Who Gho

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

Annotations already provide safety and idempotency hints. The description adds behavioral context: the probing process, default model, and that the API key is passed directly to Anthropic. It also outlines the return structure per model, which goes beyond annotations. 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, well-structured paragraph. It front-loads the core action and output, then adds model details and use cases. Every sentence contributes meaningful information without redundancy.

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

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

Despite no output schema, the description fully covers return structure (per-model score, confidence, signals, raw_response, combined view). Parameter usage is clear, and the tool's complexity (multiple models, optional API key) is well explained. Use cases are listed, making it complete for effective invocation.

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

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

Schema description coverage is 100%, but the description adds value by explaining usage context: entity is 'The thing to ask about', models are optional with default, _apiKey is only needed for Anthropic, and context helps disambiguate. This enriches the 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 the tool probes LLMs for brand/topic visibility and scores 0-100 per model. It uses specific verbs ('probe', 'score') and specifies the resource ('LLMs') and output format. It also lists use cases, distinguishing it from sibling tools that may do related but different tasks.

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

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

The description explicitly mentions when to use the tool (AI-marketing audits, pre-launch checks, competitive monitoring) and provides guidance on model selection with default model vs chargeable Anthropic. However, it does not explicitly state when not to use or point to alternative sibling tools, so a 4 is appropriate.

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, idempotentHint=true, openWorldHint=true, destructiveHint=false. Description adds that it routes to the right tool, fills arguments, and returns structured answers with citation URIs, which enhances transparency beyond annotations.

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

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

Description is thorough but efficient; each sentence adds value (preference, use cases, examples, sibling comparisons). It front-loads key information and avoids redundancy, though 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 explains return format (structured answer with citations). It covers when to use, what questions it handles, and how it compares to siblings. Complete for the tool's complexity and parameter count (6 params, 1 required).

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 of all parameters, so description does not need to re-explain them. However, the description provides multiple query examples and underscores the natural language acceptance, adding practical semantic guidance beyond schema.

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

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

Description clearly states it answers factual questions by routing to 4,482 tools across 1129 sources, returning structured answers with citations. It distinguishes from siblings like ask_pipeworx_grounded and deep_research by specifying use cases and when to prefer each.

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 'PREFER OVER WEB SEARCH' for factual queries, provides extensive examples of questions (stock data, SEC filings, weather, etc.), and instructs when to step up to grounded or deep research variants. No ambiguity.

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

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

Annotations already provide safe-read and idempotent hints. The description adds crucial behavioral details: return format with evidence, confidence, source; explicit refusal reasons (not_in_source, no_tool_match, etc.); and the additional LLM call cost. 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 about 100 words, front-loads the core purpose, then systematically covers routing, extraction process, return format, usage guidelines, and cost comparison. No wasted sentences.

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 fully documents the return object (success and refusal). With 6 parameters all being aliases and schema already covering them, the description is complete for an agent to use effectively.

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

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

Schema coverage is 100% with descriptions for each parameter (all aliases for question). The description only reiterates that aliases exist. No new semantics beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states 'Hallucination-resistant answer mode for high-stakes reads' and explains it picks the right tool, fetches data, and extracts answer only from tool result. Distinguishes from sibling ask_pipeworx by explicitly contrasting grounded vs. non-grounded behavior.

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: 'Use whenever an answer will be quoted, cited, or acted on...' and 'prefer ask_pipeworx for casual lookups.' Also notes cost trade-off (one extra LLM call), helping the agent decide when to invoke.

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, idempotentHint, and non-destructive behavior. The description goes far beyond, detailing resolution, classification, fan-out, blocking conditions (low confidence, closed markets, wide spreads), fallback mechanisms, and resolution-rule risk—all valuable context for an AI agent.

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 excessively long (multiple dense paragraphs) and lacks structure. While the content is valuable, conciseness is sacrificed; bullet points or clearer sections would improve scannability.

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 (classifiers, fan-out, multiple response shapes, error states), the description covers all critical aspects: market matching, evidence sources, parent events, news fallback, safety checks, and resolution rules. No output schema exists, but the response shape is thoroughly documented.

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 enriches each parameter: market input types, depth enumeration and behavior, include_raw effect. It adds concrete examples and clarifies aliases, going well 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 explicitly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It defines input types and use cases, clearly distinguishing it from siblings like polymarket_arbitrage or polymarket_edges.

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 ('should I bet on X', 'what does the data say about Y') and instructs agents to inspect market_match_confidence before trusting analysis. It lacks explicit exclusions (when not to use), but the guidance is otherwise thorough.

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 readOnly, openWorld, idempotent, non-destructive. The description adds specific behavioral details: data sources (SEC EDGAR, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradiction.

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

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

Description is a single, well-structured paragraph with front-loaded purpose. Every sentence contributes meaningful information without redundancy or verbosity.

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 schema (2 params, 100% coverage) and comprehensive annotations, the description fully covers what the tool does, how to use it, what data it returns, and why it's advantageous. No output schema needed as return format is described 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 is 100%, but description adds value by explaining the type param's effect on data returned (revenue, net income for companies; adverse events for drugs) and provides real examples for values (tickers, drug names). Constraints like 2-5 items are restated.

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 performs side-by-side comparisons of 2-5 companies or drugs in a single call, with specific example phrases and distinct entity types. It differentiates from siblings by explicitly advocating over sequential lookups.

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups' and gives concrete examples of when to use ('which is bigger', 'head to head'). Sibling tools like entity_profile handle single entities, reinforcing the guideline.

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 context beyond annotations: parallel execution, findings packet with evidence, confidence, source, citations, gaps array, and 15-60s response time. 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?

Packed with valuable information, but slightly long. Front-loaded with critical warnings. Every sentence earns its place, though minor trimming possible without loss.

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

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

Comprehensive: explains input, behavior, output format, response time, edge cases, and prerequisites. No output schema, but description covers return packet sufficiently.

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 both parameters 100%, and description adds value by explaining depth enum values (quick=3, standard=5, thorough=8) and emphasizing that question can be broad/multi-part.

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 performs grounded multi-source research across structured data sources, distinguishes from open-web search, and specifies it's for broad/multi-part questions. Contrasts with sibling tools like ask_pipeworx.

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

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

Explicitly says when to use (broad/multi-part questions over structured data) and when not to use (breaking/current news), provides alternative tool (ask_pipeworx), and notes account/plan requirements.

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 transparency by stating it returns ready-to-call tools with full schemas and examples, 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 concise (a few sentences) and front-loaded with the main action. It efficiently lists examples and key behaviors without unnecessary fluff.

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

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

Given no output schema, the description adequately explains the return format (top-N tools with names, descriptions, schemas). For a discovery tool with many siblings, this is sufficient for an agent to use it correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. The description mentions aliases but does not significantly add meaning beyond the schema.

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

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

The description clearly states 'Find tools by describing the data or task.' and provides a comprehensive list of example domains. It explicitly says to call this first for discovery, distinguishing it from sibling tools like deep_research or get_data.

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

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

The description advises using this when browsing or searching for tools and explicitly says 'Call this FIRST when you have many tools available'. It does not provide explicit when-not-to-use conditions, but the context implies when you already know the specific tool.

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). The description adds valuable behavioral details: fans out across multiple sources, USPTO patents soft-fail, GDELT→GNews fallback, sorted fundamentals. 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?

Front-loaded with examples and purpose. Though relatively long, every sentence provides necessary detail. Could be slightly tighter, but well-structured for readability.

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 adequately describes return fields (cik, company_name, filings, fundamentals, patents, news, LEI) and data source behavior. Missing only minor details like exact field names or pagination, but sufficient for most uses.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reinforces parameter constraints (e.g., names not supported, ticker vs. CIK examples) and adds context about the type enum, adding value beyond the schema.

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

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

The description clearly states 'full cross-source profile of a US public company' with specific data sources and output fields. Example queries and the sibling differentiation ('ALWAYS PREFER over chaining') make the purpose unmistakable.

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 prefer this tool ('when the user asks for a holistic view') and when not to use it ('names not supported — use resolve_entity first'). Provides clear context for agent decision-making.

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 and idempotentHint=true. The description confirms deletion but adds no extra behavioral details beyond what annotations convey. 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?

Two sentences, front-loaded with the verb and resource, and no wasted words. Every sentence adds value.

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

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

For a simple delete operation with one parameter and no output schema, the description is complete. It covers purpose, usage context, and sibling pairing.

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 the 'key' parameter fully described. The description does not add any additional meaning beyond the schema, 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 clearly states the tool deletes a stored memory by key, using a specific verb 'Delete' and resource 'memory by key'. It distinguishes from siblings by mentioning pairing with remember and recall, making the purpose distinct.

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

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

Explicitly states when to use: when context is stale, task is done, or to clear sensitive data. Also mentions pairing with remember and recall, providing clear context and alternatives.

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

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

The description explains that the tool fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. Annotations already indicate readOnlyHint=true and destructiveHint=false, and the description is consistent. It adds behavioral context beyond annotations, though it could mention error handling or limitations (e.g., if the URL is unreachable).

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 and well-structured. It consists of three clear sentences covering purpose, process, and output, followed by a separate useful-for list. Every sentence adds unique value with no redundancy or fluff.

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

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

Given the tool's simplicity and the presence of annotations and a full schema, the description is fairly complete. It covers purpose, behavior, use cases, and output format. However, it lacks details on edge cases like error handling, rate limiting, or what happens if the page is dynamic. These omissions prevent a score of 5.

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 explaining how parameters are used: url is the site to summarize with examples, max_links controls the number of included links with default and max values. This enriches the parameter understanding beyond the schema's basic descriptions.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifically for AI crawlers. It specifies the verb 'generate', the resource 'llms.txt file', and the scope 'for any URL'. This distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on other aspects of AI visibility.

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: getting a client's site indexed, drafting for own project, or auditing a competitor. This provides clear guidance on when to use the tool. However, it does not explicitly state when not to use it or mention alternative sibling tools, which would further improve the score.

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, idempotentHint=true, destructiveHint=false. Description adds that it returns numeric values and includes an example, but does not add significant behavioral context 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?

Three sentences: purpose, return type, and usage guidance with sibling reference. No unnecessary words, front-loaded with key information.

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

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

Given the simple lookup functionality, schema covers parameters fully, and output schema exists (not shown), the description is complete. It explains what the tool does, what it returns, and how to prepare the input.

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

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

Schema coverage is 100%, so baseline is 3. The description adds an example usage but does not provide additional semantic detail beyond the schema descriptions (e.g., country is ISO code, year is string).

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 'Get' and resource 'health data values for a WHO indicator code'. It clearly distinguishes from the sibling 'get_indicators' by directing the user to use that tool first to find the indicator code.

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 'Use get_indicators first to find the indicator code', providing clear guidance on sequencing. However, it does not exclude other tools or mention when not to use this tool.

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 read-only and idempotent nature. Description adds return format and usage context, but no additional behavioral traits beyond that.

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

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

Three sentences with front-loaded purpose and a useful example. No extraneous information.

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

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

For a simple one-parameter tool with output schema and full annotations, the description is complete. Explains return format and integration with sibling.

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 covers parameter fully (100% coverage). Description provides examples but adds no new semantic meaning beyond what's in 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 it searches or lists WHO GHO indicators and returns codes and names. Distinguishes from sibling tool get_data.

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

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

Explicitly says to use indicator codes with get_data for actual values and provides examples. Could mention when not to use, but context is clear.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by specifying the data source ('WHO-recognized') and the exact fields returned, reinforcing the tool's safety and output 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?

Two short sentences: the first covers purpose and output details, the second provides a usage example. No unnecessary words, well structured for quick comprehension.

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

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

Given the tool's simplicity (no parameters, a known output schema), the description fully covers what an agent needs: what is returned and how to use it. The existence of an output schema compensates for any omitted details about return structure.

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 tool has zero parameters, and schema coverage is 100%, so no parameter explanation is needed. The description logically focuses on the output, which is appropriate.

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

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

The description clearly states the verb 'Return' and the resource 'all WHO-recognized countries', listing specific fields (3-letter ISO code, display name, parent region code). It distinguishes itself from sibling tools like get_data by explaining how its output feeds into get_data.

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

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

The description provides explicit guidance on when to use the tool: to obtain ISO codes for use as the country parameter in get_data. It includes concrete examples. It does not mention when not to use or alternatives, but for a simple lookup tool this 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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds that it shows active subscriptions by default and optional include_inactive, plus return 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?

Two concise sentences with no wasted words. Front-loads the core action and returns, then usage guidance.

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

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

Covers purpose, usage, and parameters. Lists return fields despite no output schema. Could mention pagination but not necessary.

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 description does not need to add much. It repeats the parameter existence but no additional semantics. Baseline 3 is appropriate.

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

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

Description clearly states 'List the caller's active subscriptions' and lists returned fields. Distinguishes from sibling tools 'subscribe' and 'unsubscribe'.

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: 'review what you're monitoring before adding more or to find an id to cancel.' No explicit when-not, 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?

Discloses that it's free, doesn't count against quota, and is rate-limited. Annotations indicate non-read-only but description adds useful behavioral context beyond annotations.

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

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

Concise single paragraph with no wasted words. Front-loaded with purpose and usage, followed by constraints.

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?

Complete information for a feedback tool: purpose, usage scenarios, constraints (rate limit, quota), expected input format, and outcome (read by team). No output schema needed.

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 value by explaining the meaning of 'type' options and providing guidance on message content.

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's for sending feedback about broken, missing, or needed items. Distinguishes from sibling tools (none are feedback-related).

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 when to use (bug, feature, data_gap, praise), what not to do (avoid pasting end-user prompt), 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 declare readOnlyHint, openWorldHint, idempotentHint, and no destructive behavior. The description adds useful context: derived from CF analytics-engine, no PII, and caching behavior (5min-1h). 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 sentences, front-loaded with primary output, followed by use cases and behavioral notes. No wasted words.

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

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

For a simple tool with one optional parameter and no output schema, the description covers purpose, usage, behavioral details, and parameter semantics comprehensively.

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

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

The single parameter 'window' is well-documented in the schema with enum values and description. The description adds value by explaining the trade-off between shorter and longer windows ('hot now' vs 'steady-state demand').

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, top packs, and total call volume' over a specified window. It distinguishes itself from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on trending usage data.

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

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

Three specific use cases are listed (discovering hot data, confirming canonical choice, aligning demand). While no explicit 'when not to use' or alternatives are given, the context makes the intended usage 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?

Adds significant behavioral context beyond annotations: details monotonicity checks, partition filter, semantic anchor (Jaccard threshold), fill check against live CLOB depth, and conditions to avoid trading. No contradiction with annotations (readOnly, idempotent, etc.).

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?

Though long, every sentence adds value. Structured with requirements, modes, semantic anchor, partition filter, and fill check. No redundancy, front-loaded with critical info.

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

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

Given the tool's complexity and lack of output schema, the description thoroughly explains return fields (opportunities[], gap_pp, suggested_trade, etc.) and references sibling tool polymarket_fill_risk. Complete for effective 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?

Both parameters have full schema descriptions, but the tool description adds substantial meaning: explains event slug/URL format and topic seed question, and how each mode uses the parameter. Schema coverage is 100% and description enhances 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 tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It specifies two modes (event and topic) and distinguishes them. No ambiguity.

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 guidance on when to use each mode: event for specific market, topic for cross-event scanning. Warns that no args fail. Also directs to 'polymarket_fill_risk' for custom sizing, effectively differentiating from a sibling tool.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds extensive behavioral details: caching behavior ('Cached 1h at the KV level keyed on all knobs'), response structure (by_segment, fed_candidates, _diagnostics), and specifics about edge calculations and filters. 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 lengthy (about 350 words) but well-structured: starts with core purpose, then model families, response structure, and parameter knobs. Each sentence adds value, though some model formulas (e.g., '90d FRED log-returns') may be more detail than needed. It's front-loaded and efficient given the tool's complexity.

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

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

With no output schema, the description fully explains the response structure (by_segment, models, diagnostics) and covers edge cases like empty segments with filter skips. It explains caching, Fed bet exclusion, and parameter interactions. The description is complete for an AI agent to understand inputs, outputs, and behaviors.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context beyond schema, e.g., explaining default slippage (0.3pp due to Polymarket spreads), why min_kelly skips small opportunities, and the purpose of min_partition_leg_kelly (for per-leg Kelly in partitions). This adds moderate value beyond the schema descriptions.

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

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

The description clearly states the verb 'scan' and resource 'Polymarket markets' to return opportunities where Pipeworx data disagrees with market price. It specifies the tool is built for daily betting discovery, distinguishing it from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on broad opportunity scanning without paging.

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 usage context: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It explains the three segments and tradeable-edge knobs, and warns that Fed bets are excluded from ranking. While it doesn't directly compare to siblings, the context clearly indicates when to use this tool for broad discovery.

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, idempotent, non-destructive behavior. The description adds significant behavioral context: it reads snapshots, computes trends and decay from daily closes (not intraday), and discloses limitations like 60-day TTL and data start date. No contradiction with annotations.

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

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

The description is well-structured with sections for purpose, output fields, and limitations. It is front-loaded with the key question. However, it is somewhat lengthy and could be trimmed slightly without losing essential details.

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 (time-series data, computed metrics) and lack of output schema, the description thoroughly explains the output structure (tracked[], expired[], snapshot_dates[]) and limitations. It provides sufficient context for correct usage.

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

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

The input schema covers both parameters with descriptions (100% coverage). The description reinforces these with defaults, ranges, and meaning (e.g., 'window' as snapshot family). This adds 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?

The description clearly states the tool provides edge persistence and decay telemetry from daily polymarket_edges snapshots, answering a specific question about edge longevity. This distinguishes it from sibling tools like polymarket_edges.

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 it (to understand edge persistence) with a concrete example contrasting fresh vs. old wide edges. However, it does not explicitly mention when not to use it or point to alternative sibling tools.

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

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

Annotations already declare readOnly, idempotent, non-destructive; description adds rich context on size interpretation, default clamping, VWAP calculations, and failure modes, fully consistent 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?

Description is long but well-structured with clear sections for each mode and usage guidance; front-loaded with purpose, every line is informative. Minor redundancy could be tightened but still concise for the complexity.

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

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

Despite no output schema, description covers all return fields (top_of_book, VWAP, slippage, capture_ratio, profit_usd, etc.) and edge cases (thin_legs, directional risk), making the tool fully self-explanatory for an AI 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?

Schema coverage is 100%, so baseline is 3; description adds significant value by explaining how size_usd and side differ between modes and default behaviors, exceeding schema-only info.

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 performs an edge check against live order book depth, distinguishing two modes (single-market and basket) and explicitly differentiating from sibling tools like polymarket_arbitrage.

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

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

Explicitly advises using this tool before executing arb signals or trades above $500, explains when not to rely on theoretical edges, and notes risks of partial basket fills becoming directional positions.

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 the tool as read-only, idempotent, and non-destructive. The description significantly adds value by detailing compatibility_warning conditions, temporal alignment requirements, skipped cross-type/subtype counters, and the meaning of the spread delta, providing rich behavioral context beyond annotations.

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

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

The description is relatively long but each sentence provides necessary detail. It front-loads purpose, then modes, response, and safety fields. While verbose, it earns its length with valuable 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 no output schema, the description thoroughly explains the output structure (leg-by-leg prices, matched spreads, top_spreads_pp, safety fields) and covers edge cases like compatibility warnings. It is complete for a complex tool with many possible states.

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 each parameter has a description. The description adds clarity on how parameters interact (topic auto-fetches, explicit overrides the topic-mapped side), though it could be more explicit about combining both. Overall, it adds meaning 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on cross-venue rather than intra-venue analysis.

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 two modes (topic macros and explicit pairing) and provides guidance on when each is appropriate. It warns that most pre-mapped topics currently return compatibility warnings, advising realistic expectations. However, it does not explicitly state when not to use this tool, such as if only single-venue data is needed.

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, idempotentHint=true, destructiveHint=false. Description adds scoping detail (identifier types) and behavior when key omitted. 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?

Three sentences, front-loaded with main action, no redundant words. Every sentence serves a purpose: define action, specify usage, explain scope.

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 one optional parameter, no output schema, and sufficient annotations, the description fully covers purpose, usage, scope, and pairing. No missing information 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?

Schema coverage is 100% with clear description of 'key' parameter. Description reinforces but does not add new meaning beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys when key is omitted. It uses specific verb 'retrieve' and resource, and distinguishes behavior based on key argument, differentiating from siblings like remember and forget.

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

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

Describes when to use: to look up previously stored context without re-deriving. Mentioned scoping to identifier and pairing with remember/forget. However, lacks explicit when-not-to-use 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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds that mark_read flags events as read, and explains return format. Good context, but could mention rate limits or caching.

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

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

Concise, front-loaded main function, then specifics. 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 return fields and mark_read effect. Provides alternative endpoint and notes on polling. Complete for a retrieval 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 descriptions. Description adds extra context: explains mark_read behavior (flagging read), mentions default limit (1-200, default 50), and gives examples for type filter. Baseline 3, plus extra value.

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

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

The description clearly states it pulls fired events from a subscription feed, specifies return fields (source, citation_uri, raw payload), and mentions filtering options. This distinguishes it from sibling tools like get_data or list_subscriptions.

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 context: polling works fine, and an alternative HTTP endpoint is given. However, it does not explicitly state when not to use this tool vs 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, openWorldHint, idempotentHint, destructiveHint=false. Description adds rich behavioral context: fans out to multiple sources with fallback logic (GDELT→GNews), PatentView sunset soft-fail, and structured return format with citation URIs. No contradiction.

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

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

Dense but well-structured: opening examples, then bullet-like explanation of behavior and sources, then parameter tips, then anti-pattern reference. No unnecessary words. Every sentence serves a purpose.

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

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

Despite no output schema, description explains return structure (changes[] grouped by source, total_changes count, citation URIs), data sources with fallback and failure modes, and parameter semantics. Covers all essential aspects 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?

Schema coverage is 100% and describes all parameters. Description adds value by explaining accepted formats for 'since' (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and provides typical usage recommendation ('Use "30d" or "1m" for typical monitoring'). This goes beyond the schema's basic description.

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

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

Description starts with concrete usage examples ('What's new with X', 'latest on Y') and clearly states the action: change feed for a company via SEC, news, and patents. It explicitly distinguishes from sibling tool 'entity_profile' which is for static profiles.

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 guidance on when to use this tool vs. 'entity_profile' ('Use entity_profile instead when you want the static profile...'). Includes example queries. Missing explicit when-not to use, but the context is clear enough.

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

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

Discloses scoping by identifier, persistence for authenticated users (permanent) vs anonymous (24 hours). Adds value beyond annotations which only hint idempotent and non-destructive.

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 sentences, front-loaded with purpose, no redundant 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?

For a simple key-value storage tool with good schema and annotations, the description covers usage, persistence, and sibling relationships 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 is 100% with descriptions for key and value. Description adds example key patterns but no significant new 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 the tool saves data for reuse across conversations or sessions, specifies it stores key-value pairs, and distinguishes from siblings like 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?

Provides explicit when-to-use scenarios (e.g., discovered ticker, address, preference) and mentions pairing with recall/forget, but lacks explicit 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 declare readOnly, idempotent, and non-destructive behavior. The description adds that the tool internally cascades through multiple endpoints (replacing 2-3 lookups) and details the specific identifiers returned per type, providing context beyond annotations.

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

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

The description is well-structured with example queries, purpose, and type details. While slightly verbose, every sentence adds value and the structure is logical. Could be trimmed but earns a 4.

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 fully explains return values (ticker, CIK, RxCUI) and sources (SEC EDGAR, RxNorm). It covers both entity types thoroughly and provides enough context for an agent to use it correctly.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains the type enum values with concrete examples, describes the value parameter with valid inputs (ticker, CIK, name for company; brand/generic for drug), and mentions auto-disambiguation for company names. This goes well 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 uses concrete examples and explicitly states the tool resolves user-spoken names to canonical identifiers needed by other tools. It also says 'Use FIRST' to emphasize primary usage, clearly distinguishing from sibling tools like compare_entities or entity_profile.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID' and provides detailed supported types. It implicitly signals the tool is not for answering questions beyond ID lookup, but lacks explicit when-not-to-use or alternatives.

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

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

The description explains that the tool processes multiple entities, ranks them, and surfaces most/least recognized. Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context beyond annotations 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?

Three sentences, front-loaded with action and outcome, no unnecessary words. Each sentence adds critical 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 4 parameters, no output schema, and a sibling tool list, the description adequately covers purpose, parameters, behavior, and return format (ranked list with score, confidence, signal density). 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 descriptions for all parameters. The description adds value by clarifying that the first entity is treated as the 'subject' for narrative and rest as competitors, going beyond the schema.

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

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

The description clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and returns a ranked list. It distinguishes from siblings like ai_visibility_check (single entity) and 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 explicitly says 'Useful for competitive AI-marketing audits' and gives an example question. It mentions probing each entity with a sibling tool, but does not explicitly state when not to use it or list alternatives beyond the sibling set.

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 the annotations (readOnlyHint, etc.), the description discloses that the tool fans out to two services, explains partial failure behavior (bundlephobia may take 5-30s on first measurement, sources_failed lists timeouts), and details the return structure. There is no contradiction with annotations.

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

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

The description is dense and informative, but slightly verbose. It front-loads the main purpose and then details. Every sentence adds value, but it could be tightened without losing meaning. Still, it is well-structured and easy to parse.

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 (two data sources, multiple checks, partial failures, and no output schema), the description is complete. It specifies exactly what is returned (summary fields, advisories, links, alternatives) and explains edge cases like timing and graceful degradation.

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% field description coverage. The description adds value by explaining that 'version' defaults to latest, that scoped packages are accepted, and by detailing the rich output (summary block, advisories, links, alternatives) which the schema lacks (no output schema). The description compensates for the missing output 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 starts with a clear composite purpose: checking whether to add an npm package by aggregating data from deps.dev and bundlephobia. It specifies the resource (npm package) and the verb (scan), and distinguishes from sibling tools by stating the ecosystem scope and alternative tools for other ecosystems.

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 ('is X safe / popular / small' or 'what does adding lodash cost me') and when not to use it ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This provides clear context and alternatives.

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

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

Adds significant behavioral context beyond annotations: uses BGE-base-en embeddings + cosine over 500-char overlapping windows, cap of 200K chars with truncation and flagging, every passage carries offset for verification. 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?

Two-sentence description plus technical details, front-loaded with purpose. Every sentence adds value—no redundant information. Concise yet comprehensive.

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

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

Covers all key aspects: semantic search, chunking strategy, character limits, output format (passages with offsets and scores), and mentions of embeddings and cosine similarity. No output schema, but return values are described sufficiently. Complete 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?

Schema description coverage is 100% (all three parameters have descriptions). Description adds value by explaining query as natural-language with examples, limit range and default, and truncation behavior for text, exceeding baseline 3.

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

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

Description clearly states the tool performs semantic search inside a fetched record, specifies the verb 'search' and the resource 'record'. It also differentiates from siblings by noting it pairs with ask_pipeworx_grounded and saves context compared to fetching full documents.

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 when to use: 'when the record is too big to cram into the prompt'. Provides an alternative approach: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document'. Clear context and 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 provide readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds behavioral details: account requirement, persistence limitation, delivery channel specifics (SMS 10/day cap, webhook auto-disable after 10 failures), and the fact that webhook secret is returned once. 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 slightly verbose with inline examples but well-structured: starts with purpose and return, then requirements and type details, then delivery channels. Front-loaded with key info. Could be slightly more concise, but 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?

Despite no output schema, the description mentions the return value (subscription id). It covers all parameters, requirements, supported types with their parameter schemas, and delivery channels with security and rate-limit details. For a tool with 3 parameters and nested objects, this is a complete and self-contained explanation.

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% (description includes all 3 params). The description adds significant meaning beyond schema: for 'type' it explains each enum value; for 'params' it provides detailed per-type examples with required/optional fields; for 'delivery' it explains each channel's constraints and behavior. This is rich context beyond the schema's minimal 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 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It specifies the action (create), resource (subscription to event stream), and return value. It distinguishes from sibling tools like list_subscriptions and unsubscribe.

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 includes prerequisites: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' It lists supported types and delivery channels, providing clear context for when to use. However, it does not explicitly contrast with alternative tools for listing or pulling alerts, but the sibling context implies it.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, and openWorldHint. The description adds context about onboarding and return format ('category-bucketed example questions') but does not add significant new behavioral constraints 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 relatively long but densely packed with useful information, front-loading alternative phrasings and clear actions. Every sentence contributes meaning, though some redundancy could be trimmed.

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 role as an onboarding entry point and the rich sibling set, the description adequately covers when and how to use it. It explains the output (category-bucketed examples) and the optional argument. No output schema is needed as the description covers what is returned.

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 schema already documents the `topic` parameter. The description adds value by listing the focus area values and explaining that omitting the parameter returns a cross-category spread, which goes 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 tool's purpose: returning category-bucketed example questions for an agent unsure what to ask Pipeworx. It uses specific verbs ('returns', 'use this FIRST') and distinguishes from sibling tools by positioning itself as an onboarding entry point.

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 to use this tool first when unsure what Pipeworx can do, and to learn meta-tools. It also describes when to pass a topic vs no arguments. However, it does not explicitly mention when not to use it or name specific alternatives for comparison.

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?

Discloses that the row is deactivated (not deleted) and historical events remain via 'recent_alerts', adding value beyond annotations which only indicate non-destructive, idempotent 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?

Two sentences, no filler. Every sentence adds unique 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?

Despite no output schema, the tool is simple and the description fully covers purpose, constraints, and behavioral effects.

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?

Adds context to the 'id' parameter: specifies it's a UUID returned by subscribe. Schema already covers 100%, so baseline is 3; the 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?

Clearly states 'Cancel a subscription by id' with specific verb and resource. Distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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 mentions ownership enforcement ('only cancel your own subscriptions'), guiding when to use. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive traits. The description adds value by explaining the return format (verdict types, structured form, citation, percent delta) and that it replaces 4-6 sequential calls, providing operational insights 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 a single paragraph but efficiently packs information: common phrasings, use case, domain, return types, and efficiency gain. It is front-loaded with typical user queries, making it easy to parse. However, it could be broken into sections for even better readability.

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

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

For a tool with only one parameter and no output schema, the description fully covers all aspects: how to invoke (natural language claim), what domain is supported (financial claims), what the output looks like (verdict categories, structured data, citation, delta), and why it's efficient (replaces multiple calls). No gaps are evident.

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

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

The single parameter 'claim' already has a clear description in the schema (natural-language claim with examples). The tool description further adds meaning by specifying that claims are checked against company-financial data from SEC EDGAR + XBRL, which is not in the schema. Since schema description coverage is 100%, baseline is 3, but the extra domain context justifies a higher score.

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 starts with specific natural-language phrasings like 'Is it true that…' and 'fact check', clearly stating the tool's purpose as claim verification against authoritative sources. It distinguishes itself from sibling tools (none of which perform claim verification) by specifying the domain (company-financial claims).

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also limits usage to company-financial claims (revenue, net income, etc.), providing clear context. While it doesn't explicitly state when not to use it, the domain restriction is implicit.

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