Vimeo

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds value by disclosing the default model (free), BYO key for Anthropic, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also hints at cost implications for Anthropic calls. 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 two sentences long, front-loading the main purpose and return value. Every sentence provides essential information without redundancy. It is concise yet informative, earning its place with zero wasted words.

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

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

With 4 parameters (1 required), 100% schema coverage, and no output schema, the description covers key aspects: return format, model options, optional context, and use cases. It lacks details on error handling or timeouts but is sufficient for a moderate-complexity tool. The mention of return structure compensates for missing output schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context beyond the schema: it explains the entity scope ('business / brand / product / topic'), provides examples ('Pipeworx', 'OpenInvoice'), clarifies the default model behavior for the 'models' parameter, and explains the role of '_apiKey' and 'context'. This enriches parameter understanding.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and returns a visibility score (0-100). It specifies the verb (probe, score), resource (LLMs, business/brand/product/topic), and outcome (score per model). This distinguishes it from siblings like 'deep_research' or 'scan_competitor_ai_presence' by focusing on LLM knowledge scoring.

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

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

The description provides explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to provide the optional _apiKey and models. However, it does not explicitly state when not to use this tool or compare it to alternatives, leaving some gaps for an agent to infer.

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 (read-only, open-world, idempotent), the description discloses that the tool routes to other tools, fills arguments, returns citations, works on every tier, and handles news. It also clarifies that it's fast and the default starting point, adding meaningful behavioral context.

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

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

The description is long but tightly packed with actionable information: purpose, usage scenarios, examples, and alternatives. It is front-loaded with 'PREFER OVER WEB SEARCH' and 'START HERE', and every sentence contributes value.

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

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

For a complex routing tool with no output schema, the description covers functionality, use cases, and distinctions from related tools. It also addresses performance and compatibility, making it comprehensive enough for an agent to select and invoke correctly.

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

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

The input schema already documents all six parameters as aliases for the question, with 100% coverage. The description adds no parameter-level detail beyond restating that natural language queries are accepted, so baseline 3 is appropriate.

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

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

The description clearly identifies the tool's function: routing user questions to 5,529 tools across verified sources and returning structured answers with citation URIs. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by positioning itself as the default entry point.

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

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

The description explicitly states when to prefer this tool over web search and lists specific domains (SEC filings, FDA data, etc.). It names alternatives (ask_pipeworx_grounded, deep_research) and specifies when to step up, providing clear usage boundaries.

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 (read-only, idempotent), the description discloses the tool's beta nature, current state (no active candidate, matches ask_pipeworx exactly), and the fact that it is a full working router with no fallback. This adds significant context about the tool's behavior and potential variability, which annotations do not convey. 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 slightly verbose but each sentence contributes meaningful information: definition, current status, usage guidance, and a clarification that it is fully functional. The content is logically ordered and front-loaded with the core purpose. It could be trimmed slightly, but it earns its length.

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

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

The tool is a complex router with no output schema, but the description explicitly ties it to ask_pipeworx's response shape and provides enough context about its experimental nature. It covers what, when, and how it behaves. Missing details like specific response format are mitigated by the explicit reference to the sibling's identical shape.

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

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

The input schema already covers 100% of parameters with detailed descriptions, including aliases for the 'question' parameter. The description only says 'same arguments' as ask_pipeworx, which adds no new parameter-level meaning beyond what the schema provides. Hence, 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 identifies this as a beta version of ask_pipeworx, a universal router with 5,529 tools, and distinguishes it from the stable version by emphasizing it runs candidate routing improvements. It explicitly states what the tool does (routes questions to tools) and how it differs from its sibling ask_pipeworx.

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

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

Provides explicit when-to-use guidance: 'Use it exactly like ask_pipeworx when you want the newest routing.' It names the alternative (ask_pipeworx) and clarifies the experimental context, including that results are compared to the stable router. This is more than enough for an agent to choose between the beta and stable versions.

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 readOnly/idempotent annotations, the description discloses detailed behavior: extraction is limited to tool results, returns a structured object with evidence and refusal reasons, and includes specific refusal reasons like 'not_in_source' and 'data_truncated'. It also explains the extra LLM call cost. 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 but efficiently structured: it front-loads the core purpose, then explains the mechanism, return behavior, use cases, and cost trade-off. While it is a single long sentence, every clause contributes meaningful information, making it concise relative to its 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?

The description is remarkably complete for a tool with no output schema: it explicitly specifies the success return format, refusal reasons, and the extra call cost. It also covers routing behavior and appropriate use cases. No significant gaps remain for an agent to invoke this tool correctly.

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

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

Schema coverage is 100% and all parameters are aliases for the natural-language question. The description adds context that the question is used for routing to tools and filling arguments, but does not provide additional parameter-level details beyond what the schema already states. 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 identifies the tool as a 'hallucination-resistant answer mode for high-stakes reads' with a specific verb-like purpose ('extracts the answer using ONLY what the tool result contains'). It distinguishes itself from the sibling ask_pipeworx by explicitly noting the same routing but grounded extraction, making its unique value unambiguous.

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

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

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and lists high-stakes scenarios. Also gives a clear alternative: 'prefer ask_pipeworx for casual lookups' because of the extra LLM call cost. This fully addresses usage context and trade-offs.

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

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

The description goes far beyond annotations by detailing the resolver contract (confidence levels, alternatives, suggestions), parent event extractor, news fallback behavior, safety short-circuits, closed market handling, wide-spread warnings, and cancellation rules. No contradictions with annotations.

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

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

The description is long but well-structured, front-loading the main purpose then elaborating. Every section adds necessary detail for a complex tool; however, it could be slightly trimmed without losing clarity.

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

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

Given the tool's complexity (3 params, no output schema), the description covers all critical aspects: input types, resolver contract, parent events, news handling, safety, and cancellation rules. It is complete and leaves no major gaps for an agent to misuse.

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 parameters are well-documented. The description adds value by explaining depth options (quick vs thorough), include_raw default and recommendation, and input resolution (slug, URL, question text), enhancing understanding 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 it researches a Polymarket bet by pulling Pipeworx data, specifies inputs (slug, URL, question text), and outputs (evidence packet, market-vs-model comparison). It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on single-bet research.

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

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

Explicit use cases are provided: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. However, it does not explicitly state when not to use or directly compare to alternatives, though the context implies it.

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

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

Annotations already declare the tool safe, idempotent, and non-destructive. The description adds value by specifying it returns top-level categories only and that slugs are usable downstream.

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 concise sentence that clearly conveys the tool's purpose and provides relevant examples. No extraneous words.

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

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

Given the tool has zero parameters and an output schema exists, the description sufficiently explains the scope (top-level categories) and use of slugs. No additional context is needed.

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

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

The tool has zero parameters, so the description is not required to explain them. The baseline expectation of 4 is met.

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 returns the full list of top-level Vimeo content categories, provides examples (e.g. Animation, Comedy, Music), and notes the slugs' usability in the sibling tool 'category_videos'. This distinguishes it from category and category_videos.

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 is for retrieving all top-level categories, but does not explicitly state when to use it vs alternatives like 'category' or 'category_videos'. However, the context is clear enough given the simplicity of the 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 read-only, non-destructive, and idempotent behavior. The description adds value by detailing the return fields (name, icon, subcategories, featured video count), which is not in annotations. No contradictions.

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

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

Single sentence with no wasted words. Information is front-loaded and efficiently communicated.

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

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

For a simple fetch tool with one parameter and an output schema, the description covers the main aspects: input (slug), output fields (name, icon, subcategories, featured video count). Complete and sufficient.

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

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

Schema description coverage is 0%, so the description carries the burden. It explains the 'slug' parameter is for identifying the category but does not provide additional constraints or format details. Adequate but minimal.

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 'Fetch metadata for a single Vimeo category by slug', specifying the verb, resource, and input method. It distinguishes from sibling tools like 'categories' (plural list) and 'category_videos' (videos in a category).

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

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

The description implies usage when you need metadata for one category, and the context of sibling tools (e.g., 'categories' for listing all) provides implicit guidance. However, it lacks explicit when-not-to-use or alternative tool instructions.

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 convey readOnly, openWorld, idempotent, and non-destructive hints. The description adds no behavioral context beyond the name, missing details on pagination, error handling, or what happens with invalid slugs.

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 phrase, which is too brief to be useful. It is under-specified rather than concise, missing opportunities to add value in few words.

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

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

Given the tool has 3 parameters, an output schema, and siblings, the description is insufficient. It does not cover pagination, filtering, or return format, leaving the agent without enough 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 schema has 3 parameters (slug, page, per_page) with 0% description coverage. The description does not explain any parameter, even though slug is required. The agent must infer semantics from property names only.

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 'Videos in a category.' indicates the tool returns videos for a given category. However, it is vague and does not differentiate from sibling tools like 'category', 'channel_videos', or 'videos_search' that also return videos.

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?

There is no guidance on when to use this tool over alternatives, nor any exclusions or prerequisites. The agent receives no context for decision-making.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by enumerating return fields (name, description, subscriber count, video count, privacy settings), which annotate the response beyond schema defaults.

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

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

The description is a single sentence that front-loads the action and resource, then lists return types. Every word is informative, no redundancy.

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

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

For a simple fetch tool with annotations and an output schema (not shown), the description covers the core return fields. It lacks error handling or rate-limit info, but these are partially covered by annotations. The description is sufficiently complete for a 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 description coverage is 0%, so the description must compensate. It clarifies that the parameter accepts a numeric ID or a slug, which is meaningful but not exhaustive (e.g., no format constraints or slug pattern hints). An example is provided but parameter semantics remain minimal.

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 ('Fetch') and resource ('metadata for a Vimeo channel'), and clarifies input type ('numeric ID or slug'). It lists return fields (name, description, subscriber count, video count, privacy settings), distinguishing it from siblings like 'channel_videos' which focus on video lists.

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

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

The description implies usage for fetching channel metadata but does not explicitly state when to use this tool versus alternatives such as 'channel_videos' for video content or 'category' for hierarchical browsing. No when-not-to-use or prerequisite guidance is given.

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, which cover safety. The description adds 'returns video metadata array', which is useful but not critical. 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?

A single, well-structured sentence that front-loads the core action and resource, then adds key features. Every part earns its place with no redundancy.

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

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

Despite having an output schema, the description doesn't clarify pagination defaults or sorting semantics. For a tool with 5 parameters and zero schema descriptions, more detail would be helpful. However, the task is straightforward and the output schema mitigates some gaps.

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

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

Schema has 5 parameters with 0% coverage in descriptions. The tool description only mentions 'supports pagination and sorting', giving no details on valid values for 'sort' (e.g., date, alphabetical) or 'direction' (asc/desc). An agent would need to infer or guess parameter behavior.

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

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

The description clearly states the action (list), resource (videos in a Vimeo channel), identification method (channel ID or slug), and features (pagination, sorting, returns metadata array). It effectively distinguishes from sibling tools like 'channel' or 'video'.

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 listing videos within a specific channel, but does not explicitly contrast with alternatives like 'videos_search' or state when not to use. However, the context is clear enough for an 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 indicate safe, read-only, idempotent behavior. The description adds rich behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal year handling, sorting by primary metric, and 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?

Description is efficiently front-loaded with example triggers. Every sentence adds distinct value without redundancy. Well-structured and immediately informative.

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 (2 params, no output schema), the description fully covers input semantics, data sources, behavior, and output format (paired data + URIs). No gaps remain for effective agent use.

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

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

Schema already describes both parameters with enums and ranges (100% coverage). Description adds value by specifying expected formats (tickers/CIKs for company, names for drug) and clarifying the behavior of the 'type' parameter.

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

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

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs, with natural language examples. It specifies what data each type returns and differentiates from sequential single-pack lookups.

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

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

Explicitly instructs to prefer this over sequential lookups when comparing entities. Provides clear criteria for choosing 'company' vs 'drug' and what data each provides.

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?

Despite strong annotations (readOnlyHint, idempotentHint, destructiveHint false), the description adds extensive behavioral context: account/paid-plan requirements, parallel facet routing, gaps[]/'never invented', hop fields and citation_uri fetchability, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. No annotation 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?

Every sentence carries useful information with almost no fluff, but the text is a single dense paragraph and starts with account requirements rather than the core purpose, hurting scannability. Still, given the tool's complexity, the length is justified.

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

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

With no output schema, the description fully explains return structure: findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], hop field, and citation_uri. It also covers auth, cost, latency, and failure modes, leaving little ambiguity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mostly reiterates depth behavior already present in the schema; it adds nuance (e.g., thorough paid plan, standard re-angles gaps) but does not materially redefine or enrich parameter meaning beyond the schema.

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

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

The description names a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1455 STRUCTURED data sources' in one call. It also distinguishes itself from ask_pipeworx and explicitly says 'this is NOT open-web search', making its unique role clear.

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

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

It gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx'; for breaking/current news 'prefer ask_pipeworx'), and an alternative if not signed in. This is exemplary usage guidance.

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

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

Annotations already declare read-only and non-destructive. Description adds useful info about returning full schemas and curated examples, making the tool's behavior clear.

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 paragraph, front-loaded with action, then examples. No fluff, though could be slightly shorter.

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 details what is returned (tools with schemas and examples). Sufficient for the tool's purpose.

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

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

Schema describes all parameters with aliases and examples. Description reinforces that query is natural language and provides topic 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?

Clearly states it finds tools by describing data/task, and lists many example topics. However, does not explicitly differentiate from sibling search tools like '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?

Explicitly says 'Call this FIRST' when many tools are available, guiding when to use it versus alternatives. Provides numerous example queries.

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 significant behavioral context beyond these: it explains the parallel fan-out across multiple external services, the soft-fail status of USPTO PatentsView API sunset, the fallback from GDELT to GNews, and the specific returned fields (cik, filings with URIs, fundamentals sorted by period_end descending, etc.). 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 somewhat lengthy (over 100 words) but packs substantial information into a dense paragraph. It front-loads with example queries and uses all-caps for key guidance ('ALWAYS PREFER'), which aids scanning. However, it could be more structured (e.g., bullet points for the returned data) to improve readability 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 the tool's complexity (aggregating data from multiple external sources) and the absence of an output schema, the description is remarkably complete. It enumerates every returned data point (cik, company_name, recent_filings with URI format, fundamentals with specific fields and sort order, patent sunset status, news fallback chain, LEI). It also explains the parallel execution optimization, making the information sufficient for an agent to understand what to expect.

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

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

Schema coverage is 100% with both 'type' and 'value' having descriptions. The description adds meaning beyond the schema by specifying that 'type' currently only supports 'company', and that 'value' must be a ticker (e.g., 'AAPL') or zero-padded CIK (e.g., '0000320193'), explicitly disallowing names and directing to resolve_entity for that case. This is valuable usage 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 defines the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It provides numerous example queries and explicitly distinguishes from sibling tools by stating it should be preferred over chaining single-source lookups for holistic views. The verb 'profile' combined with specific resource mentions (SEC, XBRL, USPTO, news, GLEIF) and the cross-source scope makes the purpose unambiguous.

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

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

The description provides explicit guidance on when to use this tool: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clearly states when not to use it: 'names not supported (use resolve_entity first if you only have a name).' This gives the agent clear decision criteria and alternatives.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false. The description adds context about clearing 'sensitive data' and the idempotent nature is implied. No contradictions, and behavioral context is sufficient 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 concise sentences: first defines the action, second provides usage guidance. No superfluous words, efficient and clear.

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 (1 param, no output schema), the description covers core functionality, usage context, and sibling relationships. Missing details on return values or errors, but adequate for a straightforward deletion 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 the parameter 'key' is described in schema as 'Memory key to delete'. The tool description does not add additional parameter semantics beyond what schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete a previously stored memory by key') and identifies the specific resource (memories). It distinguishes itself from siblings by mentioning 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?

Explicit usage conditions given: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with siblings, providing clear guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds that it fetches the page, extracts content, and emits markdown, but this is expected behavior. No contradictions.

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

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

The description is two sentences: purpose first, then use cases. Every sentence is informative and concise, with no wasted words.

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

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

Given no output schema, the description explains the output format (standard llms.txt markdown) and deployment location. Parameters are fully covered, and annotations provide safety guarantees. Complete for a simple tool.

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

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

Input schema covers 100% of parameters with descriptions. The description mentions URL and max_links implicitly but does not add semantics beyond what the schema provides. 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 explicitly states the tool generates a production-ready llms.txt file for any URL, targeting AI crawlers. It clearly identifies the action, resource, and context, and is distinct from sibling tools which focus on other domains like betting or research.

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

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

The description lists specific use cases (getting a client's site indexed, drafting for own project, auditing competitors), providing clear guidance on when to use. However, it does not exclude scenarios or mention alternatives within the toolset.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds value by specifying return fields and default behavior (active subscriptions only), which is not in annotations.

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

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

Two sentences with no fluff. First sentence states purpose and output, second provides usage guidance. Front-loaded and efficient.

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

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

For a simple list tool with one optional parameter and no output schema, the description covers purpose, usage, and return fields adequately. Could mention pagination if applicable but not necessary.

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

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

Schema coverage is 100% for the single optional parameter 'include_inactive'. Description does not mention the parameter, so no added meaning beyond schema.

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

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

Description clearly states the tool lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It distinguishes from siblings like subscribe/unsubscribe by its listing action.

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

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

Explicitly tells when to use: 'review what you're monitoring before adding more or to find an id to cancel'. Provides clear context, though no explicit when-not-to-use or alternatives.

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

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

Even though annotations exist (readOnlyHint=false, etc.), the description adds valuable behavioral context: rate limits (5 per identifier per day), claim_token mechanics for tracking reports, and that it's free and doesn't count against quota. These details go beyond what annotations provide and clarify what the user can expect.

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

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

The description is long but every sentence earns its place, covering purpose, usage scenarios, exclusions, token mechanics, rate limits, and cost. It is front-loaded with the main action and structured to move from general guidance to specific examples. No fluff or 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?

The tool has no output schema, so the description carries the burden of explaining returns, which it does via the claim_token mechanism. It fully covers the tool's lifecycle: when to use, how to construct feedback, what the token means, and how to follow up. It also addresses common pitfalls like describing end-user prompts and using non-Pipeworx tools.

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

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

The schema already provides 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds extra semantics for claim_token specifically ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})'), which is more than the schema provides. It also clarifies the message content guidance ('don't paste the end-user's prompt'), adding value beyond the schema.

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

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

The opening sentence clearly states the tool's function: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' This is a specific verb-resource pairing that distinguishes it from sibling tools like ask_pipeworx, which is for asking questions rather than submitting feedback.

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

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

The description provides explicit usage guidelines: 'Use when a tool returns wrong/stale data (bug)... feature/data_gap... praise' and specifies when not to use it ('if the tool came from a different MCP server... file it with that server instead'). It also gives a method for disambiguating Pipeworx tools by name.

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

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

Annotations already declare read-only, idempotent, open-world. Description adds valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h). 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?

Four sentences, no wasted words. Front-loaded with purpose, then use cases, then technical details. Efficient and scannable.

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

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

For a simple tool (1 optional param, no output schema), the description covers purpose, use cases, data source, privacy, and caching. No gaps for agent decision-making.

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

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

Schema covers parameter fully (100%). Description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' Elevates beyond schema.

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

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

Clearly states it returns top tools, packs, and call volume over a window. Distinct from siblings like 'discover_tools' or 'ask_pipeworx'.

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

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

Lists three specific use cases (discovering hot data, confirming canonical choice, alignment check). Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

Description goes far beyond annotations (which already indicate readOnly, idempotent, non-destructive). Adds detailed behavioral traits: Jaccard similarity filtering, partition placeholder filter, fill check against CLOB depth, and response structure. No contradiction with annotations.

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

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

Description is long (over 250 words) and somewhat dense, but structured with sections for modes, semantic anchor, partition filter, and fill check. Could be more concise without losing key details.

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

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

Despite no output schema, the description thoroughly explains the tool's logic, all filters, fallback conditions, and what to do when arbitrage is not realizable. Covers both operational and edge-case behavior.

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

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

Schema coverage is 100% with parameter descriptions. Description adds meaningful context: examples of event slugs and topic queries, explains mode behavior differences, and provides usage recommendations. Value added beyond schema.

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

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

Description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) and differentiates from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

Provides explicit guidance on when to use each mode (e.g., 'event (recommended for a specific market)', 'topic (for cross-event scanning)'). Includes examples and mentions when not to trade (realizable_edge_pp ≤ 0). Lacks explicit when-not-to-use scenarios but is otherwise strong.

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

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

The description explains model families, response segments, diagnostics, caching, and tradeable-edge knobs in detail. It does not contradict annotations (readOnlyHint, etc.) and adds significant behavioral context beyond them.

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

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

The description is very long and detailed, including low-level technical specifics. While comprehensive, it lacks conciseness and could be better structured for quick agent scanning.

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 covers all essential behaviors, segments, diagnostics, and edge cases (e.g., Fed note, caching). It is thorough and self-contained.

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. The description adds general context about some parameters (e.g., tradeable-edge knobs) but does not systematically enhance each parameter's meaning beyond the schema.

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

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

The description clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It distinguishes from siblings like polymarket_arbitrage and polymarket_edge_tracker by specifying its purpose and output structure.

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 context ('Built for 'what should I bet on today'') but lacks explicit when-not-to-use guidance or alternative tools. It mentions Fed bets surface separately but doesn't contrast with sibling tools.

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

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

The description extensively documents behavior beyond annotations (readOnly, idempotent, not destructive), including data sources (daily snapshots), response structure (tracked, expired, snapshot_dates), limits (60-day TTL, data gaps), and interpretation of decay numbers.

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

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

The description is long but well-structured with a clear purpose at the start, followed by precise details on response and limits. Every sentence adds value, though slight wordiness could be trimmed.

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

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

Given two parameters and no output schema, the description fully explains the response format (tracked, expired, snapshot_dates arrays), field meanings, and data limitations, making it sufficient for correct tool usage.

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

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

The description adds default values (14 days, '1wk') and constraints (clamp 2-30) but the input schema already includes these details, so minimal additional value beyond schema coverage.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' and answers when an edge has existed and if it's shrinking, distinguishing it from sibling tools like polymarket_edges which likely give current edges.

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

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

The description gives clear context on when to use (e.g., 'a fresh wide edge and a 3-week-old wide edge are different trades') but does not explicitly list alternatives or 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds behavioral context: walks the ladder, returns verdict, warns about dominant loss mode. No contradiction; adds significant 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?

Description is lengthy but well-organized with clear sections for modes and usage. Every sentence adds value. Slightly verbose but still effective 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?

No output schema, yet description fully explains return fields for both modes (top_of_book, vwap_fill_price, verdict, etc.). Covers both single-market and basket, including warnings about unhedged positions. Complete given 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 description adds meaning: explains default side for basket (auto), clarifies size_usd interpretation per mode, and states requirement for market or event (mutually exclusive). Provides context beyond schema descriptions.

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

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

Clearly states it performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. Differentiates between single-market and basket modes, each with specific outputs. Distinguishes from siblings by directing usage before acting on polymarket_arbitrage or polymarket_edges signals.

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

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

Explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Explains when and why: theoretical overround on thin books is not capturable, and partial fills create directional risk. Provides clear conditions for 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?

Beyond annotations (readOnlyHint, etc.), the description extensively discloses behavior: compatibility_warning conditions, temporal alignment checks, skipped leg causes, and the expectation that pre-mapped topics often yield warnings. This exceeds annotation-based transparency.

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-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence contributes useful information. Minor verbosity could be trimmed but overall efficient for the complexity.

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

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

No output schema is provided, but the description covers response structure (leg prices, spread, safety fields) and edge cases (misaligned events, incompatible bet shapes). This fully compensates for the lack of an output schema, making the tool self-contained.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by explaining the two modes and how they interact (e.g., explicit overrides topic). With full schema coverage, the baseline is 3; the added context justifies a 4.

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

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

The description clearly states the purpose: cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison, not single-venue arbitrage.

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

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

The description explains two modes (topic shortcuts and explicit pairing) and warns that most pre-mapped topics return compatibility warnings, guiding when to expect meaningful spreads. It does not explicitly compare with sibling tools but provides adequate 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?

Adds scoping and pairing info beyond annotations (readOnlyHint, idempotentHint, destructiveHint) – no contradictions.

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

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

Three concise, front-loaded sentences with no waste; structure is clear and efficient.

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

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

Tool has one optional param and no output schema; description fully explains usage, behavior, and scoping.

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 the parameter fully; description reinforces the optional nature and behavior of omitting key, adding value.

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

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

Clearly states the tool retrieves a saved value or lists keys, distinguishing it from siblings remember (save) and forget (delete).

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 describes when to use (look up stored context) and provides examples; lacks an explicit when-not-to-use statement but is clear overall.

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 annotation declares readOnlyHint=true, but the description explicitly states that setting mark_read:true will flag events as read, which is a state mutation. This contradicts the readOnlyHint and undermines the accuracy of the tool definition. The description is otherwise transparent about the mark_read behavior, but the contradiction is critical.

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 three sentences, front-loaded with the primary purpose, and every sentence provides valuable information. No unnecessary words or 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 read-only list tool with 5 parameters fully documented in the schema, the description covers all key aspects: what is returned, filtering options, mark_read side effect, and alternative access method. It is complete for an agent to understand and invoke correctly.

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

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

Schema coverage is 100%, and the description adds concrete usage examples (e.g., filter by type 'sec_8k') and explains the behavioral effect of mark_read. It does not mention the limit parameter, but the schema already covers it. Overall, it adds meaningful 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 'Pull fired events from your subscription feed' specifying the verb and resource. It details the returned data and filtering options, distinguishing it from sibling tools like 'list_subscriptions' or 'recall' by focusing on alert events from a feed.

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

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

The description provides clear usage context: it mentions polling is fine, gives an alternative HTTP endpoint, and explains how to use mark_read to manage read state. However, it does not explicitly state when not to use this tool or name alternatives among siblings, though the context is sufficient for typical 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?

Adds significant behavioral details beyond annotations: describes fallback logic (GDELT→GNews), USPTO soft-fail, return structure (changes[] grouped by source, total_changes, 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 a dense paragraph that front-loads the purpose but could be more structured (e.g., bullet points). Every sentence adds value, but length could be reduced slightly.

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

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

Despite no output schema, the description fully explains the return format and error handling (fallbacks, soft-fail). Covers all major aspects given the tool's complexity.

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

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

Schema coverage is 100% and description adds value by explaining each parameter with examples (relative date shorthand, ticker vs CIK, typical monitoring value).

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

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

The description clearly states it returns a change feed for a company in a time window, listing specific sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from sister tool entity_profile, which is for static profiles.

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

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

Provides explicit context on when to use this tool (for dynamic changes) and names the alternative entity_profile for static profiles. Lacks explicit 'when not to use' cases beyond that.

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 idempotentHint=true, destructiveHint=false. The description adds meaningful context: memory is key-value and scoped by identifier, persistence differs by authentication (session vs 24 hours). 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 concise (4 sentences) and front-loaded with the core purpose. Every sentence contributes without redundancy or fluff.

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

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

Given the tool's simplicity (2 params, no output schema), the description sufficiently covers purpose, usage, behavior, and sibling relationships. It lacks mention of error cases or limits, but these are not critical for basic understanding.

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

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

Schema description coverage is 100% with descriptions for both 'key' and 'value'. The description adds extra context (scope by identifier, example key names) beyond the schema, providing additional clarity.

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: 'Save data the agent will need to reuse later.' It specifies the verb (save), resource (data/memory), and distinguishes itself from sibling tools 'recall' and 'forget' by explicitly mentioning their 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?

The description advises when to use the tool ('when you discover something worth carrying forward') and why ('so you don't have to look it up again'). It pairs with 'recall' and 'forget', implying usage context. However, it does not explicitly state when not to use it 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?

Beyond annotations (read-only, idempotent), the description adds rich behavioral detail: cross-source enrichment via SEC EDGAR, GLEIF, OpenFIGI; explicit labeling of sources; stating unresolved identifiers under 'unresolved'; graceful degradation if GLEIF/OpenFIGI are unavailable; cascading through multiple endpoints. This fully informs the agent of expected behavior and failure modes.

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

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

The description is lengthy but each sentence adds value: it begins with relatable example queries, states the core purpose, then details supported types and behavior. It is front-loaded with the main directive. The verbosity is justified by the tool's complexity, though it could be slightly tighter.

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

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

Given the tool's complexity (two entity types, multiple data sources, fallback behavior), the description is exceptionally complete. It covers inputs, outputs (though no output schema exists), internal cascading, degradation, and even notes the tool replaces 2-3 manual lookups. No important aspect is left unaddressed.

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 greatly enriches parameter meaning by explaining what each entity type returns (e.g., for company: CIK, ticker, company_name, LEI with ownership, FIGI; for drug: RxCUI, ingredient, brand, citation). It also clarifies accepted input formats for 'value' beyond schema examples, such as 'ticker (AAPL), CIK (0000320193), or name' and 'brand or generic name.'

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

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

The description clearly states the tool resolves names to canonical identifiers, with specific examples of user queries and explicit mention of entity types (company, drug). It distinguishes itself from siblings by emphasizing it provides identifiers other tools require as input, citing 'Use FIRST whenever you have a name but need an ID.'

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

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

Provides explicit usage guidance: 'Use FIRST whenever you have a name but need an ID' and illustrates with example queries. It does not explicitly state when not to use it (e.g., when you already have an ID), but the context makes the appropriate use case clear.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by explaining the process (probing each entity, ranking, surfacing most/least recognized) and the output structure (score, confidence, signal density). 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 concise (6 sentences), front-loaded with the primary action, and every sentence adds information. There is no redundancy or unnecessary detail.

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 adequately explains the output format (ranked list with score, confidence, signal density) and the process. The tool is moderately complex with 4 parameters; the description covers key aspects. Missing details like max entities or error handling would push it to 5.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds meaningful context: 'First entry treated as the subject for narrative' for entities, details on model support, and the purpose of the context parameter. This is above baseline but not exceptional.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, uses a specific verb ('compare'), and distinguishes itself from sibling tools like 'ai_visibility_check' which likely handles single entities. It also provides a concrete use case ('competitive AI-marketing audits').

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

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

The description explicitly mentions the tool is useful for competitive audits and implies it should be used when comparing multiple entities. While it does not explicitly state when not to use it, the contrast with sibling 'ai_visibility_check' provides context. A slightly higher score would require explicit exclusion 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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description discloses key behavioral traits: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s and may time out, and sources_failed lists any failed sources. This provides rich behavioral context beyond what annotations already 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 moderately concise at five sentences. Each sentence adds value, but the first sentence is quite long with parenthetical details. The most critical information (composite nature, use case) is front-loaded. Slightly more brevity could improve it, but it is not overly verbose.

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

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

For a composite tool with no output schema, the description fully covers purpose, usage guidelines, behavioral traits, parameters, output format (summary block with specific fields), and limitations (NPM only, timing). It also addresses edge cases like partial failures and scoped packages, making it complete.

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

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

Schema coverage is 100% (both parameters described). The description adds meaning by listing the return summary fields (is_latest, license, advisory_count, bundle sizes, etc.), explaining version defaults to latest, and clarifying scoped packages are accepted. This goes beyond the schema definitions.

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 identifies the tool as a composite check for npm packages, combining deps.dev and bundlephobia data. It specifies the exact resources checked (license, advisories, version history, bundle size, etc.) and distinguishes it from sibling tools by being a one-call composite solution. The verb 'scan' and resource 'dependency' are well-defined.

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

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

The description explicitly states when to use the tool: when an agent asks about safety, popularity, size, or cost of adding an npm package. It also provides clear limitations (NPM only in v1, other ecosystems use deps.dev:version directly), giving agents guidance on when not to use it and what alternatives exist.

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds algorithmic details (BGE-base-en, cosine, 500-char windows), a 200K char cap with truncation flag, and guarantees that offsets enable verification. This exceeds annotation info but does not fully describe return format (e.g., structure of passages).

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 well-organized paragraph: purpose, mechanics, use case, sibling relationship, technical details. Every sentence adds value with no redundancy. Very concise for the amount of information conveyed.

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

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

Covers all essential aspects: what, how, when, limitations (cap, truncation), verification (offsets), and relationship to sibling tool. Despite missing output schema, the description adequately describes return format. No gaps given 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% with good descriptions. The description provides minor extra context (e.g., examples for query, text as 'already pulled') but does not significantly enhance parameter understanding beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool performs semantic search inside a fetched record, specifying inputs (text + query) and outputs (passages with offsets and scores). It distinguishes from siblings like ask_pipeworx_grounded by positioning itself as a tool for searching within an already-fetched document.

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 when the record is too big to cram into the prompt.' Also provides an alternative workflow: pairing with ask_pipeworx_grounded for grounding. This helps the agent decide between sibling tools.

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

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

Adds extensive behavioral context beyond annotations: OAuth requirement, type-specific parameters, delivery channel details (SMS cap, webhook signing secret once, auto-disable after 10 failures). No contradiction with annotations.

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

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

Description is dense but relatively concise given the detail. Front-loaded with main purpose. Could be slightly better structured (e.g., bullet points), but each 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 complexity (3 params, nested objects, no output schema), description covers return value, requirements, all types, parameters, delivery options, and edge cases (SMS verification, webhook secret). 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%, and description adds rich meaning for each parameter, especially the type-specific 'params' object with detailed examples and codes (e.g., sec_8k items, polymarket_edge topic).

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 'Create a proactive monitoring subscription to a live-data event stream' with specific verb and resource, and distinguishes from siblings like 'unsubscribe' and 'list_subscriptions'. Also mentions returns subscription id.

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

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

Provides explicit requirements (OAuth account, no anonymous/BYO), lists supported types with examples, and describes delivery channels. Lacks explicit comparison to sibling tools, but 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description does not contradict these and adds behavioral context: it returns category-bucketed example questions with tool + argument shapes drawn from a live catalog. No hidden behaviors are implied.

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

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

The description is thorough but slightly verbose, starting with alternative user phrasings. While every sentence adds value, it could be tightened without losing meaning. Still, it is well-structured and front-loaded with key information.

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

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

Given no output schema, the description fully explains the return format (category-bucketed example questions with tool calls). It covers purpose, arguments, and usage context, leaving no important gaps 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% for the single optional parameter 'topic'. The description adds significant value by listing example values (finance, pharma, etc.) and explaining the effect of omitting the parameter (full spread). This goes well beyond the schema's brief description.

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

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

The description clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions. It explicitly distinguishes itself from sibling tools like ask_pipeworx and entity_profile by positioning itself as the first call to understand what Pipeworx can do.

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

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

The description provides explicit when-to-use guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains how to call with or without the topic argument, covering usage scenarios.

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 non-destructive behavior beyond annotations: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' Annotations only give destructiveHint=false, so description adds key context.

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

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

Two sentences, front-loaded with purpose, zero waste. Every sentence adds value: ownership and deactivation detail.

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 single-parameter tool, description is complete: covers ownership constraint, side effects (deactivation, not deletion), and historical data availability.

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

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

Schema coverage is 100% and description adds 'by id' which aligns with schema's 'Subscription id (uuid) returned by subscribe.' No additional parameter detail 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?

Clear verb+resource: 'Cancel a subscription by id.' Explicitly distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying cancellation action.

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

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

Provides explicit constraint: 'Ownership is enforced — you can only cancel your own subscriptions.' Guides appropriate use, though no mention of alternatives.

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

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

Annotations already declare readOnlyHint=true, etc. The description adds value by listing the returned fields (display name, bio, location, etc.), which complements the safety profile.

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

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

Single sentence front-loads the action and result, with no extraneous words.

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

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

Given the simple tool with one parameter and an output schema, the description covers purpose, input, and output fields; lacks error handling details but is adequate.

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

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

The description clarifies that the single parameter can be a user_id or vanity URL, adding meaning beyond the empty schema description, compensating for 0% coverage.

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

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

The description clearly states the verb 'Fetch', resource 'Vimeo user's public profile', and scope 'by user_id or vanity URL name', distinguishing it from sibling tools like user_videos.

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 context (by user_id or vanity URL) but lacks explicit guidance on when to use this tool vs alternatives such as user_videos.

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 convey read-only, non-destructive behavior. The description adds that pagination (page, per_page) and sorting options are supported, and that the return is a 'video metadata array'. This provides useful behavioral detail beyond the annotations, though additional info like rate limits or default sort order could enhance clarity.

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, front-loaded sentence of 16 words. Every element (list videos, user_id, pagination, sorting, return type) contributes directly to understanding the tool's function 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?

The description covers the core behavior (list by user, pagination, sorting, return type). Since an output schema exists, detailing exact return fields is unnecessary. However, it could be more complete by mentioning default sort order, maximum pagination values, or potential error conditions. Overall, it meets the needs for a straightforward read operation.

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

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

With 0% schema description coverage, the description must compensate. It mentions 'user_id' explicitly and implies 'page', 'per_page', 'sort', and 'direction' through 'supports pagination and sorting'. However, it does not explain their formats, defaults, or constraints, leaving some ambiguity.

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

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

The description clearly specifies the verb 'list', the resource 'videos uploaded by a Vimeo user', and includes the key identifier 'user_id'. It distinguishes from siblings like 'video' (singular) and 'videos_search' (search across users) by focusing on a specific user's uploads.

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 context (list videos for a specific user) but does not explicitly state when to use or not use this tool versus alternatives like 'videos_search' or 'channel_videos'. No exclusions or prerequisites are provided.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial context beyond these: it explains the two pipelines (SEC EDGAR + XBRL fast path vs grounded pipeline), the verdict enum, and crucially clarifies the semantics of could_not_verify vs unsupported, warning that could_not_verify must not be treated as evidence. This is rich behavioral disclosure with 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 longer than average but every sentence earns its place: trigger phrases, use case, routing logic, return values, and an important caller warning. It is front-loaded with the purpose and ends with a critical caveat. A minor deduction for density; it could be broken into a few shorter sentences, but there is no waste.

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 (two pipelines, nuanced verdicts) and lack of an output schema, the description covers all necessary aspects: what it does, when to use, how it routes, what it returns, and especially the critical distinction between could_not_verify and unsupported. It also notes the efficiency gain (replaces 4–6 sequential calls), which helps contextualize its role.

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 meaningful guidance beyond the schema: it explains tolerance_pct's default (implied by wording, capped at 5), provides a practical use case (set 1–2 for hallucination detection), and gives concrete claim examples for the claim parameter. This 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?

The description clearly states a specific verb+resource: natural-language claim verification against authoritative sources. It distinguishes itself with trigger phrases and a clear directive ('Use whenever the agent needs to check whether something a user said is factually correct'), and even details two distinct processing paths (SEC EDGAR for financial claims, grounded pipeline for anything else), which sets it apart from sibling tools like ask_pipeworx_grounded.

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 instructions: 'Use whenever the agent needs to check whether something a user said is factually correct' and includes a list of natural-language triggers. It also explains the routing behavior for financial vs other claims. However, it does not name alternatives or explicitly state when-not-to-use, so it falls just short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, destructiveHint. Description adds specifics on returned fields (title, duration, etc.), providing moderate additional context without contradicting annotations.

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

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

Single sentence that front-loads purpose and efficiently conveys key information with no wasted words.

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

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

Given single parameter, rich annotations, and presence of output schema, the description sufficiently covers the tool's behavior and return values, leaving no critical gaps.

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

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

Schema has 0% description coverage. Description mentions 'numeric or string ID' and provides an example, adding some meaning beyond schema, but does not fully explain constraints or format, so baseline is 3.

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

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

Description clearly states 'Fetch metadata for a single Vimeo video by numeric or string ID', specifying verb, resource, and method. It differentiates from siblings that deal with lists or other entities by focusing on a single video retrieval and listing 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?

The description implies usage for fetching a single video by ID, and siblings include search and list tools, but no explicit guidance on when not to use or alternatives is provided.

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

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable context by disclosing the authentication requirement ('authenticated with a Vimeo access token') and summarizing the response contents (title, duration, URLs, owner, play statistics), which goes beyond what annotations provide.

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

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

The description is compact and front-loaded, with the core action and resource in the first sentence. Each subsequent sentence adds distinct information (parameters/auth, return fields, use case), and there is no redundant or filler 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?

Given the 6-parameter schema, strong annotations, and an output schema, the description covers the essential aspects: purpose, auth, key parameter names, and returned data. It is slightly incomplete in explaining parameter value constraints, but overall it provides enough context for an agent to select and invoke the tool.

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

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

Schema description coverage is 0%, so the description must compensate for the 6 parameters. It names the parameters (page, per_page, sort, direction, filter) and clarifies that query is free-text, but it does not explain allowed values, semantics of sort/filter/direction, or effects of pagination parameters. This is only minimal compensation.

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 identifies the tool as a free-text search over the Vimeo video library via api.vimeo.com/videos, with a specific verb ('Search') and resource ('Vimeo video library'). It also distinguishes itself from sibling tools like video, user_videos, or category_videos by emphasizing a library-wide subject/title/creator search.

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

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

The description states the intended use case: 'Answers which videos hosted on Vimeo match a subject, title, or creator name.' This provides clear context for when to use the tool, though it does not explicitly mention alternative tools or exclusionary guidance, so it falls short of a 5.

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