Dummyjson

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

Annotations declare readOnlyHint=true and idempotentHint=true. The description adds that passing _apiKey sends data to Anthropic (external dependency) and that you pay Anthropic directly for those calls. It also details return structure (score, confidence, signals, raw_response). 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 two short paragraphs, front-loaded with the core action. Every sentence is informative and necessary. 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 no output schema, the description adequately explains the return structure (per-model fields plus combined view). All four parameters are addressed, and the complexity of key usage is clarified. The tool's purpose and use cases are complete for an agent to invoke correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining the _apiKey is passed straight through to Anthropic and is optional, and that context helps disambiguate entities. It also clarifies the default model and that multiple models can be probed.

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 knowledge about an entity and scores visibility (0-100) per model. It specifies default model, optional API key for Anthropic, and return structure. It distinguishes from sibling tools like ask_pipeworx (single model query) and scan_competitor_ai_presence (focused on competitors) by focusing on multi-model visibility scoring.

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 says the tool is useful for AI-marketing audits, pre-launch brand checks, and competitive monitoring. It explains when to use the _apiKey (only if probing Anthropic) and that the default model is free. It does not explicitly mention when not to use it or list alternatives, but the sibling tool names provide context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and behavior. The description adds valuable context: it routes questions to thousands of tools, fills arguments automatically, and returns structured answers with stable citation URIs. No additional info on rate limits or authentication, but the annotations already assure safety.

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 that front-loads the key instruction ('PREFER OVER WEB SEARCH'). It includes examples and enumerates domains. It could be slightly more concise but remains efficient and organized. No redundancy.

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

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

Given the tool's complexity (6 parameter aliases, 1 required, no output schema), the description covers the input format, scope, and output nature (structured with citations). It does not describe the exact return schema, but given the open-world nature, this is acceptable. The description is fairly complete for an agent to invoke correctly.

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

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

Schema description coverage is 100%, but the parameter descriptions are minimal (e.g., 'Alias for question'). The main description provides examples of acceptable questions but does not add semantic guidance beyond the schema. Baseline 3 is appropriate as the schema already documents the parameter and its aliases.

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 answers factual questions using authoritative structured data, routing to 3751 tools across 886 sources. It uses a specific verb ('ask') and resource ('Pipeworx'), and contrasts with web search. While sibling tools like 'ask_pipeworx_grounded' exist, the description is sufficiently specific to distinguish its general-purpose nature.

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' and lists many use cases: SEC filings, FDA data, economics, etc. Provides concrete examples ('current US unemployment rate', 'Apple's latest 10-K'). Tells agent to use for 'what is'/'look up' queries. Does not specify when not to use, but the positive guidance is strong and complete.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description details exact return format on success and explicit refusal reasons, and mentions the extra LLM call cost, fully disclosing behavioral traits.

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

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

The description is front-loaded with the key purpose, efficiently structured with sentences covering purpose, routing, output, usage guidance, and cost trade-off, all without superfluous wording.

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 fully covers return format, all refusal reasons, and the extra cost, providing complete context for agent decision-making.

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

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

With 100% schema description coverage, the schema already defines all parameters and aliases. The description adds no significant new semantic detail beyond stating that the question should be in natural language, making it adequate but not exceptional.

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 as 'Hallucination-resistant answer mode for high-stakes reads', explicitly contrasting with the sibling tool ask_pipeworx by noting the extra LLM call and use case distinction.

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: 'whenever an answer will be quoted, cited, or acted on' and when not to: 'prefer ask_pipeworx for casual lookups', providing clear decision 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?

Describes extensive behavioral traits beyond annotations: fan-out per classifier, response shapes, resolver contract with market_match_confidence, parent_event extractor, news fallback, safety short-circuits for low-confidence and closed markets, wide-spread alerts, resolution-rule risk. 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?

Very detailed and exhaustive, covering many edge cases and examples. While structured with capital-letter sections, the length exceeds what is typical for a concise description. Could be trimmed to essential guidance without losing 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?

Despite no output schema, the description fully explains return shapes (result.market, result.analysis, result.evidence), resolver contract, parent_event, news fields, safety mechanisms, and resolution-rule risk. Covers edge cases like closed markets, wide spreads, and cancellation rules.

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

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

All three parameters (market, depth, include_raw) are documented in schema (100% coverage). Description adds meaning: examples for market input, explains depth options and default, details include_raw's effect on response size and use cases. Adds context on fan-out behavior tied to market input.

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 verb+resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question) and output evidence packet. Distinguishes from sibling tools like polymarket_edges by focusing on a single bet.

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 usage examples: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Does not explicitly contrast with alternatives but context implies when to choose this tool over siblings.

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

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

The annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds 'paged', implying pagination, but does not detail other behaviors like default sort order or rate limiting. Given annotations cover safety, the description adds marginal value.

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

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

At two words, the description is extremely concise. However, it sacrifices clarity for brevity. It could be expanded to include more detail without becoming verbose.

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

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

Despite having an output schema, the tool lacks crucial context: what comments are being listed (global? per user? per post?), how pagination works (defaults? max limit?), and how it differs from similar tools. The description is insufficient for an agent to use it correctly in diverse scenarios.

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 two parameters (skip, limit) with 0% description coverage. The description only says 'paged', which hints at pagination but does not explain that skip is the offset and limit is the max items. The agent must infer parameter semantics from names alone.

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

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

The description 'Paged comments.' indicates the tool returns a paginated list of comments, but it doesn't specify which comments (e.g., all comments or comments on a specific resource). The purpose is clear but vague, leaving the agent uncertain about the scope.

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

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

No guidance is provided on when to use this tool versus sibling tools like 'post', 'posts', 'quotes', or 'todos'. The agent has no basis to choose 'comments' over 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 (readOnlyHint, idempotentHint, etc.) are supported by description that reveals data sources (SEC EDGAR/XBRL, FAERS), fiscal year handling, sorting by primary metric, and citation URI returns. No contradictions.

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

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

Description is longer but well-structured with front-loaded examples and clear sections. Every sentence adds value; minor 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?

Despite no output schema, description explains returned data (paired data, citation URIs, sorted by primary metric). Could detail exact metrics returned, but overall adequate for selection.

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 meaning beyond schema: explains what each 'type' value retrieves (financials vs. drug stats) and clarifies 'values' format (tickers/CIKs for companies, names for drugs).

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 side-by-side comparisons of 2-5 companies or drugs in one parallel call, with specific natural language triggers. It distinguishes from sibling tools like entity_profile by explicitly preferring it 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?

Provides explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Includes example queries that define when to use. Lacks explicit when-not-to-use, but context is strong.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. Description adds expected latency (15-60s), pre-verified facts, gaps[] for unanswered facets, and stable citation format. Contradiction flag false.

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 core action, each sentence adds necessary context (example, contrasts, prerequisites, latency, output format). Slightly verbose but well-structured.

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

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

No output schema, but description fully explains return type (structured findings packet with evidence, confidence, source, fetched_at, citation, gaps). Covers all needed behavioral details for a research tool with good annotations.

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

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

Schema coverage is 100%, but description adds value: clarifies depth enum values (quick=3, standard=5, thorough=8) and paid requirement for thorough. For question, emphasizes broad/multi-part is fine. Adds meaning beyond schema.

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

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

The description clearly states the tool performs grounded multi-source research by decomposing questions, routing to 3,751 tools across 886 sources in parallel, and returning structured findings. It differentiates from sibling ask_pipeworx by noting it handles broad/multi-part questions instead of single 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?

Explicitly says 'Use for broad or multi-part questions' and gives examples. Directly contrasts with ask_pipeworx for single lookups. Also mentions account requirements and paid plan necessity for depth:'thorough', providing complete 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 indicate readOnlyHint=true and idempotentHint=true, so the description's burden is lower. The description adds useful context: returns top-N relevant tools with full schemas, no second lookup needed. No contradictions with annotations. It does not discuss potential limitations like rate limiting or empty results, but overall transparent.

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

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

The description is a single paragraph that front-loads the purpose and provides key details. It is informative but slightly verbose, listing many domain examples. It could be tightened while retaining clarity, but 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?

The description covers the main functionality: searching tools by query, returning top-N results with schemas, and when to use it. It lacks explicit details on output structure (but no output schema is provided) and does not mention error handling or sorting criteria. However, for a discovery tool with good annotations, it is reasonably complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mentions that 'query' accepts aliases (task, q, search, description) which is already in the schema. It adds little extra meaning beyond reinforcing the natural language usage. Thus, score 3.

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

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

The description clearly states the tool finds tools by describing data or task, and gives extensive domain examples. It distinguishes from sibling search tools by explicitly saying 'Call this FIRST when you have many tools available and want to see the option set.' The purpose is specific and unambiguous.

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

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

The description explicitly tells when to use the tool: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also implies this is for discovery, not direct data retrieval, and provides example queries to guide usage.

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

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

Annotations already indicate readOnly and non-destructive. Description adds value by detailing data sources (SEC, XBRL, USPTO, news, GLEIF), return fields, and a known limitation (PatentsView API sunset May 2025 soft-fails). Could mention error handling or performance but still strong.

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 well-structured with examples, sources, and constraints. It's slightly long but every sentence adds value. Could be tightened slightly but effective.

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

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

Given no output schema, description thoroughly explains return components (cik, filings, fundamentals, patents, news, LEI). Covers limitations and fallback behavior. Complexity is high and description adequately completes the picture.

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 describes both parameters with enums and descriptions. Description adds nuance: explains 'value' must be ticker or zero-padded CIK, reinforces no names, and provides examples. Schema coverage is 100%, but description adds meaningful context 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?

Clearly states the tool creates a 'full cross-source profile of a US public company in ONE parallel call' with specific examples of user queries. Differentiates from siblings like resolve_entity by noting it doesn't accept names.

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 'ALWAYS PREFER over chaining single-pack lookups' for holistic views and warns names not supported, directing to resolve_entity first. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already provide destructiveHint: true and idempotentHint: true. The description confirms deletion but adds no new behavioral context beyond what annotations convey, which is acceptable.

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 with no fluff. The first sentence delivers the core action, followed by usage context and pairing 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?

For a simple one-parameter delete tool, the description fully covers purpose, usage, and behavioral traits. No output schema is 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 coverage is 100% and the schema already describes 'key' as 'Memory key to delete'. The description adds no additional 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 'Delete a previously stored memory by key,' using a specific verb and resource. It also distinguishes from siblings by mentioning 'Pair with remember and recall.'

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

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

The description provides explicit when-to-use scenarios ('context is stale, the task is done, or you want to clear sensitive data') and pairs with siblings. However, it doesn't explicitly state when not to use it.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds context: fetches external page, extracts title/description/key links, outputs standard markdown format. 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, no fluff. First sentence defines purpose, second explains process, third provides use cases. Highly efficient and front-loaded.

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

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

Given no output schema, the description explains the output format ('single text blob ready to drop at site-root/llms.txt'). It covers key behaviors (fetch, extract, emit). Minor omission: could mention that the URL should be publicly accessible, but overall sufficient.

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

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

Schema description coverage is 100% with both 'url' and 'max_links' described. Description does not add new information beyond the schema (e.g., default 25, max 50 already in 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?

Description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb 'generate', resource 'llms.txt file', and outcome 'AI crawlers can index the site cleanly'. It details the process (fetch, extract, emit) and distinguishes itself from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on llms.txt generation.

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

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

Provides explicit use cases: getting client's site indexed, drafting own project's llms.txt, auditing competitor. However, it does not explicitly state when not to use this tool or compare to related siblings, leaving some ambiguity for tool selection.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds value by specifying returned fields (id, type, params, created_at, last_fired_at, fire_count) and default behavior (active only, with optional include_inactive parameter). 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 with no wasted words. The core purpose is front-loaded, and usage guidance follows efficiently.

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 usefully lists returned fields. The single parameter is well-documented in schema. It adequately covers the tool’s behavior for its simplicity.

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

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

The schema has one parameter with full description (100% coverage). The description does not add extra semantic detail beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing.

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 this to review what you're monitoring before adding more or to find an id to cancel', providing clear when-to-use guidance and excluding other contexts.

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

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

The description discloses behavioral traits beyond the annotations: rate-limited (5 per identifier per day), free (doesn't count against quota), and signal affects roadmap. Annotations already show readOnlyHint=false, etc., so the description adds valuable context about limitations and impact.

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 (5-6 sentences) and well-structured. It front-loads the main purpose, then provides usage details, behavioral notes, and constraints. Every sentence earns its place without redundancy.

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

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

Given the tool's simplicity (feedback submission) and the absence of an output schema, the description is complete. It covers purpose, usage, parameters, rate limits, and impact. No additional information is needed for an agent to invoke this tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema: it provides guidance for the 'message' parameter ('Describe the issue in terms of Pipeworx tools/packs – don't paste the end-user's prompt') and reinforces the enum semantics for 'type'. This extra context elevates the 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 clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the resource (Pipeworx team) and the action (sending feedback), and distinguishes from sibling tools by being a dedicated feedback mechanism, which no other sibling provides.

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: bug, feature/data_gap, praise. It also provides what not to do ('don't paste the end-user's prompt') and mentions rate limits and free usage. While it doesn't explicitly state when not to use, the context is clear enough that alternatives are not relevant since no sibling tool serves this purpose.

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 data source (CF analytics-engine), no PII, caching behavior, and self-aggregating nature. Annotations already mark read-only, idempotent, non-destructive; description adds meaningful context.

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

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

Three concise sentences covering purpose, use cases, and technical details. 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?

For a simple read tool with one optional param, description fully explains output, source, and caching. 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 covers parameter fully, but description adds practical guidance on window meaning. Slight enhancement 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?

Clearly states it returns top tools, packs, and call volume over time windows. Use cases provided distinguish it from siblings like discover_tools.

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

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

Explicitly lists three use cases and explains window semantics (short for hot, long for steady-state). 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 indicate readOnlyHint and idempotentHint, but the description significantly expands with behavioral details: it explains the monotonicity and partition checks, semantic anchor, partition filter, fill check procedure, and that no arguments leads to failure. 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 long and detailed, with structured sections (REQUIRES, event, topic, SEMANTIC ANCHOR, etc.), which aids comprehension for a complex tool. However, it is verbose and some details (e.g., exact response fields) could be streamlined without loss of clarity.

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

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

Given the tool's complexity and lack of output schema, the description thoroughly explains return values (opportunities array with fields, partition_check, fill_check details). It covers prerequisites, failure modes, and edge cases (e.g., partition filter, skipped_low_similarity). Slightly more context on error handling would elevate completeness.

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

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

Schema description coverage is 100%, providing baseline adequacy. The description adds substantial meaning beyond schema descriptions: it explains event accepts slugs or full URLs, topic accepts seed questions, and provides behavioral context (walks child markets, searches related events). This enriches parameter understanding.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It specifies the resource (Polymarket) and the action (finding arbitrage) with distinct modes (event vs topic) and concrete examples, effectively distinguishing its purpose.

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

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

The description explicitly requires one of 'event' or 'topic' and recommends which to use (event for a specific market, topic for cross-event scanning). It explains when each mode is appropriate and notes cross-event catches patterns single-event misses. However, it does not differentiate this tool from sibling tools like polymarket_edges or polymarket_edge_tracker, so guidance on alternatives is limited.

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

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

The description discloses extensive behavioral details beyond the annotations: caching at 1h KV-level, response structure with by_segment and diagnostics, algorithm logic for each model family, and handling of Fed bets. Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent and the description adds valuable context about side effects (e.g., caching). 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 long but well-structured with sections and front-loaded purpose. Every sentence provides essential detail given the tool's complexity. While it could be slightly more concise, the thoroughness is justified and the organization supports 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?

Despite lacking an output schema, the description thoroughly explains the response structure including top-level fields (by_segment, fed_candidates, _diagnostics) and per-opportunity details (edge_pp_net, kelly_fraction, liquidity, spread, volume, 24h-move warning). It also covers caching, algorithm internals, and filter knobs, making it complete for agent use.

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

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

All 9 parameters have detailed descriptions in the input schema (100% coverage). The tool description reiterates some parameter purposes (like tradeable-edge knobs) but does not add significant new meaning beyond what the schema already provides. Baseline score of 3 is appropriate given the high schema coverage.

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

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

The description clearly states the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, specifically designed for daily betting research ('what should I bet on today'). It distinguishes from siblings like polymarket_arbitrage by focusing on data-disagreement edges rather than pure arbitrage.

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

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

The description provides a clear use case but does not explicitly state when to avoid this tool or mention alternatives. It explains tradeable-edge knobs and filter parameters, which guide usage, but lacks direct comparisons to sibling tools like polymarket_arbitrage or bet_research.

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 destructiveHint as safe/read-only. The description adds valuable behavioral context: it uses daily snapshots, returns tracked/expired/snapshot_dates, explains that decay is computed from daily closes of edge_pp_net (net of default slippage), not intraday, and notes limits on history depth (60-day TTL, start from snapshot enablement). 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 well-structured with a clear purpose first, then a RESPONSE section detailing outputs, and a LIMITS section for constraints. It is dense but not overly verbose; every sentence provides necessary information. Slight room for improvement in condensing some phrases.

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 has 2 optional parameters, no output schema, and is moderately complex, the description fully covers inputs, outputs (tracked, expired, snapshot_dates with fields like first_seen, trend, decay_pp_per_day, lifespan_days), and constraints (TTL, start time). An agent can correctly invoke and interpret results.

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

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

Schema description coverage is 100% with parameter descriptions for 'days' and 'window'. The description reinforces these and adds context: default values, max for days (30), and that window refers to a snapshot family. It does not introduce new parameters but clarifies usage.

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

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

The description clearly states it tracks edge persistence and decay from daily snapshots, answering how long edges have existed and whether they are shrinking. It distinguishes this from a raw edge list by emphasizing temporal context (fresh vs old wide edges). The verb 'track' and resource 'edge persistence' are specific.

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

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

The description explicitly explains when to use the tool: to understand edge longevity and decay, and how the interpretation differs for edges of different ages. However, it does not explicitly state when not to use it or provide direct comparisons to sibling tools like 'polymarket_edges', though the context of temporal analysis is implied.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description aligns with these and adds useful behavioral details: walks the ladder, returns verdict and forced directional risk, and explains the dominant loss mode. It adds significant 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 structured with uppercase headers for different modes and uses bullet-like descriptions. It is relatively long but every sentence adds value. Front-loaded with core purpose, though could be slightly more concise.

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

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

Despite lacking an output schema, the description fully explains return values for both modes (top_of_book, vwap_fill_price, slippage_pp, verdict, etc.) and covers edge cases (thin_legs, forced_directional_risk). It provides complete information for correct invocation and understanding of results.

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

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

Schema covers all 4 parameters with descriptions (100% coverage). The description further clarifies parameter semantics, e.g., how size_usd interpretation differs between single-market and basket modes, and default values. It adds value by explaining mode-dependent behavior not fully captured in schema.

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

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

The description states a specific verb+resource: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It clearly distinguishes between single-market and basket modes, and differentiates from siblings by specifying it should be used before acting on polymarket_arbitrage or polymarket_edges signals.

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 THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (unfillable theoretical edges, partial basket fills leading to unhedged positions), providing clear context for when to use this tool versus alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds extensive behavioral context: two modes, response structure (leg-by-leg prices, matched spreads, safety fields like compatibility_warning and temporal_alignment), and detailed warnings about non-equivalent bet shapes and event mismatches. This far exceeds what annotations alone 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 thorough but somewhat verbose, spanning multiple paragraphs. It front-loads the purpose and key features, but includes repeated warnings (e.g., 'pre-mapped ≠ tradeable' appears twice). However, for a tool with nuanced behavior, every detail is justified, so it remains efficient despite length.

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 (3 parameters, no enums, no output schema), the description is remarkably complete. It covers all aspects: modes, response format, safety fields, temporal alignment, and compatibility warnings. Examples in the input schema further aid understanding. No gaps remain 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?

All three parameters are fully described in the input schema (100% coverage). The description adds significant semantic value by explaining the `topic` parameter's shortcut list, and how explicit parameters override topic-mapped sides. It clarifies usage in both modes, going beyond the schema's property descriptions.

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

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

The description clearly states the tool's purpose: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison. The two modes (topic shortcuts and explicit event pairs) are explicitly described, making the tool's function unambiguous.

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

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

The description provides explicit guidance on when and how to use the tool: use the `topic` parameter for pre-mapped shortcuts or explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. It also warns about compatibility issues and temporal misalignment, advising when spreads are meaningless.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no behavioral details, such as what a 'post' represents or any side effects.

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

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

The description is overly brief and under-specified. While short, it does not earn its place due to lack of useful 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 having annotations and an output schema, the description is insufficient for a tool with a single parameter. It does not explain what the tool returns or how to use it.

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

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

With 0% schema description coverage, the description must compensate but fails entirely. It gives no information about the 'id' parameter beyond its presence 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?

The description 'Single post.' is vague; it does not specify whether the tool fetches, creates, or updates a post. It is slightly better than a tautology but lacks a specific verb or action.

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

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

No guidance on when to use this tool versus siblings like 'posts' or other entity tools. The description provides no context for selection.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint=false) already declare the tool safe and idempotent. Description adds 'Paged' confirming pagination pattern but does not disclose return format or side effects beyond annotations.

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

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

Only two words, which is under-specification rather than concise. Every sentence should add value; here the description is too sparse to be 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 having an output schema and annotation richness, the description is extremely minimal. It does not describe the resource, pagination details, or parameter usage, leaving significant gaps for a tool with 3 parameters.

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

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

Schema coverage is 0%, so description should explain parameters. 'Paged' implies skip and limit are for pagination, but 'select' remains unexplained. The description only partially compensates for missing 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 'Paged posts.' indicates the tool returns a list of posts with pagination. It vaguely distinguishes from possible sibling 'post' (singular) but lacks specificity about what type of posts.

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

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

No guidance on when to use this tool versus alternatives. No mention of prerequisites or exclusions, leaving the agent without context for selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds no behavioral context beyond what is already in annotations. For example, it does not explain what happens if the ID does not exist.

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

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

The description is extremely concise (two words) but at the cost of clarity. It is front-loaded but lacks substance. For a simple tool, brevity is acceptable, but here it provides insufficient 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?

The tool is simple with one parameter and an output schema, but the description 'Single product.' does not specify the action (retrieve) or scope. It is incomplete and leaves the agent uncertain about the tool's behavior.

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

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

Schema coverage is 0% with no parameter descriptions. The description 'Single product.' does not explain the single parameter 'id' (e.g., its meaning, format, or constraints). Since schema coverage is low, the description should compensate but fails entirely.

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 'Single product.' is a minimal restatement of the tool name. It vaguely suggests retrieving one product but does not specify the action (get, fetch) or distinguish it from sibling tools like 'products' (list) or 'product_search' (search).

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

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

No guidance on when to use this tool versus alternatives. Does not mention that it retrieves a product by ID or that 'product_search' is for searching. No exclusions or context provided.

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, and destructiveHint. The description adds little (just 'paged'), but does not contradict annotations. It could mention that results are paginated and the nature of the output.

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

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

Extremely concise (2 words) but at the cost of clarity. The description is too brief to be useful, leaving the agent without sufficient 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 having an output schema (not shown) and clear annotations, the description is incomplete. It does not mention common use cases, required auth, or pagination behavior beyond the word 'paged'.

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 3 parameters (skip, limit, select) with 0% description coverage. The description 'Paged products.' provides no explanation of these parameters, forcing the agent to infer their meaning.

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

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

Description 'Paged products.' clearly indicates the tool lists products with pagination, but it does not distinguish from siblings like 'product' (single item) or 'product_search' (search results). Still, the verb and resource are identifiable.

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

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

No guidance on when to use this tool versus alternatives such as 'product', 'product_search', or 'products_categories'. The description lacks context for selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral context beyond being a list, failing to provide additional traits such as ordering, filtering, or result size.

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

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

Extremely brief (two words) but under-specified. While concise, it sacrifices clarity and does not front-load important 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 simplicity, strong annotations, and presence of an output schema, the description still fails to provide sufficient context. It does not explain the nature of the category list, when to use it, or how it relates to siblings.

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

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

There are no parameters, and schema coverage is 100%. The description adds zero parameter info, but baseline for no parameters is 4.

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

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

The description 'Category list.' vaguely indicates the tool returns a list of categories, and the name 'products_categories' suggests product-related categories. However, it lacks a specific verb and does not distinguish this tool from siblings like 'products' or 'product_search'.

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

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

No usage context or when/when-not guidance is provided. The description does not mention alternatives or scenarios where this tool should be preferred over others.

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

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

Annotations already indicate safe read operation (readOnlyHint=true, destructiveHint=false). The description adds no behavioral context beyond what annotations provide, missing details like result sorting or pagination.

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 too brief (2 words) to be effective. While concise, it lacks necessary detail and does not front-load critical information about the tool's behavior.

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

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

Despite an output schema existing, the description fails to explain what 'search' entails (e.g., full-text, fuzzy, exact). With 3 parameters and a required 'q', the description is incomplete for effective use.

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

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

Input schema has 0% description coverage; the description 'Search products' provides no information about parameters (q, skip, limit). Agent receives no guidance on how to use parameters.

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

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

Description states 'Search products' which is a clear verb+resource, but it is vague and does not differentiate from sibling tools like 'product' (single) or 'products' (list all). The purpose is clear but not specific enough to distinguish.

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

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

No guidance on when to use this tool versus alternatives (e.g., 'products', 'product'). No context for required parameters or typical use cases.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, which describe the tool's safety and behavior. The description 'Paged quotes' adds no new behavioral context beyond what annotations convey. It does not disclose pagination behavior, response structure, or any side effects.

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

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

At two words, the description is extremely concise but under-specified. Conciseness is only beneficial when it captures essential information; here it omits critical details like resource type and parameter semantics. The description should be longer to adequately inform the agent.

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 having an output schema (as indicated by context signals), the description lacks details about what data the quotes contain, how to parse results, or any formatting. The annotations and schema partially compensate, but the description itself is insufficient for an agent to fully understand the tool's behavior and output.

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 two parameters (skip, limit) with 0% description coverage. The description does not explain their roles or expected values. While 'Paged' implies pagination, the description fails to clarify that skip is the offset and limit is the page size. This forces the agent to infer meaning, increasing risk of incorrect invocation.

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 'Paged quotes' is vague. It indicates a list of quotes with pagination, but it does not specify what type of quotes (e.g., stock quotes, inspirational quotes). The title is generic. Given sibling tools like polymarket_arbitrage, it is likely financial, but the description fails to clarify the domain or distinct resource.

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

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

No guidance on when to use this tool versus alternatives like posts, products, or recipe. The description does not mention when it is appropriate to invoke or any exclusion criteria. The context of sibling tools suggests it is for fetching quotes, but no explicit usage direction is provided.

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. The description adds value by clarifying scoping (anonymous IP, BYO key hash, account ID) and listing behavior when key is omitted. 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 two sentences, front-loaded with the core purpose, and every sentence adds value. No redundant or 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?

With one optional parameter, no output schema, and sufficient annotations, the description provides complete context for usage (scope, pairing, behavior). A minor gap: no mention of return format, but acceptable.

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

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

Schema coverage is 100%, but the description adds meaning: explains that omitting the key lists all saved keys, complementing the schema definition.

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

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

The description uses specific verbs ('Retrieve', 'list') and identifies the resource ('a value saved via remember'). It clearly distinguishes the tool from siblings 'remember' and 'forget'.

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

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

The description explains when to use the tool (to look up stored context) and mentions pairing with remember/forget. It does not explicitly state when not to use it, but context is clear.

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

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

The description discloses the optional side effect of mark_read flagging events as read, which goes beyond the readOnlyHint annotation. It also explains that each event carries source and citation_uri. The annotations already indicate idempotent and non-destructive, and the description adds useful behavioral details without contradiction.

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

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

The description is four sentences long, front-loaded with the main action, and each sentence adds unique information. No redundant or vague statements. It is efficiently structured for quick parsing by an AI agent.

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 five optional parameters, no required parameters, and no output schema, the description covers filtering, mark_read behavior, polling suitability, and an alternative API endpoint. It is nearly complete, though it could mention default limit behavior (already in schema) or error handling. Overall, it provides sufficient context for safe 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?

All five parameters have descriptions in the schema (100% coverage). The description adds value by providing an example for the 'type' parameter ('sec_8k') and explaining the effect of 'mark_read' on subsequent calls. It also describes the return fields (source, citation_uri, raw event payload), compensating for the absence of an 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 clearly states the verb 'Pull fired events' and the resource 'alerts from your subscription feed'. It distinguishes itself from siblings like 'list_subscriptions' by focusing on alerts rather than subscriptions. The purpose is specific and unambiguous.

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

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

The description provides clear context on filtering by type and ISO timestamp, and mentions polling as a valid usage pattern. It also gives an alternative access method (direct API) for scripts and dashboards. While it doesn't explicitly list when not to use the tool, the alternatives are clear enough for an agent to decide.

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

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

Annotations already declare read-only and idempotent, but description adds significant detail: multiple sources (SEC, GDELT/GNews, USPTO), fallback logic, soft-failure, output structure, and parameter behavior. 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?

Description is long but well-structured and front-loaded with examples. Each sentence adds distinct information. Could be slightly more concise but not wasteful.

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 details the output structure (changes grouped by source, total_changes count, citation URIs). Also covers edge cases (fallback, soft-fail) and parameter behavior, making it complete for this 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 coverage is 100%, baseline 3. Description adds extra meaning by explaining `since` format thoroughly (ISO date or relative, with usage suggestion), confirming `type` is only 'company', and giving examples for `value`. Adds value 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 provides a change feed for a company over a time window using specific verbs and resources. It distinguishes from sibling 'entity_profile' by explicitly stating when to use that instead.

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 example queries, explains when to use ('what's new' queries), and explicitly directs to use 'entity_profile' for static profiles. Also explains fallback behavior for news sources.

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, and destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral context (e.g., whether missing IDs cause errors, return format, or caching behavior). With annotations covering the basics, the description should provide supplementary details but fails to do so.

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

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

The description is extremely short (two words), but this is under-specification, not conciseness. Important information about the tool's behavior is omitted. Every sentence should add value; here it adds almost none.

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 (one param, output schema present), the description should still state the action (e.g., 'Retrieve a recipe by ID'). The current description leaves room for misinterpretation and does not cover basic usage context.

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

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

The single parameter 'id' has no description. Schema coverage is 0%. The description 'Single recipe.' does not explain the purpose or expected value of 'id' (e.g., recipe ID format, range, or meaning). The input schema example shows a numeric ID, but without textual clarification, an agent cannot validate or interpret the parameter correctly.

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

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

The description 'Single recipe.' does not specify what action the tool performs (e.g., retrieve, create, update). It is ambiguous whether this tool fetches, shows, or manipulates a single recipe. The name and input schema with an 'id' parameter suggest retrieval, but the description fails to state the verb or resource scope clearly.

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

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

No guidance is provided on when to use this tool versus siblings like 'recipes' or other search/fetch tools. The description does not indicate prerequisites, typical use cases, or when to avoid 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the 'paged' behavior, which is useful but minimal. No additional disclosure about rate limits or response format.

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

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

Extremely concise (two words), but at the cost of clarity. While concise, it lacks sufficient information to be considered well-structured for agent understanding.

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 3 parameters, no schema descriptions, and an output schema (not shown), the description fails to provide necessary context like default pagination, parameter interactions, or return structure. Incomplete for effective tool selection.

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

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

Schema description coverage is 0%, and the description provides no explanation for the three parameters (skip, limit, select). The agent has no insight into parameter roles, defaults, or valid values from the description.

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

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

The description 'Paged recipes.' indicates it deals with recipes and involves pagination, but does not specify the exact operation (e.g., listing, searching). It is slightly better than a tautology, but still vague compared to siblings like 'recipe'.

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

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

No guidance on when to use this tool versus alternatives. Sibling tools like 'recipe' (singular) and 'product' are not distinguished, and there is no mention of prerequisites or context.

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

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

Annotations already provide readOnlyHint=false and destructiveHint=false. Description adds useful context about scoping by identifier and 24-hour retention for anonymous sessions, but does not explain idempotentHint=true (overwrite 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?

Single focused paragraph with clear front-loading of purpose. Every sentence adds needed context without redundancy.

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

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

Given no output schema, explanation of return value is unnecessary. Describes usage context, persistence, and integration with sibling tools. Complete for a simple store operation.

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

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

Input schema covers both parameters with descriptions (100% coverage). Description adds examples of key naming conventions and clarifies value can be any text, adding value 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?

Clearly states it saves data for reuse across conversations, with specific examples of what to store (resolved ticker, target address, etc.). Distinguishes itself from siblings 'recall' and 'forget'.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget. Also provides context on persistence differences for authenticated vs. anonymous users.

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, and idempotent. The description adds useful behavioral context: 'cascades through several lookup endpoints internally' and 'replaces 2-3 manual lookups,' which is valuable beyond the annotations.

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

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

The description is somewhat lengthy but every sentence adds value. It front-loads common queries, then details types. Could be slightly more concise, but structure is logical 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?

No output schema, but the description explains return fields for each type. It covers key aspects (what is returned, types supported) but omits error handling or limits. Given the complexity and annotations, it is adequate.

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 extensive meaning: example values ('AAPL', 'ozempic'), detailed return structure per type (ticker+CIK+company_name vs RxCUI+ingredient+brand), and citation URIs. This greatly enriches the parameter semantics.

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

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

The description clearly states the tool resolves a name to a canonical identifier, with specific examples ('ticker', 'CIK', 'RxCUI'). It distinguishes itself by listing supported entity types and their return formats, making the purpose unambiguous.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. It does not explicitly state when not to use or mention alternative tools, but the context is sufficient for an AI agent.

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, etc., indicating a safe operation. The description adds beyond that: it explains internal delegation to ai_visibility_check, ranking logic, and output details (score, confidence, signal density). 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 the main action, no fluff. Every sentence adds value: first states purpose, second explains mechanism, third gives use case. Perfectly concise.

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

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

With 4 parameters, no output schema, and siblings like ai_visibility_check, the description provides enough: specifies return type (ranked list with score, confidence, signal density) and uses an example. It does not cover error handling or edge cases, but that is acceptable given the annotations.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds extra meaning: that the first entity in the array is treated as the 'subject' for narrative purposes, and that 'context' is shared across probes. This goes beyond the schema's brief 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 compares AI visibility across multiple entities, ranks them, and returns a sorted list. It uses specific verbs ('compare', 'probes', 'ranks') and resources ('AI visibility', 'entities'), and the mention of 'side-by-side' distinguishes it from single-entity tools like ai_visibility_check.

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

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

The description gives clear context: 'Useful for competitive AI-marketing audits' and an example question. It mentions that probes are done with ai_visibility_check, hinting at the alternative for single entities. However, it does not explicitly state when not to use this tool or contrast with siblings like compare_entities.

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

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

The description adds significant behavioral details beyond annotations: it explains the composite fan-out, partial failure degradation with 5-30s latency for bundlephobia, and that sources_failed lists timeouts. Annotations already indicate safe operation (readOnly, idempotent, non-destructive), so no contradiction.

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

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

The description is informative but slightly verbose. It is front-loaded with the core purpose, and every sentence contributes value. Could be tightened slightly but remains efficient.

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

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

Without an output schema, the description fully explains the return structure: summary block (with enumerated fields), per-advisory detail, links, and alternative versions. It also covers latency, degradation, and ecosystem constraints, making it complete for this tool's complexity.

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

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

Schema coverage is 100%, but the description adds value by clarifying that scoped packages are accepted for the package parameter and that version defaults to latest when omitted. These details are not 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?

The description clearly states the tool's purpose: a composite check for adding npm packages, combining deps.dev and bundlephobia. It distinguishes from sibling tools by specifying the ecosystem (NPM only) and mentioning that other ecosystems fall under deps.dev:version.

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

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

Explicitly says 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also provides guidance on when not to use (e.g., for non-NPM ecosystems) and an alternative tool (deps.dev:version directly).

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds significant detail beyond annotations: specifies BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, a 200K char cap with truncation flagging, and output includes offsets and scores. This fully discloses 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?

The description is concise yet comprehensive, front-loading the core function and use case, followed by technical details. Every sentence adds value—no filler. It effectively uses structure to separate usage guidance from implementation specifics.

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 no output schema, the description explains the return values: top-N passages with character offsets and similarity scores, plus a truncation flag. It also covers constraints (200K char cap) and pairing guidance. It is mostly complete, though the exact output format is not specified.

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 3 parameters. The description adds value by reiterating the max length for 'text', providing example queries for 'query', and confirming the default and range for 'limit'. It goes beyond the schema with the truncation flag detail.

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 performs semantic search within a fetched record, saving context and returning relevant passages. It distinguishes itself by specifying the use case for large records and pairing with a specific sibling tool, making its purpose unambiguous.

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

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

The description explicitly advises using this tool when a record is too large for the prompt, saving context and allowing verification via offsets. It also mentions pairing with ask_pipeworx_grounded, but does not explicitly state when not to use it or list alternatives beyond that pair.

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

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

The description adds extensive behavioral details beyond annotations: auth requirements (Pipeworx OAuth), delivery limitations (10/day SMS cap, webhook auto-disable), and type-specific parameter constraints. It also notes that the webhook signing secret is returned only once.

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 fairly long but well-organized and front-loaded with the main purpose. Every sentence provides value, though slight trimming could improve conciseness.

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

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

Given the tool's complexity (multiple subscription types, delivery channels, auth), the description covers all necessary aspects: return value, auth, type-specific params, delivery limitations, and webhook security. No output schema exists, but the description compensates.

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

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

With 100% schema coverage, the description enriches each parameter with concrete examples and usage context, such as sec_8k item codes, polymarket_edge topics, and delivery object field details, adding significant 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 the verb 'Create' and the resource 'proactive monitoring subscription', with a specific outcome 'Returns the new subscription id'. 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 auth requirements, supported types with examples, and delivery channel limitations. However, it lacks explicit when-not-to-use or direct comparison to sibling tools like list_subscriptions.

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 as true, and destructiveHint false. Description adds that the tool returns category-bucketed example questions with tool+argument shapes, providing 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?

Description is relatively long but well-structured with examples upfront and clear purpose. Each sentence adds value, though it could be slightly more concise.

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

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

Given one optional parameter, no output schema, and a simple use case, the description fully explains the return format and usage scenario. It covers what the tool does, when to use it, and what to expect.

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

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

Input schema has one optional parameter 'topic' with description. Description adds examples ('finance', 'pharma') and clarifies omission gives a cross-category spread, 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 the tool is an onboarding entry point to suggest example questions, with a specific verb and resource. It distinguishes from siblings like 'ask_pipeworx' by emphasizing it is for initial exploration.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions alternatives like 'ask_pipeworx, entity_profile, compare_entities'. Also describes optional topic parameter for focus.

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 destructiveHint. The description adds no additional behavioral context (e.g., pagination behavior, default limits, or ordering) beyond what is implied by the annotations.

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

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

The description is extremely concise (two words), but this comes at the cost of completeness. It lacks structure and does not sufficiently explain the tool's functionality.

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 having an output schema, the description omits essential details such as what a 'todo' contains, ordering, pagination specifics, and how it differs from other list tools. The context is insufficient for accurate use.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'paged' which hints at limit/skip but does not explain their roles or provide any parameter-specific semantics, leaving the agent to infer meaning.

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

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

The description 'Paged todos' is minimal and does not clearly state what the tool does beyond indicating it returns todos with pagination. It fails to distinguish from sibling list tools like 'posts' or 'products', which might also be paged.

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

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

No guidance is provided on when to use this tool versus alternatives. There are no criteria, prerequisites, or exclusions mentioned, leaving the agent without context for selection.

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

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

Description adds ownership enforcement and clarifies that the row is deactivated not deleted, preserving historical events. This complements annotations (destructiveHint=false). No contradictions found.

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

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

Two concise sentences with no wasted words. Front-loaded with action and followed by key nuance.

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

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

For a simple tool with one parameter and annotations present, the description covers ownership, deactivation behavior, and historical data availability completely.

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

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

Schema coverage is 100% and the description does not add significant meaning beyond the schema's parameter description. Baseline 3 is appropriate.

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

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

The description clearly states 'Cancel a subscription by id' with specific verb and resource, and distinguishes from siblings like 'subscribe' by adding ownership enforcement and deactivation 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?

Usage is implied (you can only cancel your own subscriptions) but no explicit when-to-use or when-not-to-use guidance, nor alternatives mentioned.

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, and destructiveHint, so the description's lack of behavioral detail is less critical. It adds no additional behavioral context but does not contradict 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 extremely short, which is concise but at the expense of substance. It fails to provide necessary information in the few words used.

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 has an output schema and multiple siblings, the description is insufficient. It does not clarify the expected output or differentiate from 'users'.

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

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

With 0% schema description coverage, the description must explain the parameter. 'Single user.' implies the id parameter identifies a user, but provides no details on format, role, or constraints.

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 'Single user.' does not specify the verb or action. It vaguely indicates the resource but lacks clarity on whether it retrieves, updates, or does something else. This is insufficient for distinguishing from sibling tools like 'users'.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'users' or other related tools. The description offers no context or constraints.

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 read-only, idempotent, and non-destructive behavior. The description adds no additional behavioral context (e.g., pagination behavior, result ordering, error conditions). It fails to leverage the opportunity to describe traits beyond the structured 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?

While extremely concise (two words), the description is under-specified and does not earn its simplicity. It lacks necessary information, so conciseness is detrimental rather than efficient.

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

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

Given the lack of parameter documentation and the minimalist description, the tool is poorly specified. Even with an output schema present, the agent cannot understand what the tool does or how 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 description coverage is 0%, and the description does not explain any of the three parameters (skip, limit, select). The agent must infer their meaning from names alone, which is insufficient for correct invocation.

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 'Paged users' is vague, lacks a verb (e.g., list, get), and does not clarify that it returns a paginated list. It only adds the word 'Paged' to the title, which slightly distinguishes it from the sibling 'user' tool but remains insufficient for an agent to understand the exact 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?

No guidance is provided on when to use this tool versus alternatives like 'user' (singular) or other list tools. There is no mention of filtering, sorting, or prerequisites.

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. The description adds behavioral details: returns a verdict, structured form, actual value with citation, and percent delta, and notes that it replaces multiple sequential calls. No contradiction.

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

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

Description is concise and front-loaded with key usage cues. Every sentence adds value. Could be slightly shorter but effective.

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

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

Given a single parameter, no output schema, and a specific domain, the description adequately covers input, output, domain limitations, and purpose. It is complete enough for effective agent use.

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

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

Only one parameter 'claim' with schema description already covering format. The description provides examples but does not add substantial new semantic detail beyond what the schema provides. Schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: to verify natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It lists trigger phrases and distinguishes from sequential alternatives.

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 ('whenever the agent needs to check factual correctness') and specifies the supported domain (company-financial claims). Does not explicitly state when not to use, but the domain limitation is clear.

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