behavior, direct API key passing to Anthropic, and that context helps disambiguate common names. This adds 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 it probes LLMs for knowledge about entities and scores visibility. It specifies the verb 'probe' and resource 'LLMs', and it distinguishes itself from siblings like 'ask_pipeworx' or 'scan_competitor_ai_presence' by focusing on AI visibility scoring across multiple models.

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

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

Description mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default model and optional Anthropic key. However, it does not explicitly state when not to use it or provide 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds context about routing behavior and citation URIs, but does not disclose additional traits like rate limits or fallback behavior. Value added but limited.

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 that front-loads the key instruction and lists supported domains concisely. It is information-dense without being overly long, earning its place.

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

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

Given the tool's complexity as a query router with one parameter and no output schema, the description covers scope, examples, and output format (citations). It lacks explicit handling of ambiguous queries but is generally 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% with a single 'question' parameter. Description enhances understanding by specifying the types of questions and the tool's routing mechanism, adding meaning beyond the schema's generic description.

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

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

Description clearly states the tool routes questions to appropriate tools among 1,423+ and returns structured answers with citations. It distinguishes itself from web search and sibling tools by emphasizing authoritative data and structured responses.

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 advises to prefer over web search for specific types of queries (SEC filings, FDA data, etc.) and provides many examples. Does not explicitly state when not to use, but the positive guidance is strong.

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

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

Annotations indicate read-only, open-world, non-destructive. Description adds rich behavioral detail: resolving market, classifying bet type, fanning out to specific packs, and returning evidence + comparison. No contradictions. The added context justifies a high score.

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 paragraph with front-loaded purpose, efficient explanation of process, and clear value proposition. Every sentence is informative and 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?

Despite no output schema, the description explains the return (evidence packet + market-vs-model comparison) and mentions classification categories and data packs. For a 2-parameter tool with open-world annotations, this is nearly complete; a minor gap is the exact structure of the comparison.

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 descriptions cover 100% of parameters. Description adds value by elaborating on the three forms of 'market' (slug, URL, question) and clarifying the default for 'depth' (thorough). This reinforces and extends schema information.

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 specifies the verb ('Research'), the resource ('Polymarket bet'), and the integrated data retrieval process. It differentiates from siblings by focusing on Polymarket bets with automated fan-out to relevant packs.

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 ('should I bet on X?', 'what does the data say?', 'is there edge?'). Contrasts with alternatives by saying agents using this tool 'convert better than ones that have to discover the packs themselves.'

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 (safe read), openWorldHint=true, destructiveHint=false. Description adds that it pulls data from specific sources (SEC, FAERS) and returns paired data with citation URIs, providing behavioral context beyond annotations. However, it could mention potential rate limits 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?

Description is 5 sentences, front-loaded with main purpose and usage. Every sentence adds new information (usage triggers, type-specific details, return format, efficiency benefit). 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, description covers key aspects: what data is pulled per type, return includes paired data and citation URIs, and efficiency (replaces many calls). However, output format details are vague (e.g., structure of paired data, citation format). Slightly incomplete for a complex tool.

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

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

Both parameters have schema descriptions (100% coverage). Description adds meaning: explains what each type value does (company pulls financial data, drug pulls adverse events etc.) and provides examples for values (tickers vs drug names). This adds value but could include more formatting 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?

Description starts with 'Compare 2–5 companies (or drugs) side by side in one call', providing a specific verb and resource. It distinguishes from sibling tools like entity_profile or resolve_entity by explicitly stating it replaces 8–15 sequential calls and is designed for batch comparison, making purpose clear and unique.

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

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

Description explicitly states when to use: when user says 'compare X and Y', 'X vs Y', etc., or wants tables/rankings. It separates by type (company vs drug) and details what each type returns. It implies not to use for single entity queries, as it replaces many calls.

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. Description adds that tool returns tool names+descriptions, which is helpful behavioral context. No contradiction; description complements annotations nicely.

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 reasonably concise given the need to list domains and usage context. Front-loaded with primary action 'Find tools by describing the data or task.' Every sentence adds value, but length is slightly longer than minimal.

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

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

Given no output schema, description clearly states it returns tool names+descriptions. Lists many domains, making coverage clear. Combined with annotations and schema, the description provides complete context for a discovery tool.

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

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

Schema coverage is 100% with both parameters described. Description mentions 'top-N' which aligns with limit, but does not add new semantics beyond schema examples. Baseline score of 3 is appropriate as 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?

Description clearly states the tool finds tools by describing data/task. Lists specific domains (SEC filings, FDA drugs, etc.) and explicitly says it returns top-N relevant tools with names and descriptions. Distinguishes from siblings by being a meta-discovery tool, with instruction to call it FIRST.

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

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

Explicitly says when to use: to browse, search, discover tools for listed domains. Also instructs to call FIRST when wanting to see option set, not just one answer. Lacks explicit when-not-to-use, but positive guidance is strong and implies alternative is using specific tools directly.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context about input formatting (name examples) but does not disclose other behavioral traits like error handling or response variability beyond what annotations imply.

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—two short sentences—with the core purpose front-loaded. Every word adds value without unnecessary elaboration.

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

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

Given the tool's simplicity (single parameter, no output schema), the description covers the essential purpose and input format. However, it omits details about the output (e.g., document content or metadata) and does not address potential failure modes despite the openWorldHint annotation.

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% parameter description coverage. The description compensates by providing concrete examples ('rfc9000', 'draft-ietf-quic-transport') which clarify the expected string format beyond the schema's bare type definition.

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 a document by name, with examples like 'rfc9000'. However, it does not differentiate from sibling tools such as 'rfc' or 'documents_search' which may have overlapping functionality.

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. Sibling tools like 'rfc' and 'documents_search' exist, but no explicit usage context or exclusions are given.

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 no behavioral context beyond what annotations provide, such as search logic, pagination, or state filtering.

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 only two words, the description is too brief to be informative. While concise, it sacrifices clarity and utility.

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 (5 parameters, no output schema, many siblings), the description fails to explain the search scope, result format, or how it differs from related tools.

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 80% schema coverage, the input schema already documents most parameters. The description adds no parameter-level information, so baseline score of 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 'Search documents' is vague and does not specify the type of documents or distinguish from sibling tools like 'document' or 'wgs_search'. It lacks a specific verb+resource combination.

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 such as 'document' for a single document or 'wgs_search' for working group documents. The context is missing.

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

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

Annotations declare readOnlyHint, openWorldHint, and destructiveHint. The description adds specifics: returns SEC filings, fundamentals, patents, news, and LEI with citation URIs. 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 a single, well-structured paragraph, front-loading the purpose and use cases, then detailing the output. Every sentence adds essential information without redundancy.

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

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

Despite no output schema, the description thoroughly covers the major data categories returned. Given the tool's complexity (aggregating multiple sources), it is fully informative for an agent to understand what the tool provides.

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

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

Input schema has 100% coverage, but description adds critical context: 'type' is limited to 'company' with future plans, and 'value' must be a ticker or zero-padded CIK, with an explicit exclusion of names. This goes beyond the schema's property 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 'Get everything about a company in one call,' specifying a concrete verb and resource. It distinguishes from sibling tools like resolve_entity by noting that names are not supported, and implies aggregation across multiple data sources.

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 lists example queries (e.g., 'tell me about X') and provides a condition for using resolve_entity first if only a name is available. This gives clear when-to-use and when-not-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?

Annotations already provide destructiveHint: true, so the description's 'Delete' confirms but adds minimal extra context. It does clarify that it applies to 'previously stored memories' but doesn't detail permanence or side effects 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?

Two sentences with clear action and usage guidance. No redundant information, 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?

For a simple one-parameter tool with clear annotations and schema coverage, the description is almost complete. It lacks clarification on return values, but that's not crucial for a deletion tool.

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

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

The input schema has 100% coverage with a description for the key parameter. The description adds no additional information about the parameter beyond what's in the schema, meeting the baseline.

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

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

The description explicitly states 'Delete a previously stored memory by key' which clearly identifies the action, resource, and method. This distinguishes it from sibling tools like remember and recall, indicating specific deletion functionality.

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 use cases: 'when context is stale, the task is done, or you want to clear sensitive data.' Doesn't explicitly mention when not to use, but the pairing with remember and recall gives 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 safe, read-only behavior. The description adds value by detailing the extraction process and output format. It could be improved by mentioning potential fetch delays or site access issues.

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 with three sentences, each earning its place: purpose, process, use cases. No wasted words, and critical information is 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?

With no output schema, the description correctly states the output format (text blob for llms.txt). It covers the main functionality but could address error handling for invalid URLs.

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 covers both parameters with 100% coverage, so the description adds minimal semantic value (e.g., 'Full URL' is redundant with the schema). A baseline of 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?

Description specifies a clear verb-resource pair ('generate llms.txt') and explains the internal steps (fetch, extract, emit). It distinguishes itself from sibling tools by its unique output format and use case for AI crawlers.

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

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

The description lists three concrete use cases (client site indexing, own project drafting, competitor auditing), providing clear context for when to use. However, it does not mention when not to use or compare to alternative 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 indicate read-only and non-destructive behavior. The description adds no additional behavioral context such as permission needs or response characteristics.

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?

Extremely short, but under-specification means it doesn't earn its place. It is concise but fails to convey necessary information.

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

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

Given minimal complexity and annotations, the description is still incomplete. It doesn't explain return values or any other aspects needed for effective use.

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

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

Schema description coverage is 0%, and the description does not explain the 'id' parameter beyond its type. No added semantic value over 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 it retrieves a person by datatracker id, indicating a specific verb and resource. However, it does not differentiate from sibling tools like 'entity_profile' or 'document'.

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

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

No guidance on when to use this tool vs alternatives. It simply states what it does without context for selection among siblings.

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

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

Adds significant behavioral context beyond annotations: 'Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.' Also notes that the team reads digests daily and feedback affects 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 comprised of five clear sentences, each adding value. It is front-loaded with purpose and usage. While slightly wordy, it remains efficient and well-structured.

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

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

Given 3 parameters (one nested) and no output schema, the description is complete. It explains purpose, when to use, how to write feedback, rate limits, and that it doesn't count against quota. Sufficient for an agent to correctly invoke the tool.

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

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

Schema coverage is 100% (all parameters have descriptions). The tool description reinforces usage scenarios for the 'type' parameter and provides additional guidance on writing the 'message' ('Be specific...'). This adds value over schema alone.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies concrete use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools like ask_pipeworx or discover_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 says when to use: for wrong/stale data, missing tools, or praise. Provides guidance on message content: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt.' Also mentions rate limits and that it's free.

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 cover safety (readOnly, idempotent). Description adds valuable context: data derived from CF analytics-engine, no PII, caching duration. No contradictions.

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

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

Three concise paragraphs, each sentence provides essential information. Front-loaded with purpose. No waste.

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

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

For a simple tool with one parameter, description covers data source, privacy, caching, and three concrete use cases. 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% with enum description. Description adds practical guidance on how windows affect results (shorter for hot, longer for steady-state), exceeding 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 returns top tools, packs, and call volume over a recent window. It distinguishes from siblings by focusing on trending usage data from other AI agents.

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 lists three use cases (discovering hot sources, confirming canonical choice, alignment check). Does not explicitly state when not to use, but context is sufficient.

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 the tool's behavior beyond annotations: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. There is no contradiction with the readOnlyHint and openWorldHint 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 a single paragraph that efficiently conveys purpose, usage, and output. It is not overly verbose, but could potentially be split for readability. Still, it is concise and well-structured.

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

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

The description fully explains the input (event slug/URL) and output (list of arbitrage pairs with details), covering what the agent needs to know. Despite missing output schema, the description compensates 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?

The description adds meaning to the single parameter 'event' by explaining it can be a slug or full URL and that the tool will walk child markets. This goes beyond the schema's description, providing valuable context.

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: find arbitrage opportunities in Polymarket events by checking for monotonicity violations. It explains the logic with a concrete example and distinguishes the tool from potential alternatives like polymarket_edges by its specific 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 explains when to use the tool: for events with multiple 'by date' or 'by threshold' markets. It does not explicitly state when not to use it or provide alternatives, but the context is clear enough for an agent to decide.

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

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

The description details internal behavior (V1 model from FRED + coinpaprika, scanning, grouping, fetching price history once, computing model probability, ranking by edge). Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false, and the description adds constructive behavioral 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?

The description is a single, well-organized paragraph that front-loads the purpose and method. It is concise but could benefit from bullet points or structure for easier parsing, though it remains clear and 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?

Despite no output schema, the description explains what is returned (top N ranked by edge magnitude with suggested trade direction). It covers the model, data sources, and intended use case, making the tool's behavior fully understandable for an agent.

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

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

All three parameters have descriptions in the input schema (100% coverage), so the description adds little beyond context. It explains the purpose of 'window' (volume filtering) and 'min_edge_pp' (minimum edge), but the schema already provides the details.

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

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

The description clearly specifies the verb 'scan' and resource 'highest-volume Polymarket markets', with a specific objective: find where Pipeworx data disagrees most with market price. It distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on edge magnitude for opportunity 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?

The description explicitly states it is built for the 'what should I bet on today' question and helps avoid paging through hundreds of markets. It implies usage for opportunity discovery, but does not explicitly state when not to use it or provide direct alternatives among siblings.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds operational detail: returns raw probabilities (0-1) and spread in percentage points, and explains the two modes. No contradictions. Adds value beyond annotations without over-claiming.

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?

Well-structured: first sentence states purpose, then explains rationale, then details the two modes, finally summarizes output. Each sentence adds value. Slightly lengthy but justified by complexity. Could be slightly tighter, but effective.

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

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

Lacks output schema, but description covers return format (leg-by-leg prices, spread) and explains both usage modes. Does not address edge cases like missing matching events, but overall sufficiently complete for a data retrieval tool with good 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 covers 100% of parameters with descriptions. The description adds extra meaning: explains the topic values list, that explicit tickers override topic-mapped sides, and provides example formats (e.g., 'KXFED-26OCT'). This enriches 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?

Clearly states it computes cross-venue spreads between Kalshi and Polymarket, identifies two distinct modes (topic shortcuts and explicit tickers), and distinguishes from siblings like polymarket_arbitrage which focus on intra-venue 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?

Describes two modes and when to use each (pre-mapped topics vs custom pairings), and notes that the spread is a real arbitrage signal, implying usage context. However, it does not explicitly mention when not to use or compare to alternatives like polymarket_arbitrage.

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?

Adds behavioral context beyond annotations: scoping to identifier, ability to list all keys by omitting argument. No contradictions with readOnlyHint and destructiveHint.

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 succinct sentences, each adding value: action, usage examples, scoping, and pairing. No wasted words.

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

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

For a simple read-only tool with one optional parameter, the description is complete: explains what it does, how to use, and what to expect. Covers listing behavior and scoping.

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 one parameter. Description reinforces omit behavior but adds little extra meaning beyond the schema description.

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

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

The description clearly states the verb 'retrieve' or 'list' and the resource 'value previously saved' or 'keys'. It distinguishes from siblings 'remember' and 'forget' by explicitly mentioning them.

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

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

Provides clear context on when to use: to look up previously stored context. Mentions pairing with remember and forget. Does not explicitly state when not to use, but the usage is well implied.

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 transparency by disclosing parallel fan-out to multiple sources and the return structure (structured changes, total_changes, citation URIs). It does not contradict annotations. No mention of rate limits or timeouts, but the given details are adequate.

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

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

The description is concise and well-structured. It starts with the core function, provides example queries, lists data sources, explains input formats, and summarizes output. No unnecessary repetition or jargon.

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 multi-source fan-out and flexible input formats, the description covers all essential aspects: purpose, input parameters, data sources, and output summary. There is no output schema, so the description appropriately mentions return fields without over-specifying.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by clarifying the 'since' format with examples (ISO date vs relative shorthand) and reinforcing that 'value' accepts ticker or CIK. It also ties parameters to the tool's purpose, slightly improving 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 the tool's purpose: checking recent changes for a company. It provides specific example queries and lists the three data sources (SEC EDGAR, GDELT, USPTO), effectively distinguishing it from sibling tools like entity_profile or compare_entities.

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

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

The description gives explicit usage scenarios (e.g., 'what's happening with X?') and input format guidance for the 'since' parameter. It could be improved by noting when not to use this tool (e.g., for detailed historic data), but it provides sufficient context for appropriate invocation.

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

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

Annotations are minimal, but the description adds rich behavioral context: scoping by agent identifier, persistence differences between authenticated and anonymous users, and the 24-hour retention limit. This goes well beyond the structured data and discloses key behaviors.

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

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

The description is concise at four sentences, each serving a clear purpose: stating overall function, providing usage guidance, explaining scoping and persistence, and directing to companion tools. No wasted words.

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

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

Given the tool's simplicity and the availability of sibling tools, the description is complete. It explains the key-value store model, persistence semantics, and how it fits with recall and forget. No output schema is needed given the simplicity.

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 brief descriptions. The tool description adds substantial semantic value by providing concrete examples of keys ('subject_property', 'target_ticker') and values ('findings, addresses, preferences, notes'), which helps the agent understand how to format and use parameters correctly.

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', which is a specific verb and resource. It distinguishes itself from siblings by explicitly naming 'recall' and 'forget' as companion tools, making its unique role evident.

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

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

The description provides explicit guidance on when to use: 'Use when you discover something worth carrying forward...' and gives concrete examples like 'a resolved ticker, a target address'. It includes limitations (24-hour retention for anonymous) and directs to alternatives ('Pair with recall to retrieve later, forget to delete').

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, destructiveHint=false. The description adds value by specifying the return of IDs plus pipeworx:// citation URIs, which is behavioral context not covered by annotations.

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

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

The description is concise, front-loaded with the action and purpose, and every sentence adds value. No unnecessary words or repetition.

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

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

Given the tool's simplicity (2 parameters, 100% schema coverage, comprehensive annotations), the description fully covers the purpose, usage, output (IDs + URIs), and context. 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?

Input schema has 100% coverage with descriptions and an enum for type. The description adds meaning beyond schema by providing concrete examples like 'Apple' → AAPL and 'Ozempic' → RxCUI 1991306.

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

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

The description clearly states the tool resolves entity names into canonical identifiers (CIK, ticker, RxCUI, LEI) for companies and drugs. It distinguishes itself from sibling tools by noting it replaces 2–3 lookup calls and specifies the ID systems it outputs.

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

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

Explicit guidance is provided: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' Examples for 'Apple' and 'Ozempic' further clarify usage.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety profile. The description does not contradict annotations but adds no behavioral context beyond the bare minimum.

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 (3 words) and front-loaded. It is concise but risks being too terse; a bit more detail would not hurt conciseness.

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

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

Given no output schema and only one parameter, the description fails to explain what the tool returns or how to interpret results. An agent would need to guess the response format, which is insufficient for reliable use.

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

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

Schema description coverage is 0%, so the description must compensate. It clarifies that the 'number' parameter refers to an RFC number, but lacks details like range, format, 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 'RFC by number' indicates the tool retrieves an RFC document by its number, but it does not specify what RFC stands for or differentiate it from similar sibling tools like 'document' or 'wg'.

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. It does not mention prerequisites, exclusions, or context where this tool is preferred.

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details behavioral traits: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density per entity. This adds significant value beyond the 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 3-4 sentences, front-loaded with purpose, and every sentence provides essential information. There is no redundancy or fluff, making it concise and well-structured.

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

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

Given moderate complexity (4 params, no output schema), the description fully explains the tool's purpose, usage, parameters, and return format (ranked list with specific fields). It also mentions the underlying tool (ai_visibility_check), which provides additional context. No gaps are apparent.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by clarifying that the first entity is treated as the 'subject' and rest as competitors, and by explaining the default model and API key usage. This extra context justifies a 4.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side using specific verbs like 'Compare', 'Probes', 'ranks', and 'surfaces'. It distinguishes itself from sibling tools by specifying that it aggregates multiple probes (using ai_visibility_check) and returns a ranked comparison, which is unique among siblings.

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

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

The description provides clear context: 'Useful for competitive AI-marketing audits' and gives an example question. It implicitly suggests using ai_visibility_check for single entities by mentioning it as the underlying probe. However, it does not explicitly state when not to use this tool or list alternative tools for other 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral details such as the return values (verdict, actual value with citation, percent delta) and the tool's reliance on EDGAR+XBRL for US public companies, which provides useful 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 informative and front-loaded with the core purpose. Each sentence adds value, covering action, scope, usage, output, and efficiency benefits. It is not overly verbose, though a minor reduction in length could improve conciseness.

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

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

Given the tool's moderate complexity and no output schema, the description adequately covers input, output format (verdict values), usage context, and scope limitations. It does not detail error handling or data freshness, but the core information is present for effective use.

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

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

The input schema covers the single parameter 'claim' with a description. The tool description adds value by providing examples of claim formats (e.g., 'Apple's FY2024 revenue was $400 billion'), enhancing understanding beyond the schema's minimal description.

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

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

The description clearly states the tool's purpose: fact-check, verify, validate, or confirm/refute factual claims. It specifies the domain (company-financial claims via SEC EDGAR + XBRL) and distinguishes from siblings by focusing on claim verification. The verb-resource pairing is specific and unambiguous.

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

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

The description explicitly states when to use the tool: when an agent needs to check truthfulness of a claim, with example phrasings. It implies scope limitations (v1 supports company-financial claims) but does not explicitly mention when not to use or list alternative tools among siblings.

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

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

Annotations already provide readOnlyHint=true, openWorldHint=true, destructiveHint=false, which describe the tool's safety and output variability. The description does not add any additional behavioral context beyond what annotations cover.

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 only 3 words, which is very concise. Every word is necessary and contributes to the purpose. However, it is at the edge of being under-specified.

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 required parameter and read-only nature, the description is minimally adequate. However, with no output schema, a hint about what is returned (e.g., details of the working group) would improve completeness.

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

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

Schema coverage is 0%, so description must compensate. It mentions 'acronym' but does not explain the format, allowed values, or meaning of the acronym. This minimal information partially clarifies the parameter but is insufficient.

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 says 'Working group by acronym.', which clearly indicates the tool retrieves information about a working group identified by its acronym. It implies a lookup function, distinguishing it from sibling search tools like wgs_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. There is no mention of when to use wg vs wgs_search or other tools, leaving the agent without 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=true and destructiveHint=false, indicating safe read-only behavior. The description adds no additional behavioral details beyond what the annotations already convey.

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 with no extraneous information. For a simple list operation, this is efficient and front-loaded.

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

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

Given that the tool has two parameters and no output schema, the description is too minimal. It fails to explain the pagination parameters or the format of the returned list, leaving the agent without necessary 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 description coverage is 0%, and the description does not mention the 'limit' and 'offset' parameters. These standard pagination parameters are left undocumented in the description.

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

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

The description 'List working groups' clearly states the verb (list) and resource (working groups), making the tool's purpose obvious. However, it does not differentiate from sibling tools like 'wg' or 'rfc', which might have overlapping functionality.

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 such as 'wg'. There are no usage contexts, prerequisites, or exclusions mentioned.

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