Mailchimp

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: the default model is free, Anthropic requires a BYO API key, and the key is passed through directly. It also explains the return structure, covering what the agent can expect 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 concise at ~5 sentences, front-loaded with the core action and result. Every sentence adds essential information without redundancy.

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

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

For a tool with 4 parameters, no output schema, and moderate complexity, the description covers all key aspects: purpose, parameters, return structure, and cost implications. It provides sufficient context 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 description coverage is 100%, but the description adds extra meaning: clarifying that 'models' can include 'workers-ai' (free default) and 'anthropic' (requires _apiKey), and explaining that 'context' helps disambiguate. Since there is no output schema, the description compensates by detailing the return format (per-model fields).

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 an entity and scoring visibility. It uses specific verbs ('probe', 'score') and resources ('LLMs', 'visibility'). It distinguishes itself from sibling tools like 'ask_pipeworx' or 'compare_entities' by being a specialized AI visibility audit 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 mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') which provide clear context for when to use the tool. However, it does not explicitly state when not to use it or recommend alternatives among siblings, missing a bit of exclusion 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?

The description discloses internal behavior: it routes questions to one of many tools, fills arguments, and returns structured answers with citation URIs. This adds value beyond annotations, which already indicate safe read-only 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 well-structured and front-loaded with the key advice to prefer over web search. It is efficient, though slightly verbose in listing domains. 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 the tool's complexity as a router to many sources, the description adequately explains its function and output format. No output schema exists, but the description clarifies that citations are provided, making the tool complete for an agent.

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

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

With 100% schema coverage, the baseline is 3. The description provides example questions but does not add new semantic information beyond what the schema says. The parameter descriptions are already explicit about the question field.

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 answer factual questions using structured data from many sources, preferring it over web search. It lists specific domains and gives examples, making the purpose highly specific and distinguishable.

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 to prefer this tool over web search for factual questions and provides a list of example queries. However, it does not differentiate from sibling tools like 'ask_pipeworx_grounded' or 'deep_research', which could also be relevant.

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 context: costs an extra LLM call, returns structured success/failure responses with specific refusal reasons, and is hallucination-resistant. No contradictions.

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

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

The description is a single paragraph that is efficient and front-loaded with the core purpose. Every sentence adds value, though it is somewhat dense. Could be slightly more structured, but very concise overall.

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 fully explains the return JSON structure, success/failure cases, and refusal reasons. It covers all necessary behavioral aspects for a high-stakes 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 100% with all parameters and aliases documented. The description adds minimal extra beyond the schema, merely noting 'natural language.' 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: 'Hallucination-resistant answer mode for high-stakes reads.' It explains the routing, extraction process, and distinguishes it from the sibling ask_pipeworx by emphasizing that it only uses tool result data.

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

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

Explicitly tells when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with examples. Also tells when not to: 'prefer ask_pipeworx for casual lookups.' Clearly differentiates from sibling.

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=true, idempotent=true, destructive=false. The description goes far beyond by detailing fan-out behavior, response shapes, resolver contracts, parent event extraction, news fallback, safety mechanisms, wide-spread markers, and resolution-rule risk. 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 dense but well-organized with labeled sections and bullet points, making it scannable. It prioritizes the core purpose upfront, though brevity is sacrificed for completeness given the tool's complexity.

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

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

Without an output schema, the description thoroughly documents all response fields (market, analysis, evidence, resolver contract, parent event, news, safety flags, resolution rules) and edge cases (low confidence, closed markets, wide spreads). It covers everything an agent needs 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?

Schema coverage is 100% with all three parameters described. The description adds value by providing concrete examples of market_input formats, explaining quick vs thorough depth, and detailing the effect of include_raw. This exceeds the 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 researches Polymarket bets by pulling Pipeworx data, resolving markets, classifying, and returning evidence. It explicitly distinguishes its use case ('should I bet on X') from sibling tools like polymarket_edges or validate_claim, with specific examples.

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 scenarios ('Use for...') and concrete examples, but does not explicitly contrast with alternative tools or state when not to use it. However, it offers practical guidance on inspecting confidence fields and handling edge cases.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. The description adds significant context: data sources (SEC EDGAR/XBRL, FAERS, FDA), off-calendar fiscal year handling, sorted results, citation URIs. No contradiction with annotations.

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

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

The description is moderately long but front-loaded with example queries and key guidance. Every sentence adds value; a slight trimming could improve conciseness without losing content.

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 (2 entity types, multiple data sources), the description covers purpose, usage, data details, output format, and efficiency claim. No output schema, but description covers return format adequately.

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

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

Schema coverage is 100% with enum and array descriptions. The description enriches by explaining what each type retrieves (company vs drug data) and gives concrete examples, 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?

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs in one parallel call, with example queries. It distinguishes itself from sequential single-pack lookups, strongly implying the alternative.

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing direct when-to-use guidance. It could add when not to use (e.g., single entity), but the guidance is clear enough.

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

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

Beyond annotations (readOnlyHint=true, etc.), the description details behavioral traits: it returns verbatim evidence, confidence, source, fetched_at, stable citations, and explicitly mentions gaps[] for unanswered facets. It also notes that it never invents answers and gives expected time (15-60s).

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 main purpose and key differentiators. However, it is somewhat lengthy and includes details that could be streamlined without losing 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?

Given no output schema, the description adequately explains return format (structured findings packet with evidence, gaps, etc.). It covers prerequisites, behavior, edge cases (gaps), and performance expectations, making it 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%. The description adds meaning by explaining the depth enum values (quick=3, standard=5, thorough=8) and clarifies that the question parameter should be broad/multi-part, which is key for proper 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 'Grounded multi-source research in ONE call' and explains the decomposition and parallel execution. It distinguishes itself from sibling 'ask_pipeworx' by specifying that this tool is for broad or multi-part questions, while 'ask_pipeworx' is for single lookups.

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

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

The description explicitly states when to use the tool ('Use for broad or multi-part questions') and when not to ('use ask_pipeworx for single lookups'). It also mentions prerequisites (Pipeworx account) and limitations (depth:'thorough' requires a 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 declare readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns ready-to-call results with schemas, no need for 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?

Purpose is front-loaded. Listing many domains is helpful but slightly verbose. Overall clear and well-structured.

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

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

No output schema, but description thoroughly explains what is returned (top-N tools with schemas and examples). Complete for a discovery tool.

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

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

Schema covers all 6 parameters (100% coverage). Description adds context: natural language description, alias acceptance, default/max limit. 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?

Clearly states it finds tools by describing data or task, listing many domains (SEC filings, FDA drugs, etc.). Distinct from siblings which are specific tool endpoints.

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 need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available', providing strong contextual guidance.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds details on data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), soft-fail for patents, and specific return fields, going 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 front-loaded with examples and key guidance. While comprehensive, it could be slightly more concise; however, the extra detail is justified given the tool's complexity.

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

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

Without an output schema, the description provides a reasonable overview of return fields and data sources. Missing exact output structure but sufficient for a complex multi-source tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds context (e.g., zero-padded CIK, names not supported) and clarifies usage, slightly exceeding schema info.

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

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

The description lists concrete example queries and clearly states the tool provides a full cross-source profile of a US public company. It distinguishes from sibling tools like resolve_entity by specifying input requirements and preferred usage.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views, and warns that names are not supported, directing users to resolve_entity first. Provides 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 indicate destructive and idempotent behavior. Description adds context about clearing sensitive data, enhancing transparency beyond annotations.

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

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

Two focused sentences, front-loaded with purpose, no extraneous information.

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

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

For a simple one-parameter tool with no output schema, the description adequately covers purpose, usage, and behavioral 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 already covers the single parameter fully. Description adds no new semantic detail beyond what 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?

Clearly states the tool deletes a previously stored memory by key, and distinguishes itself from siblings by pairing 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?

Provides specific use cases (stale context, task done, clear sensitive data) and related tools, though does not explicitly state 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 mark the tool as readOnly, idempotent, and non-destructive. The description adds behavioral details: it fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown. This goes beyond the annotations by describing the process and output format.

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

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

The description is concise with three main sentences plus a bulleted list of use cases. It is front-loaded with the primary purpose and contains no filler, making it efficient for an agent 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?

With no output schema, the description adequately explains the output as a 'single text blob' in standard llms.txt format. It covers the input and expected outcome but omits details on error handling or edge cases, which is acceptable for a straightforward generation tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description mentions 'any URL' and 'key links', which adds context to the 'url' parameter and relates to 'max_links', but does not significantly extend 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 explicitly states the tool generates an llms.txt file for a given URL, using clear verb 'generate' and resource 'llms.txt'. It also differentiates from siblings by specifying the output format and use cases like client indexing or competitor auditing.

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 (client indexing, own project, competitor audit) under 'Useful for', providing context on when to employ the tool. However, it lacks explicit guidance on when not to use it or direct comparison to sibling tools like ai_visibility_check.

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. The description adds value by specifying the default scope (active subscriptions) and listing the returned fields, which informs the agent of what to expect 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?

Two sentences efficiently cover purpose, return fields, and usage guidance without any superfluous content. 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 the simple one-parameter tool and no output schema, the description adequately covers the return structure (listing fields) and usage. The annotations further assure idempotency and read-only nature, making the definition complete.

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

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

Schema coverage is 100% for the single parameter 'include_inactive', so the description does not need to elaborate. However, the description implicitly references the parameter by mentioning 'active subscriptions', which adds minimal extra context.

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

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

The description clearly states 'List the caller's active subscriptions' and lists the specific return fields (id, type, etc.), making the tool's purpose unambiguous and distinct from sibling tools like subscribe/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 explicitly advises when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear contextual guidance for the 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=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds specific return fields (name, member count, engagement metrics, configuration), which adds some value beyond annotations but does not disclose additional behavioral traits.

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

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

Two sentences: first captures purpose, second provides usage example and return details. No redundant information, fully front-loaded, and 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?

The tool has an output schema and high schema coverage, so the description is adequate. It explains the action and lists key return fields. Minor improvement could include noting authentication requirements or that it's read-only (though annotations cover that).

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds an example for the audience ID ('abc123def456'), which is helpful but not essential. Baseline 3 is appropriate as schema already carries the load.

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 'Get detailed settings and stats for a specific audience,' specifying the verb (get), resource (audience), and scope (specific). This clearly distinguishes it from siblings like mailchimp_list_audiences (list all) and mailchimp_list_members (list members).

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 mentions 'Pass the audience ID' as a usage instruction, but provides no guidance on when to use this tool versus alternatives (e.g., when to use list_audiences instead). No explicit when-not-to-use or alternative tool mentions.

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, and destructiveHint=false. The description adds context by specifying what data (settings, tracking, performance, send history) is returned, which is useful but not extensive.

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. No wasted words, highly 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?

The description adequately covers the tool's purpose and return types. An output schema exists to provide further detail, so the description is sufficiently complete for this low-complexity 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% and both parameters have descriptions. The description provides an example format for campaign_id (e.g., 'abc123def456'), adding meaning beyond the schema's generic description.

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

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

The description uses a specific verb ('Get') and resource ('campaign'), includes an example ID, and lists the types of data returned (settings, tracking, performance, send history). This clearly distinguishes it from sibling tools like mailchimp_list_campaigns.

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 the tool should be used when a campaign ID is known and full details are needed. It does not explicitly mention when not to use it or point to alternatives, but the context is clear given the sibling list tool.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that the tool returns 'audience names, member counts, and engagement stats,' but does not elaborate on pagination, rate limits, or other behavioral aspects 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 two sentences with no superfluous words. The first sentence immediately conveys the purpose, and the second provides alternative guidance. It is 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 the tool's simplicity, annotations, and presence of an output schema, the description is mostly complete. It mentions the return fields but could optionally note pagination behavior or scope limits for complete context.

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

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

The input schema covers all 3 parameters with descriptions (100% coverage). The description does not add any additional meaning or context to the parameters, so a 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 'View all audiences in your account' with a specific verb and resource. It also differentiates from the sibling tool 'mailchimp_get_audience' by noting it is for detailed settings, ensuring no ambiguity.

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

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

The description explicitly directs users to 'Use mailchimp_get_audience for detailed settings,' providing clear guidance on when to use an alternative tool. This satisfies the need for 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 already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds value by specifying the returned fields (title, type, status, timestamps).

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 fluff, front-loaded purpose statement. 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 the presence of a full output schema and 100% schema coverage, the description is complete enough. It covers purpose, sibling differentiation, and basic return data.

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 parameters are already well-documented. The description does not add additional semantic information 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 'View all email campaigns' and lists the returned fields. It distinguishes from the sibling mailchimp_get_campaign by directing users to that tool for full details.

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 for when to use this tool and explicitly recommends mailchimp_get_campaign for full details, though it does not list explicit exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description adds limited behavioral context by listing return fields (emails, status, merge fields). It does not discuss pagination behavior beyond schema, rate limits, or authentication nuances.

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, focused sentence with an example ID. It is front-loaded with the core action and efficient, containing no unnecessary words.

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

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

Given the presence of a complete schema (100% coverage) and output schema, the description is mostly complete. Missing minimal context like default pagination limits (count default 10) but these are in the schema. Slightly lacks non-obvious usage notes.

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 does not add new semantic meaning to parameters; it only mentions return values. Parameters are already well-documented 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 verb 'Get' and the resource 'subscribers in an audience', with a concrete ID example. It distinguishes from sibling tools like 'mailchimp_list_audiences' by specifying it returns members of a specific audience.

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 audience members but does not explicitly state when to use versus other mailchimp tools or provide usage boundaries. No alternatives or exclusions 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?

The description discloses important behaviors beyond annotations: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. It also mentions that the team reads digests daily and signal affects roadmap. Annotations provide no contradictory info.

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 moderately long but each sentence adds value. It front-loads the purpose, then provides usage guidelines, and ends with behavioral details. No redundant or unnecessary sentences. Could be slightly more concise, 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 and three parameters, the description covers input comprehensively: explains the feedback types, context usage, and message guidelines. It does not detail post-submission behavior (e.g., no response expected), but for a feedback tool this is 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%, so baseline is 3. The description adds meaning by explaining each enum value for 'type', describing the optional context fields (pack, tool, vertical), and specifying message length constraints (1-2 sentences, 2000 chars max). This provides useful context 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: sending feedback about bugs, missing features, data gaps, or praise to the Pipeworx team. It specifies the verb 'Tell' and resource 'Pipeworx team', and distinguishes from sibling tools by focusing on feedback about Pipeworx tools/packs.

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

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

The description explicitly states when to use this tool (e.g., 'when a tool returns wrong/stale data', 'when a tool you wish existed isn't in the catalog'). It also provides guidance on what to include ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt'). No explicit alternatives or when-not-to-use are mentioned, but the use cases are clear enough.

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

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

Adds significant context beyond annotations: specifies data source (CF analytics-engine), privacy (no PII), aggregation method (just counts), and caching behavior (5min-1h). Aligns with all annotations (readOnly, idempotent, not destructive).

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

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

Well-structured with bullet points and short paragraphs. Every sentence adds value; no redundancy. Efficient at ~100 words with clear sections.

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 read-only nature with one parameter and no output schema, the description is fully complete. It covers return values, use cases, privacy, and caching.

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

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

Schema coverage is 100% with a single enumerated parameter 'window'. Description adds semantic value by explaining the trade-off: shorter windows for 'hot right now', longer for steady-state demand.

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

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

Clearly states it returns top tools, top packs, and total call volume over a window. Uses specific verbs like 'returns' and lists what is provided. Distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on trends and aggregate data.

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

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

Provides three explicit use cases for when to use the tool, such as discovering hot data sources or confirming popular tools. However, it does not explicitly state when not to use or mention alternatives, though the context is clear enough.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent with description. Description adds rich behavioral detail: algorithm steps, fill check, semantic anchors, partition filter, and explicit 'do not trade' guidance. 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 detailed and well-structured with bolding and clear sections, but is somewhat dense. Front-loaded with the critical requirement. A bit verbose for an AI agent but appropriately so given 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?

Covers all key aspects: two modes, algorithm, fill check, response structure (opportunities, partition_check, fill_check), and references to sibling tools. No output schema, so description compensates by explaining expected response format.

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 significantly extends meaning with concrete examples, accepted formats (full URLs), and detailed behavior for each parameter (e.g., event slug vs. topic seed question).

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It specifies two modes (event and topic) with examples, and distinguishes from sibling tools by mentioning polymarket_fill_risk for custom sizing.

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 requires one of event or topic, and explains when to use each mode. Provides a recommendation ('recommended for a specific market') and links to sibling polymorphic fill risk for custom sizing. However, doesn't explicitly contrast with other sibling tools like polymarket_edges.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant behavioral context: caching (1h KV-level), response structure (by_segment with diagnostics), and detailed model methodologies. No contradictions with annotations. This provides rich transparency 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 verbose (approx. 600 words) and densely technical. It front-loads the purpose in the first sentence but then delves into extensive methodology for each segment. While thorough, it lacks conciseness and could be restructured for quicker parsing. Every sentence provides value, but the length is not minimal for 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 tool's complexity (9 parameters, no output schema), the description comprehensively covers response structure (by_segment, diagnostics), caching behavior, and tradeable-edge knobs. It explains what to expect in each segment and how to interpret results, including edge and Kelly details. This fully equips an agent to use the tool without requiring external documentation.

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 detailed parameter descriptions. The tool description adds context like 'edge_pp_net (after slippage)' and explains the rationale behind min_partition_leg_kelly, but this primarily enriches understanding of output or intent rather than input parameter semantics. The description does not significantly augment parameter 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 the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the target use case ('what should I bet on today') and distinguishes itself from paging through hundreds of markets, providing a clear and specific verb-resource pairing.

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?

While the description implies usage for discovering betting opportunities and mentions it's built for agents, it does not explicitly state when to use this tool versus alternatives like polymarket_arbitrage or bet_research. No when-not-to-use guidance is provided, leaving the agent to infer context from the 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?

The description extensively details behavior beyond annotations: response structure (tracked, expired, snapshot_dates), how decay is computed (absolute edge_pp_net, signed negative for SELL YES), and snapshot creation conditions (cache miss gaps). No contradiction with annotations.

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

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

The description is dense with information but somewhat lengthy. It is front-loaded with purpose but includes many technical details that could be condensed.

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 fully explains the response fields (tracked[], expired[], snapshot_dates[]) with details on edge trend, decay, lifespan, and gaps. It also covers limitations, making it 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 the description adds value: explains days parameter (lookback, default 14, max 30) and window parameter (snapshot family, default '1wk'), plus clamping behavior for days.

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: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers specific questions about edge duration and shrinkage, distinguishing it from related tools like polymarket_edges by focusing on temporal analysis.

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

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

The description provides clear context on when to use the tool, explaining that a fresh wide edge differs from a 3-week-old edge. It includes limitations (60-day TTL, decay from daily closes) but does not explicitly mention alternatives or 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 indicate read-only, idempotent, non-destructive. The description adds behavioral details: walks the ladder, returns verdict, and highlights risks like stranded positions in basket mode. 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?

Well-structured: starts with purpose, then requirements, then modes, then return fields, then usage guidance. Every sentence adds value. Length is justified given 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?

Fully covers return fields (top_of_book, vwap_fill_price, slippage_pp, etc. for single-market; theoretical_sum, realizable_sum, capture_ratio, etc. for basket). No output schema, but description compensates 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%, but the description adds critical context: explains side defaults and auto-detection for basket, specifies how size_usd is interpreted differently for single-market vs basket (spend vs target proceeds vs settlement notional), and clarifies parameter requirements.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes two modes (single-market and basket) with specific examples of return 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?

Explicitly tells when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', and explains why (thin books, partial fills leading to directional risk).

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, such as compatibility conditions, temporal alignment, and skipped leg-pair counters. It explains why spreads may be invalid (non-equivalent bet shapes, semantic mismatches, temporal gaps). No contradictions with readOnlyHint or idempotentHint.

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

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

The description is dense and front-loaded with core purpose, but it is somewhat verbose. Every sentence adds value, but could be slightly more concise without losing information.

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

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

Given no output schema, the description explains the response structure (leg prices, spread, safety fields). It covers all critical aspects: modes, compatibility, temporal alignment, and counters. Complete for a tool of this 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%. The description adds meaning by explaining how the two modes interact: explicit parameters override topic-mapped sides. It lists valid topic values and specifies that explicit tickers override the topic mapping, providing context beyond the schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic shortcuts and explicit tickers) and distinguishes from related tools by focusing on cross-venue comparison.

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 each mode (topic for pre-mapped shortcuts, explicit for custom pairs). It warns that most pre-mapped topics currently return compatibility warnings and that real spreads are rarer than the list suggests, guiding appropriate 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=true and destructiveHint=false. Description adds scoping ('to your identifier') and pairing details, providing useful context beyond annotations.

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

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

Two sentences, front-loaded with action, no wasted words. Every sentence adds meaningful 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-only tool with one optional parameter, the description is complete: covers purpose, usage, scoping, and pairing. No output schema needed for such retrieval.

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 of the 'key' parameter. The description adds value by explaining the effect of omitting the key (list all), which goes 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?

Clearly states 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument)' — specific verb+resource+dual mode. Distinguishes from sibling tools 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?

Explicitly says 'Use to look up context the agent stored earlier' and mentions pairing with remember/forget. Could be stronger on when not to use, but the use case is clear given sibling 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 the tool as read-only, idempotent, and non-destructive. The description adds behavioral details beyond this, such as the effect of 'mark_read:true' (flags returned events as read so next call only shows newer ones) and that polling is acceptable. It does not contradict annotations.

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

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

The description is four sentences, each carrying distinct value: purpose, output details, filter usage, and additional behavior. It is front-loaded with the core action and avoids fluff. 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?

Despite no output schema, the description explains what each returned event contains (source, citation_uri, raw payload). It covers filtering with 'type' and 'since', the 'mark_read' behavior, and even provides an alternative endpoint. Given the tool's simplicity (list recent alerts), this is fully 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 100% coverage with descriptions for all 5 parameters, but the description adds meaningful context: it gives an example for 'type' (e.g., 'sec_8k'), clarifies that 'since' is an ISO timestamp, and explains that 'mark_read:true' affects subsequent calls. This adds value beyond the basic 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 pulls fired events from the subscription feed, specifies that it returns the most recent alerts with source, citation_uri, and raw payload, and mentions filtering options. This distinguishes it from sibling tools like 'subscribe' or 'list_subscriptions' by focusing on reading alerts.

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 for using the tool, including that polling works fine and that the same feed is available at a URL for scripts and dashboards. However, it does not explicitly state when not to use this tool or mention alternative sibling tools for similar purposes.

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

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint. Description adds details: data sources (SEC EDGAR, GDELT/GNews, USPTO), failover behavior, soft-fail for patents, and return structure (changes grouped by source, total_changes, URIs). No contradiction.

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

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

Front-loaded with example queries. Slightly verbose but every sentence adds value. Could be tightened slightly but overall well-structured.

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

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

For three parameters with full schema coverage and detailed description, it's complete. No output schema but description mentions return structure. Contextual completeness is high.

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

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

Schema coverage is 100% with descriptions. Description adds value: explains `since` accepts ISO date or relative shorthand ('7d', '3m'), provides usage examples, clarifies type only supports 'company'.

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

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

The description clearly states the tool provides a change feed for a company, including SEC filings, news, and patents. It gives example queries like 'what's new with X' and distinguishes from sibling entity_profile.

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

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

Explicitly tells when to use this tool (queries about recent changes, news, updates) and when to use entity_profile instead ('when you want the static profile'). Provides example queries and relative time formats.

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

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

Adds significant behavioral context beyond annotations: persistence details (authenticated vs. anonymous, 24-hour TTL), scoping by identifier, and pairing with recall/forget.

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

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

Concise, front-loaded with the primary purpose, and every sentence adds value. No unnecessary text.

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 2-parameter tool with no output schema, the description covers purpose, usage, behavior, and inter-tool relationships 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% with clear descriptions for key and value. Description references key-value pairs but doesn't add information beyond the schema; baseline 3 applies.

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

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

The description clearly states the tool saves data for reuse across conversations/sessions, specifies the resource (key-value pair), and distinguishes from sibling tools like recall and forget.

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

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

Provides explicit when-to-use guidance (discovering something worth carrying forward) and mentions related tools, but lacks explicit when-not-to-use or alternatives beyond 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, openWorldHint, idempotentHint, and destructiveHint as true/false. The description adds that each call cascades through several lookup endpoints internally, replacing 2-3 manual lookups, and mentions auto-disambiguation for company. This provides useful behavioral context 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 well-structured and front-loaded with examples and usage guidance. It is concise but packs substantial detail. Minor redundancy like 'auto-disambiguated' could be trimmed, but overall it 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 no output schema, the description fully explains the return values for both entity types. It also describes the internal cascading behavior and provides usage context. All necessary information for an AI agent to correctly invoke the tool is present.

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%. The description adds significant meaning beyond the schema: for each type, it specifies the exact return values (e.g., company returns ticker, CIK, company_name, and citation URI; drug returns RxCUI, ingredient, brand, and citation). This is critical since no output schema exists.

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 concrete examples ('What's the ticker for…'), clearly states the verb 'resolve', and specifies the resource: entity name to canonical/official identifier. It explicitly says to use it 'FIRST whenever you have a name but need an ID,' differentiating it from sibling tools that presumably use IDs.

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 directive: 'Use FIRST whenever you have a name but need an ID.' Includes example queries and supported types, giving clear context. Though it doesn't list when not to use, the guidance is strong enough to prevent misuse.

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 it as read-only, safe, and idempotent. The description adds that it internally probes with ai_visibility_check and returns a ranked list with specific metrics, providing behavioral context 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 two concise sentences plus a helpful example, with all essential information front-loaded. Every sentence adds value without redundancy.

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

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

Given no output schema, the description sufficiently describes the return value (ranked list with score, confidence, signal density). It also explains relationship to ai_visibility_check. Minor missing detail on potential limitations (e.g., rate limits) but overall complete for its 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 meaning: entities first entry is the subject, context disambiguates names, models and apiKey are optional. This provides extra guidance on how to use parameters effectively.

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

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

The description clearly states it compares AI visibility across multiple entities, using ai_visibility_check, ranks results, and identifies most/least recognized. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (general).

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 concrete use case ('competitive AI-marketing audits') and an example question, making it clear when to use. It does not explicitly state when not to use or list alternatives, but the context implies the sibling ai_visibility_check for single 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?

Annotations already provide safety and idempotency hints. The description adds behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. 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 and front-loaded, but slightly long. Every sentence earns its place, though minor 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 (composite call, two external APIs, no output schema), the description is complete. It details return values (summary, advisories, links, alternatives), notes ecosystem limitation, and explains behavior on partial failures.

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 coveres both parameters fully. Description adds value: scoped packages accepted, version defaults to latest. This goes beyond what the schema 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 it is a composite check for adding an npm package, combining deps.dev and bundlephobia. It uses a specific verb-resource combination and distinguishes from siblings like entity_profile or search_within.

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

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

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Also notes NPM ecosystem only and alternatives for other ecosystems.

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 (readOnly, idempotent, etc.), the description discloses the embedding model (BGE-base-en), chunking (500-char overlapping windows), character cap (200K with truncation and flag), and that each passage carries an offset for verification. 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?

Approximately 4 well-structured sentences. The description is front-loaded with the core action and use case, then details behavior and pairing. Every sentence adds value, with no redundancy or fluff.

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

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

Despite lacking an output schema, the description fully explains return values (passages with offsets and scores), behavior (top-N, truncation), and how it integrates with the workflow (fetch then search). It covers all essential aspects for an agent to use the 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% with clear parameter descriptions. The description adds context: text is the 'fetched record', query examples, and limit as 'max passages'. It enhances understanding without repeating schema, earning a score above baseline.

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

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

The description clearly states the tool performs semantic search inside a fetched record, with specific examples (SEC 10-K body, article) and outputs (top-N passages, offsets, scores). It distinguishes itself from complementary tools like ask_pipeworx_grounded, making its purpose unmistakable.

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

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

Explicitly says 'Use when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded. It provides context for use but doesn't explicitly exclude other scenarios or mention alternatives like simple search tools. The guidance is strong but not exhaustive.

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 behavioral traits beyond annotations: requires OAuth, lists supported types with parameter examples, details delivery channels (always-on feed, optional email/SMS/webhook with HMAC signing and auto-disable after 10 failures). Annotations only provide hints; description adds rich 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?

Description is well-structured with front-loaded purpose, followed by requirements, types, and delivery details. Slightly verbose but each sentence adds value. Could trim minor redundancies.

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 complexity (3 parameters, nested objects, multiple types), description fully covers prerequisites, return value (subscription id), type-specific options, delivery channels, and limitations. No output schema, but description covers what is returned. Complete for tool selection.

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

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

Schema coverage is 100%, so baseline is 3. Description adds significant meaning: explains each type's parameters with examples and clarifies delivery channel requirements (phone verification, cap). Exceeds baseline.

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

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

Description clearly states 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the verb 'Create' and resource 'subscription', and distinguishes from siblings like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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 prerequisite: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' Also details when to use each delivery channel and mentions phone verification for SMS. Lacks explicit guidance on when not to use this tool versus alternatives like 'recent_alerts'.

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, open-world, idempotent, non-destructive. Description adds context: returns example questions drawn from live tool catalog, call with no arguments for full spread. Adds value 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?

Single paragraph with front-loaded purpose and clear structure. Could be slightly tighter but no wasted words. 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?

No output schema but description fully explains what is returned (category-bucketed example questions with tool shapes). For its role as simple onboarding, it is 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 one optional parameter with description. Description adds meaning by listing possible topics and explaining 'pass topic to focus'. Schema coverage is 100%, baseline 3, description enriches it.

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

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

Clearly states the tool is the onboarding entry point for a new agent to discover Pipeworx's capabilities. Lists example questions and distinguishes from siblings like ask_pipeworx and 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 states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to use it for general or focused browsing. Provides clear guidance on when to use and not 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?

The description adds significant behavioral details beyond the annotations: it explains that the subscription is deactivated (not deleted) and that historical events remain available via recent_alerts. This is crucial context that the annotations (readOnlyHint=false, destructiveHint=false) alone do not 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 extremely concise with two sentences, front-loading the action and then adding key behavioral details. Every sentence adds value without redundancy.

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

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

Given the simplicity of the tool (one parameter, no output schema) and the existing annotations, the description adequately covers the essential behavior (deactivation, ownership). It lacks details on return values or error handling, but these are not critical for an agent to use 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?

The input schema has 100% coverage for the single parameter 'id', which is well-described as 'Subscription id (uuid) returned by subscribe.' The description does not add further semantic detail 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 'Cancel a subscription by id', specifying the action (cancel), resource (subscription), and identifier (id). This strongly distinguishes it from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

The description provides clear context by stating 'Ownership is enforced — you can only cancel your own subscriptions', which guides the agent on when to use this tool. However, it does not explicitly mention when not to use it or suggest 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 discloses that it returns a verdict, extracted structured form, actual value with pipeworx:// citation, and percent delta, adding behavioral context beyond the annotations. The annotations already declare readOnlyHint, idempotentHint, and no destructiveness, so the description complements them well 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 reasonably concise given the complexity, with natural-language triggers front-loaded. It uses bullet points in the explanation to structure the return information. While it could be slightly shorter, 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 the tool has only one parameter, no output schema, and annotations are present, the description fully covers the tool's purpose, domain, return values, and usage context. It is complete and leaves no significant 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?

The single 'claim' parameter is fully described in the schema with 100% coverage. The description adds extra semantic context by specifying the type of claims supported (company-financial) and the natural-language format, which helps the agent understand the expected input beyond the schema's generic description.

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

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

The description clearly states that the tool performs natural-language claim verification against authoritative sources, with specific examples like 'Is it true that…' and 'fact check'. It differentiates itself from siblings by noting it replaces multiple sequential calls. The verb 'validate' along with the title 'Validate Claim' directly indicates the action on the resource.

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

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

The description explicitly instructs 'Use whenever the agent needs to check whether something a user said is factually correct.' It also narrows the scope to company-financial claims via SEC EDGAR + XBRL. While it doesn't explicitly state when not to use, the domain limitation is clear enough for the agent to decide.

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