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 readOnly, openWorld, idempotent, non-destructive. The description adds that the tool routes queries to 4,819 tools, fills arguments, and returns structured answers with stable pipeworx:// citation URIs. This consistent context complements the annotations without contradicting them.

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 densely packed with valuable information, front-loaded with the key directive. While slightly long, every sentence serves a purpose: scope, usage criteria, examples, and alternatives. It is well-organized but could be marginally 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?

Covers all essential aspects: tool function, when to use, alternatives, example inputs, how it works (routing to sources, citations), and tier availability. With no output schema, it adequately describes return format. The description is complete for an agent to invoke the tool correctly.

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

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

Schema coverage is 100% with aliases. The description enriches parameter meaning by listing example data domains and question types (SEC filings, FDA drugs, etc.), giving practical guidance beyond 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 explicitly states the tool answers factual questions using a vast set of verified sources, distinguishes itself from siblings (web search, ask_pipeworx_grounded, deep_research), and provides clear examples. The verb 'ask' and resource 'Pipeworx' are clearly defined.

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

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

Provides explicit when-to-use guidance (e.g., 'START HERE for most questions', 'Use whenever the user asks...') and when-not-to-use with specific alternatives (ask_pipeworx_grounded, deep_research). It also explains that it works on every tier and is a fast single call.

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 false. The description goes significantly beyond: explains the extra LLM call cost, return shape with fields like 'evidence', 'confidence', 'refusal_reason', and lists specific refusal reasons. This is rich behavioral context.

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

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

The description is densely informative but not overly long. It front-loads the key value proposition ('Hallucination-resistant answer mode') and each sentence provides necessary detail. Slightly verbose but still effective; minor room for tightening.

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 parameters, many sibling tools, high-stakes usage), the description covers the return structure, refusal reasons, cost trade-off, and appropriate usage contexts. No output schema exists, but the description sufficiently explains the output shape.

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 confirms that the question parameter accepts natural language and lists its aliases ('query, q, prompt, text, input'), but adds no additional meaning beyond what the schema already provides.

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

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

The description clearly states 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes itself from 'ask_pipeworx' by emphasizing extraction from tool result only and explicit refusals. It identifies the specific verb (answer extraction) and resource (tool results), effectively distinguishing from sibling tools.

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

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

Explicitly states when to use: 'Use whenever an answer will be quoted, cited, or acted on...' and when not: 'prefer ask_pipeworx for casual lookups.' Provides contexts like financial, legal, medical, and public statements. This is model 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?

Annotations already declare safety (readOnly, idempotent, non-destructive). Description adds specific output details (comment body, postId, user info) and pagination behavior, exceeding annotation coverage.

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

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

Two sentences, no wasted words. Front-loaded with purpose, followed by key features.

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?

Output schema exists, annotations cover safety. Description covers pagination and return fields. Minor gaps: no mention of max limit or error handling, but adequate for a simple read tool.

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

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

Schema description coverage is 0%, and description only mentions 'supports limit and skip' without explaining their semantics, defaults, or constraints. Insufficient compensation for missing parameter docs.

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 verb 'Fetch' and resource 'list of fake comments' from DummyJSON, distinguishing it from sibling tools like posts or products.

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?

Implies usage for fetching paginated comments but provides no explicit guidance on when to use this tool vs alternatives like posts or 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 (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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds important behavioral context: requires account, executes parallel tool invocations, returns findings with gaps, and warns about empty gaps for breaking news. 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 relatively long but front-loaded with critical info (account requirement, alternative). Some redundancy (e.g., 'NOT open-web search' is implied). Overall well-structured but could be 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 the tool's complexity (parallel execution, 1259 sources), the description covers expectations (findings packet, gaps, citation style) and appropriate use cases. No output schema, but description explains return packet sufficiently. Adequate for agent understanding.

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

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

Schema coverage is 100% with both parameters described. Description adds nuance: depth levels 'quick=3, standard=5 (default), thorough=8 (paid plans)' and notes that broad/multi-part questions are fine. This adds marginal 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's purpose: 'Grounded multi-source research across Pipeworx's 1259 STRUCTURED data sources... decomposes your question into facets, routes to tools in parallel, returns findings packet.' It distinguishes from sibling ask_pipeworx by noting that tool is for single lookups or breaking news.

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

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

Provides explicit when-to-use and when-not-to-use: 'If not signed in, use ask_pipeworx instead', 'For a single lookup use ask_pipeworx', 'For BREAKING news prefer ask_pipeworx'. Also notes depth 'thorough' requires paid plan.

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?

Beyond the annotations (readOnlyHint, idempotent, etc.), the description details internal logic: walks child markets, performs partition_sum checks with >3pp deviation, uses Jaccard similarity for cross-event pairing, and explains the fill check against live CLOB depth. It also mentions filtering placeholder slugs and handling thin legs. 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 lengthy but well-structured with sections and bullet points. Every sentence adds value for a complex tool. Slightly verbose compared to simpler tools, but the density of information is appropriate for the complexity. Could be slightly more concise without losing meaning.

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 modes, internal logic, response structure, fill check), the description is remarkably complete. It explains response fields (opportunities[], partition_check), the fill check conditions, and references a sibling tool for custom sizing. No output schema, but the description covers expected outputs sufficiently.

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

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

Schema coverage is 100%, but the description adds substantial meaning: for event parameters, it explains the partition check and date-axis/threshold-axis ordering; for topic, it explains cross-event scanning with similarity thresholds and partition filter. Example values (e.g., 'fed-decision-may-2026') are provided. This far exceeds basic schema descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies three distinct modes (trending_scan, event, topic) with clear differentiation, making it easy for an agent to understand what the tool does and when to use each mode.

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?

Excellent usage guidance: explicitly recommends when to use each mode (no args for trending scan, event for specific market, topic for cross-event scanning). Also provides example slugs and seed questions, and references polymarket_fill_risk for custom sizing. The description gives clear context on when to choose each parameter.

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 declare readOnlyHint, idempotentHint, and destructiveHint false, so the agent knows this is a safe, read-only operation. Description adds that the post is 'fake' and returns specific fields, but no additional behavioral traits beyond annotations.

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

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

Two sentences with no wasted words. Action is front-loaded. Efficiently conveys the purpose and expected output.

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

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

Given the simple input schema, annotations covering safety, and presence of output schema, the description is nearly complete. Missing only edge case behavior (e.g., what happens if id not found) and any rate limiting information.

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 0% description coverage. Description clarifies the 'id' parameter is numeric and from DummyJSON, matching the example. However, no further detail on format, range, or behavior for invalid ids.

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 verb 'Fetch', the resource 'single fake blog post', the method 'by numeric id', and the source 'DummyJSON'. It distinguishes from sibling tool 'posts' which presumably fetches multiple posts. The return fields are explicitly listed.

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 but not explicitly stated. No guidance on when not to use this tool or when to prefer alternatives like 'posts' for multiple posts. The description does not provide context for choosing this over other tools.

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

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

Adds value beyond annotations by specifying external source (DummyJSON), fake nature, and return fields. Annotations already indicate read-only, idempotent, non-destructive; description supplements with operational 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?

Two sentences, front-loaded with purpose, efficient in word count. Every sentence adds value without repetition.

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

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

Covers purpose, pagination, field selection, and return fields. With output schema present, return details are sufficient. Lacks differentiation from siblings like 'post' or ordering behavior, but adequate for a simple list 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?

With 0% schema description coverage, the description must compensate. It mentions 'limit, skip, and field selection via `select`' but does not explain parameter syntax, constraints, or defaults. Minimal addition over schema types.

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 'Fetch a paginated list of fake blog posts' with specific verb and resource. Distinguishes from sibling 'post' (singular) by indicating plural and pagination.

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?

Implies usage for list retrieval via 'paginated list', but does not explicitly mention when to use this vs alternatives like 'post' or 'comments'. No when-not or alternative guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety and idempotency. The description adds useful context about the data source (DummyJSON) and return fields, which enriches behavioral understanding beyond annotations.

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

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

The description is a single, well-structured sentence followed by a brief list of return fields. Every word contributes value, and the primary action is front-loaded for quick comprehension.

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

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

For a simple fetch-by-id tool with an output schema, the description is complete. It clearly states what the tool does, what parameter it takes, and what fields are returned, leaving no ambiguity.

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 compensates by explaining the parameter 'numeric id' and its purpose. While it does not specify range or format, it adds essential meaning beyond the schema's type definition.

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

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

The description clearly states the action ('Fetch') and the resource ('a single fake product by numeric id'), and lists the returned fields. It effectively distinguishes from sibling tools like 'products' (plural) and 'product_search' by specifying single product retrieval by ID.

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

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

The description implies usage when a specific numeric ID is available, but does not explicitly contrast with alternatives like 'product_search' or 'products_categories'. It provides clear context but lacks explicit exclusions or guidance on when not to use.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds pagination behavior, field selection capabilities, and return fields, providing additional 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?

Two sentences with no wasted words. First sentence clearly states purpose, second lists supported parameters and returns. Front-loaded and efficient.

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

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

Has output schema (not shown but indicated), but description still explains return fields. Covers pagination and field selection sufficiently for a simple list 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 lacks descriptions (0% coverage). Description compensates by explaining limit/skip as pagination, select as field selection, and lists return fields, adding meaning beyond basic schema types.

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 'Fetch a paginated list of fake products from DummyJSON', which is a specific verb+resource. Distinguishes from siblings like 'product' (singular) and 'product_search' (search based).

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

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

Description mentions features like limit, skip, and select, but does not provide explicit guidance on when to use this tool vs alternatives like 'product' or 'product_search'. Context is implied but not formalized.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so description adds context about returning a full list and the data source (DummyJSON). No contradictions, and it complements annotations well.

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

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

Two sentences, front-loaded with the main action and examples, followed by usage guidance. No wasted words; every sentence adds value.

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

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

For a simple no-parameter tool with full annotations and output schema, the description fully explains what it returns and when to use it. No missing critical information.

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 no parameters and 100% coverage, so description does not need to add parameter info. Baseline is 4 for zero parameters; it provides no extra but none is needed.

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 returns the full list of product category names, with examples (e.g., smartphones, laptops, fragrances). Verb 'Return' and resource 'category names' are specific, and it distinguishes from sibling tools 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?

Explicitly says 'Use before filtering products by category,' providing clear usage context. However, it does not mention when not to use or list alternative tools, but for this simple case it is sufficient.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds transparency by mentioning the data source (DummyJSON), the search mechanism (by keyword `q`), and pagination support via `limit` and `skip`. 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?

Two sentences with no wasted words. The first sentence states the primary purpose, the second adds return fields and pagination. Front-loaded and efficient.

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

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

Given the simplicity of the tool (3 params, 1 required, no enums, with output schema), the description covers the essential behavior: keyword search, returned fields, and pagination. It does not explain the output schema, but that is provided separately. Slightly better would be a brief note on the output structure, but not essential.

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 description fully carries the burden. It explains `q` as a search keyword and `limit`/`skip` for pagination, adding meaning beyond the schema's type-only definitions. Could be improved by clarifying default values for `limit` and `skip`.

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 'Search', the resource 'fake DummyJSON products', and specifies the key parameter `q`. It also distinguishes from sibling tools like 'products' (likely list all) and 'product' (single entity).

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 implicitly guides usage for keyword search but does not explicitly state when to use this tool vs alternatives like 'products' for listing all or 'product' for a single product. No explicit when-not-to-use or prerequisite conditions are 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?

Description adds context beyond annotations by specifying the data is 'fake inspirational quotes' and indicating return fields, without contradicting any annotations.

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

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

Two concise, front-loaded sentences cover purpose, data source, pagination, and return format with no wasted words.

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

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

Given the tool's simplicity, output schema presence, and rich annotations, the description fully covers what an agent needs to invoke 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?

With 0% schema description coverage, the description compensates by explaining that 'limit and skip' support pagination, adding meaning to the parameter names.

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

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

The description clearly states the action ('Fetch'), resource ('paginated list of fake inspirational quotes'), and output ('quote text and author name'), distinguishing it from sibling tools which are unrelated content types.

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

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

Provides clear context for when to use (need quotes) and mentions pagination support (limit/skip). No explicit exclusions, but siblings are all distinct tools, so no confusion arises.

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, openWorldHint, idempotentHint, and destructiveHint (all safe). The description adds behavioral context by specifying the data source ('DummyJSON') and the exact return fields, without contradicting annotations.

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

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

The description is a single, well-structured sentence that front-loads the verb and resource, then lists the returned fields. No wasted words.

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

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

Given the presence of an output schema and the simple nature of the tool (fetch by ID), the description fully covers purpose, parameter, and return values. It is complete for an idempotent, read-only lookup 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?

With 0% schema description coverage, the description compensates by clarifying that 'id' is numeric and its role in fetching a single recipe. This adds meaning beyond the schema's bare type and examples.

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

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

The description clearly states the action ('Fetch a single fake recipe'), the resource ('recipe'), and the scope ('by numeric id from DummyJSON'). It lists the specific fields returned, distinguishing it from sibling tools like 'recipes' (plural) and other unrelated tools.

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

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

The description tells when to use this tool ('to fetch a single recipe by numeric id from DummyJSON'). It implies a single item retrieval, contrasting with the sibling 'recipes' tool. However, it does not explicitly state when not to use it or name alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by disclosing that data is from DummyJSON and is fake, and details return fields. No contradiction with annotations.

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

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

The description is two sentences long, front-loaded with the main action, and every word adds value. No unnecessary information.

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

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

Given the presence of an output schema, the description covers pagination, parameters, and return fields. It lacks error handling or data source details beyond 'DummyJSON', but is sufficient for a straightforward read tool with 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 description coverage is 0%, but description compensates by explaining that 'limit', 'skip', and 'select' control pagination and field selection. However, it does not specify parameter types or constraints, leaving some ambiguity.

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 'Fetch' and resource 'paginated list of fake recipes from DummyJSON', distinguishing it from sibling tools like 'recipe' for a single recipe. It also lists specific return fields, making 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 implies usage for fetching recipe lists with pagination and field selection, but lacks explicit guidance on when to use this tool versus alternatives like 'recipe' or 'products'. No when-not-to-use or alternatives are 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 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 indicate readOnly, idempotent, non-destructive. The description adds context about source (DummyJSON) and return fields, but does not disclose additional behavioral traits like rate limits or error handling. No contradiction with annotations.

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

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

The description is a single sentence that conveys key information efficiently. It is front-loaded with the action and resource. Minor improvement could include structuring into bullet points for readability.

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

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

Given the tool's simplicity and the presence of an output schema, the description adequately covers purpose, source, pagination parameters, and return fields. It is complete enough for an agent to use correctly.

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

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

Schema has 0% description coverage for the two parameters (skip, limit). The description only mentions 'Supports limit and skip' without explaining their semantics, defaults, or constraints. The description does not sufficiently compensate for the lack of schema descriptions.

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

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

The description clearly states the tool fetches a paginated list of fake todo items from DummyJSON, specifying the supported parameters and return fields. It is distinct from siblings like 'posts' or 'products' which cover different resources.

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

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

The description implies usage for retrieving fake todo items with pagination but does not explicitly state when to use this tool over alternatives or provide exclusions. No guidance on 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?

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 indicate read-only, idempotent, non-destructive. Description adds context: source is DummyJSON (external API), returns fake user data with specific fields (name, email, phone, etc.). 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?

Single sentence clearly stating purpose and key details. No wasted words.

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

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

For a simple tool with one required parameter and an output schema, the description fully explains the operation and lists returned fields, making it 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 has 0% description coverage, but description adds that id is numeric and includes an example {id:1}. This compensates for lack of schema description.

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

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

Description clearly states the tool fetches a single fake user by numeric id from DummyJSON, returns full profile including specific fields. Distinguishes from sibling 'users' tool which likely lists multiple 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 explicit usage guidance or alternatives mentioned. Implicitly for a single user by id, but does not say when to prefer this over other tools like 'users' or 'search'.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by mentioning returned fields (name, email, etc.) and parameter support, but doesn't disclose additional behavioral traits like pagination limits or data source reliability beyond 'fake users'.

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 tight sentences, front-loaded with purpose, no redundancy. Every sentence adds essential 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?

With an output schema present, return values are covered. Missing details like max limit, exact select syntax, and error handling, but sufficient for a simple fetch tool given annotations provide safety context.

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

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

Schema coverage is 0% with no parameter descriptions. The description mentions 'limit, skip, and field selection via `select`', adding some meaning. However, it doesn't explain the format of 'select' (e.g., comma-separated) or valid values, leaving ambiguity.

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 fetches a paginated list of fake users from DummyJSON, with specific verb and resource. It implicitly distinguishes from sibling tools like 'user' (singular) by indicating it returns multiple 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 on when to use this tool vs alternatives like 'user' (singular fetch) or other listing tools. Lacks context for choosing this over similar paginated tools like 'posts' or 'products'.

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.