Webflow

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds key behavioral context beyond annotations: that passing an _apiKey proxies calls to Anthropic and the user pays directly, and the return format includes per-model scores, confidence, signals, and 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 concise: two sentences with no fluff. The first sentence states purpose and output, the second adds key details (default model, optional API key, billing). Every sentence earns its place.

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

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

Given 4 parameters, no output schema, and annotations, the description is complete. It explains the return structure (per-model {score, confidence, signals, raw_response} + combined view) and parameter behavior (defaults, optional fields). This compensates for the missing output schema.

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 good descriptions. The description adds value by explaining defaults ('Default model is Workers AI Llama-3.3-70b (free)'), the billing model for Anthropic ('BYO key — you pay Anthropic directly'), and the purpose of the 'context' parameter ('disambiguates common names'). This goes beyond the schema, raising it above baseline 3.

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

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

The description clearly states the tool's purpose: probing LLMs for knowledge about a topic and scoring visibility. It specifies the verb 'Probe', the resource 'LLMs', and the output 'score (0-100) per model'. This is specific and distinguishes from sibling tools like 'scan_competitor_ai_presence' which focuses on competitors, though it doesn't explicitly differentiate.

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

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

The description provides clear usage context: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It also explains how to use the tool with defaults and optional API key for Anthropic. However, it does not explicitly state when NOT to use this tool or compare it to alternatives like 'scan_competitor_ai_presence', leaving room for ambiguity.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds significant context: it routes to 3,336 tools across 747 sources, fills arguments, and returns structured answers with citation URIs, fully disclosing behavior without contradiction.

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

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

The description is front-loaded with the key preference instruction and then lists domains and examples. It is informative without being overly verbose, earning its keep.

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

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

Given the tool's complexity and the simple single-parameter schema, the description provides a comprehensive overview of when and how to use it. It is sufficient for an AI agent to understand its capabilities, though more details on handling ambiguous queries could enhance completeness.

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

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

Schema coverage is 100% and explains aliases. The description adds semantic context about the types of questions it handles and the preference over web search, which goes beyond the schema's syntax.

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 routes questions to a vast set of tools and returns structured citations, and it contrasts with web search. It provides specific examples of domains and queries, making the purpose clear and differentiated from siblings.

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

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

The description gives clear guidance to prefer this tool over web search for factual queries, listing many domains and example questions. It does not explicitly mention when not to use it or alternatives among siblings, but the 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?

The description discloses many behavioral traits beyond annotations: fan-out behavior, response shapes with specific fields, resolver contract with confidence levels, parent event extraction, news fallback handling, safety mechanisms for low-confidence matches and closed markets, and wide-spread market notes. All align with annotations (readOnlyHint=true, etc.) and add significant value.

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

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

The description is long but well-structured with sections (overview, classifiers, fan-outs, response shapes, resolver, parent event, news, safety). It is front-loaded with the main purpose. While dense, it avoids unnecessary redundancy. Slightly verbose but justified by complexity.

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

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

Given the tool's complexity (multiple classifiers, fan-outs, edge cases) and lack of output schema, the description is comprehensive. It covers input, behavior, response fields, safety, and edge cases (low-confidence, closed markets, wide spreads). An agent can safely and correctly invoke the tool with this 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 coverage is 100%, and the description adds meaning beyond schema descriptions. It explains the market parameter accepts slug, URL, or question text; depth parameter effects; and include_raw describes response size impact (e.g., keeps responses under ~20KB). Examples further enhance understanding.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies the verb 'research', the resource 'Pipeworx data', and the mechanism (resolves market, classifies, fans out). It distinguishes from sibling tools like polymarket_edges or polymarket_arbitrage by focusing on deep single-bet research.

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 when to use it: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides extensive examples of classifier fan-outs, but does not explicitly state when not to use it or mention alternative tools. However, the context of siblings makes the usage clear.

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

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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds valuable context such as data sources (SEC EDGAR/XBRL, FAERS), specific metrics, the fix for off-calendar fiscal years, and sorted results. No contradiction with annotations.

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

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

The description is well-structured with a clear first sentence stating the main purpose, followed by use cases, detailed behavior per type, and advantages. All sentences are informative 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 moderate complexity and no output schema, the description adequately covers key aspects: entity types, data sources, metrics, sorting, and citation URIs. It provides enough context for an agent to understand usage and expected output.

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

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

Schema coverage is 100% with both parameters described. The description enhances understanding by explaining the meaning of values per entity type (e.g., 'tickers/CIKs' for company, 'names' for drug) and provides examples. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'Compare 2-5 companies (or drugs) side by side in one call.' It specifies the verb (compare), resource (companies/drugs), and scope. It distinguishes from sibling tools by being the only comparison tool.

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

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

The description explicitly lists use cases ('compare X and Y', 'X vs Y', 'which is bigger', rank-by-metric) and explains what each entity type provides. It doesn't include when-not-to-use, but context is clear enough. The note about replacing 8-15 sequential lookups provides practical 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 indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining the tool returns top-N most relevant tools with names, descriptions, full input schemas with examples, and that each result is ready to call without a second lookup. 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 clear and front-loaded with the key action, but it is slightly verbose. It effectively communicates the tool's purpose without unnecessary repetition.

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

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

Given no output schema, the description adequately explains what the tool returns (top-N tools with schemas) and its use case. It is fairly complete for a discovery tool, though it lacks details on pagination or error handling.

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

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

Input schema coverage is 100% with clear descriptions for each parameter, including aliases. The description does not add significant meaning 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 specifies the tool finds tools by describing data or task, listing many domains (SEC filings, financials, FDA drugs, etc.). It clearly distinguishes from sibling tools which are more specific, and states it returns top-N relevant tools with full schemas.

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 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear guidance on when to use it versus directly calling a specific sibling tool.

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

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

Beyond readOnly/idempotent annotations, description details return fields, data sources (SEC, USPTO, GDELT, GLEIF), fallbacks, and known issues (patents sunset). 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?

Well front-loaded with purpose, followed by structured return details. Slightly verbose but all sentences add 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?

No output schema, but description thoroughly enumerates return fields, explains nuances (e.g., fundamentals sorting, patent status), and covers limits (up to 5 filings). Adequate for agent to understand 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 coverage is 100% and description adds value: explains type enum restriction, value format (ticker or CIK), and explicitly states names not supported, using resolve_entity as prerequisite.

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 explicitly states purpose: 'Get everything about a US public company in one call.' Provides specific use cases ('tell me about X') and distinguishes from siblings by noting when to use resolve_entity for names and that this replaces 10+ pack tools.

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

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

Clear when-to-use guidance: user asks for company info. Exclusions: names not supported, must use resolve_entity first. Implicitly indicates US public companies only, which aligns with type enum.

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 confirms destructive nature ('delete') matching annotations (destructiveHint=true). Adds context about clearing sensitive data, which goes 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?

Extremely concise: two sentences, no wasted words. Front-loaded with the action, making it easy to scan.

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 no output schema, the description covers when to use, what it does, and pairing with siblings. No gaps.

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

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

Schema coverage is 100% and the schema already describes the 'key' parameter. The description does not add extra meaning beyond what's in the schema, so baseline score of 3 is appropriate.

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

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

Description clearly states 'Delete a previously stored memory by key', using a specific verb and resource. It also highlights pairing with 'remember' and 'recall', distinguishing it 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: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with related tools, providing clear usage 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 indicate readOnly, idempotent, non-destructive. The description adds that it fetches the page, extracts content, and outputs standard format. No contradictions. With annotations covering safety, the description adds useful process 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 a single well-structured paragraph, front-loaded with purpose and output. Every sentence adds value; 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?

No output schema exists, but the description explains the output format (llms.txt markdown) and typical uses. It covers the essential behavioral and usage context for a simple 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%, so the schema already documents both parameters. The description mentions 'Full URL' and 'max_links' with default/limit, adding slight value but not significantly beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, explains the purpose (AI crawler indexing), output format, and provides explicit use cases. It distinguishes itself from sibling tools, none of which share similar functionality.

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

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

The description lists specific use cases: getting a client's site indexed, drafting for own project, auditing competitor visibility. While it lacks explicit when-not-to-use or alternatives, the context is clear and the tool is distinct from siblings.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the bar is lower. The description adds value by specifying the returned fields: 'item's id, draft/archived status, last published time, and fieldData.' This is beyond what annotations provide and helps the agent understand output structure.

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 action and purpose. Every sentence serves a purpose: first sentence states what it does and what it returns; second sentence gives usage context. No redundancy 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?

Given the simple parameter set (2 required strings), rich annotations covering safety and idempotency, and no output schema, the description is complete. It covers purpose, usage order, and return fields. The agent has enough information 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 coverage is 100% with descriptions for both parameters ('The id of the CMS item to retrieve' and 'The id of the Webflow CMS collection'). The description does not add additional parameter semantics beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states 'Get a single item (record) from a Webflow CMS collection by its id.' This distinguishes it from sibling tools like list_collection_items (which lists) and get_site (which gets site details). The verb 'get' and resource 'item' are 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 recommends using this tool 'after list_collection_items to read one piece of Webflow site content in detail.' This provides clear context for when to use it and implies it should be used after listing. It does not explicitly mention when not to use, but the guidance is sufficient for an agent to select correctly among siblings.

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

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

Annotations already declare readOnlyHint and idempotentHint; description adds value by specifying returned metadata fields (display name, preview URL, etc.) 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?

Single, front-loaded sentence with no redundancy; every word 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 single-parameter, read-only tool with full schema coverage and no output schema, the description sufficiently explains inputs and outputs.

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 description; the tool description explains that site_id comes from list_sites, adding 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 'Get metadata for a single Webflow site by its id' and lists specific fields, distinguishing it from related tools like list_sites.

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

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

Explicitly advises using after list_sites and before browsing CMS collections, providing good context but no when-not-to-use or alternatives.

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

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, establishing non-destructive, idempotent behavior. The description adds transparency by stating it returns paginated items and listing specific fields (id, draft/archived status, last published time, fieldData), which goes beyond annotations without contradiction.

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

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

The description is only two sentences, each earning its place. The first sentence defines the action and gives examples, the second specifies return fields and usage. It is front-loaded and has no redundancy.

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

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

Given the tool's simplicity (list operation), rich annotations, full parameter coverage in schema, and no output schema, the description sufficiently explains what the tool returns (id, status, time, fieldData) and its purpose. It is complete 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?

The input schema has 100% coverage, with each parameter already described in detail. The description does not add new semantic meaning beyond noting that collection_id refers to the collection whose items to list, which is already implied by the schema description.

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

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

The description clearly states the tool lists items (records) in a Webflow CMS collection, with concrete examples like blog posts, products, or team members. It distinguishes itself from sibling tools like get_collection_item (single item) and list_collections (list collections) by specifying it retrieves multiple items with specific fields.

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 the agent to use this tool to read the actual content stored in a Webflow collection. While it does not explicitly list when not to use it or name alternatives, the purpose is clear and the sibling context implies it is for listing multiple items rather than a single item or collection metadata.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds value by specifying the exact fields returned (id, display name, slug, etc.) and its role in discovering CMS structure, which is not captured by annotations alone.

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 three concise sentences: action + returns + usage guidance. No superfluous words; every sentence serves a purpose.

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

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

Given the simple tool (one parameter with full schema description, comprehensive annotations, no output schema), the description is complete. It covers purpose, return fields, and usage context without gaps.

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

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

Schema coverage is 100% with a clear description for site_id. The tool description does not add additional parameter details beyond the schema, so a baseline score of 3 is appropriate per guidelines.

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 ('List the CMS collections'), the resource ('on a Webflow site'), and specifies the return fields (id, display name, slug, etc.). It distinguishes from sibling tools like list_collection_items and get_collection_item by focusing on collection metadata, not items.

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 'before listing or reading collection items', providing clear context for when to use it. While it doesn't explicitly mention when not to use or list alternative tools, the guidance is direct and helpful for sequencing API calls.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating no side effects. The description adds that results are scoped to the 'connected account' and lists specific return fields, providing useful 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?

Two sentences: first states action and return fields, second gives usage guidance. No unnecessary words, efficiently 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 parameters and no output schema, the description covers the tool's purpose and usage. Could mention pagination or limits, but overall 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?

The input schema has no parameters, so schema coverage is 100%. The description need not explain parameters, and the baseline for 0 parameters is 4.

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

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

The description clearly states 'List all Webflow sites accessible to the connected account', specifying the verb (list), resource (Webflow sites), and scope. It lists the returned fields, distinguishing it from siblings like 'get_site' and 'list_collections'.

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

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

The description advises using this tool 'before reading their CMS collections or site content', providing clear usage context. It does not explicitly state when not to use it, but the guidance 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 are minimal (no hints), but description adds key behavioral details: free, doesn't count against tool-call quota, rate-limited to 5 per identifier per day, and team reads daily. 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?

Concise 4-sentence description that is front-loaded with purpose, each sentence providing unique and essential guidance. 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 feedback tool with 3 parameters (100% schema coverage) and no output schema, the description covers purpose, usage, limitations, and message content comprehensively.

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

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

Input schema has 100% coverage with descriptions, but description adds value by explaining how to structure the message (specific, 1-2 sentences, 2000 chars max) and re-emphasizes enum 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?

Description clearly states the tool's purpose: reporting bugs, missing features, data gaps, or praise to the Pipeworx team. This distinguishes it from sibling tools like 'ask_pipeworx' or 'discover_tools'.

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

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

Explicitly specifies when to use and how to formulate feedback: in terms of Pipeworx tools/packs, not pasting end-user prompts. Also provides rate limits and quota exemption.

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 nondestructive. The description adds valuable context: data source (CF analytics-engine), privacy (no PII), and caching (5min-1h). No contradiction exists.

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

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

The description is concise, front-loaded with the core function, and uses bullet points for use cases. Every sentence adds unique information with no redundancy.

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

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

For a simple tool with one optional parameter and no output schema, the description fully covers purpose, behavior, return content (top tools, packs, call volume), and appropriate use. No gaps remain.

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 only parameter (window) is fully described in the input schema with enum values and a clear description. Schema coverage is 100%, so the description adds no additional semantics. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: returns trending tools and packs on Pipeworx based on recent call volume. It specifies the verb (returns) and resource (trending data), and distinguishes from sibling tools by focusing on aggregated, self-referential signal rather than individual queries or discovery.

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

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

Three explicit use cases are listed: discovering hot data sources, confirming canonical tools, and aligning use case with agent demand. This provides clear guidance on when to use the tool and what it helps accomplish, without needing alternative tool names.

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 goes beyond annotations by detailing response structure, filter logic (Jaccard similarity, placeholder fraction), and edge cases (null arb signal). There is no contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint).

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

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

The description is well-structured with clear mode separation but is somewhat verbose. It front-loads the main purpose and details follow, making it easy to scan.

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 absence of an output schema, the description comprehensively explains return values (opportunities array and partition_check object) and covers edge cases (placeholder fraction threshold). It provides all necessary context for an agent to understand tool behavior.

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

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

Schema description coverage is 100%, but the description adds value by explaining how each parameter maps to the two modes and provides an example for the topic parameter. It reinforces the schema descriptions without redundancy.

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

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

The description clearly states the tool's function: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific explanation, differentiating it from sibling tools like polymarket_edges.

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

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

The description provides clear guidance on when to use each mode (event for single event, topic for cross-event) and explains the cross-event mode's advantage. However, it does not explicitly state when not to use the tool or mention 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 readOnly, idempotent, non-destructive. The description adds significant behavioral details: 1h KV caching, Fed bets excluded from ranking, diagnostics for empty segments, and model-specific assumptions (e.g., Kelly caps, sports alpha values). This goes well beyond the structured fields.

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 highly detailed but verbose, with dense technical explanations that may overwhelm the agent. It front-loads purpose but then lists three model families with specific formulas. While informative, it 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?

With 9 parameters and no output schema, the description compensates by detailing the response structure (by_segment, diagnostics), explaining why segments may be empty, and providing filter/slippage defaults. Covers edge cases and is thorough for a complex scanning 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%, but the description adds meaning beyond field definitions—e.g., explains default values, how slippage is subtracted, and reasoning behind filters like min_liquidity and max_spread_pp. Provides actionable guidance for each parameter.

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 and returns opportunities where Pipeworx data disagrees, with a specific use case 'what should I bet on today'. It distinguishes itself from siblings like polymarket_arbitrage by focusing on disagreement-driven opportunities and providing three model families.

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 specifies it's for discovering opportunities without paging hundreds of markets, and mentions tradeable-edge knobs. However, it doesn't explicitly compare to sibling tools like bet_research or polymarket_arbitrage, nor does it 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 readOnly, idempotent, and non-destructive. The description adds extensive behavioral context, including compatibility warnings (matched_pairs, skipped_cross_type), temporal alignment checks, and bet shape equivalence checks, ensuring the agent understands edge cases and when results are reliable.

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 detailed and well-structured with sections for modes, response, and safety fields. While dense, every sentence provides necessary information for complex behavior. Could be slightly more concise, but given the complexity, the structure is effective.

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

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

Given the tool's complexity, no output schema, and significant edge cases, the description is comprehensive. It covers response structure (leg prices, spreads, compatibility warnings, temporal alignment), making it complete for an agent to understand outcomes and when spreads are valid.

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

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

With 100% schema description coverage, the description adds significant value by explaining the two modes, listing all topic shortcuts, and clarifying override behavior for explicit tickers. It provides examples and context beyond the schema's parameter 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 it is a cross-venue spread tool comparing Kalshi and Polymarket for the same resolving question, with explicit modes (topic shortcuts vs. explicit tickers). It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue analysis.

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

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

Provides explicit guidance on when to use (to compare prices between venues) and when not to use (with compatibility warnings explaining scenarios where spreads are not meaningful). It also details the two modes of operation, helping the agent choose based on available parameters.

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 scoping to identifier and pairing with sibling tools, which complements annotations without contradiction.

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

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

Description is concise with three sentences, front-loaded with purpose, and each sentence adds value. No fluff.

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

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

Given the simplicity of the tool and strong annotations, the description covers all essential aspects: retrieval, listing, scoping, and relation to other tools.

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

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

Schema coverage is 100% and already describes the 'key' parameter. Description repeats the listing behavior but adds no new semantic 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 the tool retrieves a value saved via 'remember' or lists all keys when omitted. Uses specific verb 'retrieve' and resource 'value/keys'. Distinguishes from sibling tools by naming '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?

Provides guidance to use for looking up stored context and explicitly pairs with 'remember' and 'forget'. Lacks explicit when-not-to-use but context is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds rich behavioral context: parallel fan-out to SEC EDGAR, GDELT/GNews fallback, USPTO soft-fail, and detailed return structure (changes[], total_changes, URIs). There is no contradiction with annotations.

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

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

The description is efficiently structured: starts with a clear purpose question, then usage examples, then technical details (sources, fallback, parameter formats), and finally an explicit alternative. Every sentence adds value without unnecessary verbosity.

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

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

Given the complexity (multiple data sources, fallback logic, various parameter formats) and the absence of an output schema, the description covers all essential aspects: return format (structured changes grouped by source, total_changes, URIs), behavior of each source, and limitations (USPTO soft-fail). The agent has sufficient context to use 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?

Although the input schema already describes each parameter, the description adds significant value: examples for 'since' (ISO date and relative shorthand like '7d', '30d', '3m', '1y'), a recommendation ('Use “30d” or “1m” for typical monitoring'), and clarification that 'value' accepts ticker or CIK. This goes beyond the schema's 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 begins with a clear question 'What's new with a company in the last N days/months?' and lists concrete use cases like 'updates on Y' and 'news on Apple this month'. It explicitly distinguishes itself from the sibling tool 'entity_profile' by stating when to use that instead, making the purpose and differentiation crystal clear.

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 and an explicit alternative: 'Use entity_profile instead when you want the static profile...'. It also outlines the tool's behavior (fan-out to multiple sources, fallback logic) which guides the agent on when to invoke this tool versus others.

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

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

Discloses persistence differences (authenticated vs anonymous) and scoping. Annotations already indicate idempotent and non-destructive, so description adds contextual depth 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?

Single paragraph, front-loaded with purpose. A bit wordy (e.g., 'across this conversation or across sessions' is redundant) but still efficient.

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

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

For a simple key-value store with good annotations, all necessary info is provided: scope, persistence, pairing with other tools.

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

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

Schema already has 100% coverage with descriptions for key and value. The description reinforces 'key-value pair' but doesn't add new parameter-level 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?

Specifically states 'save data' and provides concrete examples like 'resolved ticker, target address, user preference'. Distinguishes from siblings by mentioning 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 pairs with recall and forget, providing clear when-to-use and alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that each call cascades through multiple internal endpoints and replaces 2-3 manual lookups, providing rich behavioral context beyond annotations. No contradictions.

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

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

Description is concise, front-loaded with purpose, then details. Two sentences with no fluff. 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?

Given two entity types and internal cascading lookups, the description covers return values, input formats, and replacement of manual steps. No output schema, but description provides sufficient info for agent to use tool effectively.

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

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

Schema coverage is 100%, baseline 3. Description adds value by explaining accepted input formats (ticker, CIK, name for company; brand/generic for drug) and what each returns, enriching 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 user-spoken names to canonical IDs, with specific verb 'resolve' and resource 'canonical/official identifiers'. It distinguishes itself from siblings by explaining that other tools require these IDs, positioning it as a first-step utility.

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 when you have a name but need an ID', providing clear when-to-use guidance. Also details supported types and outputs. Lacks explicit when-not-to-use, but context implies if IDs already known, not needed.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, so agent knows it's safe. Description adds behavioral context: probes each entity, ranks, and returns a ranked list. No contradictions or missing disclosures.

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

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

Three sentences: purpose, method, example. Front-loaded with main action, efficient without 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?

No output schema, but description specifies return: ranked list with score, confidence, signal density. Sufficient for agent to understand 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?

Schema coverage is 100% and description adds meaning: entities first is subject, models defaults to workers-ai, context disambiguates. This enriches parameter understanding beyond 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?

Description clearly states it compares AI visibility across multiple entities, probes with ai_visibility_check, ranks results, and identifies most/least recognized. Differentiates from sibling ai_visibility_check (single entity) and compare_entities (generic).

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 provides clear context for competitive AI-marketing audits with an explicit use case example. Does not specify when not to use or explicitly name alternatives, but purpose is clear enough to guide 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?

Goes far beyond annotations by detailing fan-out to two services, return fields, partial failure handling, and startup latency for new versions. No contradiction with annotations; adds significant 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?

Packed with useful information in a single paragraph, front-loaded with purpose. Could be slightly improved with bullet points or clearer sectioning, but no extraneous sentences.

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

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

Despite no output schema, the description explains the return format, covers inputs, outputs, ecosystem, and failure modes comprehensively. Complete for the 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%, and description adds minimal new info beyond schema. It confirms default version behavior, but that is already implied in the schema. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: a composite check for adding npm packages, consolidating multiple data sources. It uses a specific verb+resource combination and distinguishes from siblings by focusing on npm packages and combining multiple checks in one call.

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

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

Explicitly says when to use: when agents ask about safety, popularity, size, or cost of adding a package. Also specifies ecosystem limitation (npm only) and directs users to deps.dev:version for other ecosystems, providing clear alternatives.

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

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

The description adds significant behavioral context beyond the annotations: it details the return format ('verdict... actual value with pipeworx:// citation'), explains the efficiency benefit ('Replaces 4–6 sequential calls'), and specifies the supported domain (US company-financial). Annotations already declare the tool is read-only, idempotent, and non-destructive, and the description does not contradict 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 concise yet comprehensive: two well-structured paragraphs. The first paragraph clearly states the purpose and usage trigger, while the second covers scope, outputs, and efficiency. Every sentence serves a purpose, and the most critical information is front-loaded.

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

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

Given the tool has a single parameter, no output schema, and rich annotations, the description provides all necessary context: what it does, when to use, return format, domain scope, and efficiency gains. No gaps are present for an agent to misuse the tool.

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

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

The schema covers 100% of the single parameter with a brief description. The tool description adds meaningful value by providing examples of valid claim formats ('Apple's FY2024 revenue was $400 billion') and emphasizing that it is natural-language, which helps the agent construct appropriate inputs.

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 defines the tool's purpose: 'Fact-check, verify, validate, or confirm/refute a natural-language factual claim'. It uses specific verbs and identifies the resource (natural-language claim), distinguishing it from siblings like 'bet_research' by highlighting that it replaces multiple sequential calls.

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

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

The description explicitly states when to use the tool ('Use when an agent needs to check whether something a user said is true') and provides example phrasings. It also notes the scope limitation ('v1 supports company-financial claims...'). However, it does not explicitly state when not to use it or suggest alternatives, which would merit a 5.

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