Useragent

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. Description adds behavioral context: probing multiple LLMs, scoring mechanism, need for API key for Anthropic, and return format (per-model + combined). 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 sentences, front-loaded with the main action, no fluff. Every sentence adds essential information (action, default model, return format, use cases).

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

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

Given 4 parameters and no output schema, description adequately covers return structure (per-model data + combined view) and model options. Could mention rate limits or cost implications for Anthropic, but not critical.

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 100% parameter description coverage. Description adds value by explaining _apiKey's role (BYO key, direct cost to user) and context parameter's disambiguation purpose, enhancing beyond schema.

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

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

The description uses specific verb 'probe' and resource 'LLMs', clearly stating the tool scores visibility (0-100) per model. It differentiates from siblings like 'scan_competitor_ai_presence' by focusing on general brand/product visibility 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?

Provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains default vs. Anthropic model with BYO key. However, does not mention when not to use or compare 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context about routing to 5008 tools, returning stable citation URIs, and being a single fast call. No contradictions.

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

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

The description is long but well-structured, front-loading key guidance and providing examples. Minor verbosity but earns its length with valuable content.

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

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

Given the tool's complexity (5008 tools, many domains), the description is comprehensive: covers when to use, what it does, and provides ample examples. No output schema needed for this tool.

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

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

Schema coverage is 100% with clear aliases. Description adds examples and usage context beyond the schema, but the schema itself is already informative.

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 routes questions to structured data sources with citations, distinguishing it from web search and sibling tools like ask_pipeworx_grounded and deep_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?

Explicitly states 'PREFER OVER WEB SEARCH', provides specific use cases with examples, and explains when to use alternatives like ask_pipeworx_grounded or deep_research.

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

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: it is a beta with experimental routing, mentions source-preference ranking, and states it falls back to nothing (fully working). 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 detailed but not overly verbose. It front-loads key information (beta, identity) and then provides specifics and usage instruction. Every sentence adds value, though it could be slightly more concise.

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

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

Given the complexity of a beta router with many tools and the presence of annotations, the description is highly complete. It explains the difference from the stable router, the source-preference ranking, usage instructions, and that it is a full working router. No output schema exists, but the description references identical response shape, which is 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 coverage is 100% and all parameters (question and its aliases) are described in the schema. The description adds no additional parameter-specific meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly identifies the tool as a beta version of ask_pipeworx with identical functionality but experimental routing improvements, specifically source-preference ranking. It distinguishes itself from the stable sibling ask_pipeworx by noting it is the 'experimental edge'.

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 it: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also explains that results are compared to the stable router, providing usage context. However, it does not explicitly mention when not to use it or compare with other siblings like ask_pipeworx_grounded.

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 detailed behavioral traits beyond annotations: returns answer, evidence, confidence, source, fetched_at, and refusal reasons. Also mentions the extra cost of one LLM call versus the sibling tool. 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?

Description is front-loaded with key information but is somewhat lengthy. Every sentence serves a purpose, though it could be slightly trimmed without losing meaning. Overall well-structured and 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?

Despite no output schema, the description thoroughly details the return format and refusal scenarios. It provides all necessary context for understanding tool behavior and decision-making, making it fully 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% with all parameters as aliases for 'question'. Description adds value by explicitly listing the aliases (query, q, prompt, text, input) and stating that the question is in natural language, which clarifies usage beyond the schema.

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

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

Clearly states it's a hallucination-resistant answer mode for high-stakes reads. Distinguishes itself from sibling 'ask_pipeworx' by specifying it extracts answers only from tool results, preventing invention of facts.

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: for answers that will be quoted, cited, or acted on with high stakes. Also advises to prefer 'ask_pipeworx' for casual lookups, providing clear guidance on tool selection.

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

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

The description extensively discloses behavioral traits beyond annotations: fan-out logic, response shapes, resolver contract, parent extractor, news fallback handling, safety mechanisms (low-confidence suppression, closed markets, wide spreads), and resolution-rule risk. No contradiction with annotations.

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

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

The description is very long (multiple paragraphs in one block) and lacks clear structure. It front-loads the main purpose but then includes extensive details that could be better organized or summarized. Conciseness is low.

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 extremely complete: it covers all fan-out paths, response structures, edge cases (low-confidence, closed markets, wide spreads), and resolution-rule risk. With no output schema, the description fully explains return values. Given the tool's complexity, nothing is missing.

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 by explaining how 'depth' parameter works (quick vs thorough) and what 'include_raw' does (summarized vs full payloads). This adds value.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, and lists inputs (slug, URL, question text) and outputs (evidence packet, comparison). It is specific but could be more concise; however, it distinguishes from sibling tools by focusing on Polymarket 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 provides explicit usage examples ('should I bet on X', 'what does the data say about Y') and mentions when to use it. However, it does not explicitly state when not to use it or provide alternatives among 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?

Annotations declare safe read operations, and the description adds details: for companies, pulls latest 10-K data with fiscal year handling; for drugs, FAERS data. Explains result sorting 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 slightly verbose with multiple query examples, but they serve as useful patterns. Well-structured with front-loaded purpose. Could be more concise, but every sentence adds value.

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

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

No output schema provided, but description explains return format (paired data + citation URIs) and metrics included. Covers nuances like fiscal year handling. Adequately complete for a comparison tool.

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

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

Schema coverage is 100%, but description adds meaning beyond schema: explains that type='company' uses tickers/CIKs and pulls specific financial metrics, and type='drug' uses drug names. Adds value despite full 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 performs side-by-side comparison of 2–5 companies or drugs, listing example queries and specifying data sources. It distinguishes itself from siblings like entity_profile by focusing on multiple entities in one call.

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

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

Explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities. Provides natural language triggers and context for when to use it.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive; description adds extra context: not open-web, returns empty gaps for missing topics, performance expectations, semantic excerpting, and contradictions for certain depths.

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

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

Description is long but every sentence adds value, front-loaded with critical info (account requirement, alternative). Slightly exceeds conciseness ideal, but structure is adequate.

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 prerequisites, alternatives, behavior, schema details, output format (findings packet), performance expectations, and edge cases like empty gaps. No output schema, but description sufficiently explains return values.

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 100% of parameters; description adds meaning: explains depth levels (quick=3 facets, standard=5+gap recovery+contradictions, thorough=8+leads+contradictions) and that question is natural language. Enhances understanding beyond schema.

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

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

The description clearly states it performs grounded multi-source research across structured data sources, not open-web search, and distinguishes from sibling tools like 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?

Explicitly specifies when to use (broad/multi-part questions over structured data) and when not to (single lookup or breaking news), and provides alternative tools (ask_pipeworx) and account requirements.

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

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

Annotations are consistent (readOnlyHint, idempotentHint, destructiveHint false). The description adds behavioral context: returns top-N most relevant tools with full schemas and examples, ready to call directly, avoiding a second lookup. It also lists supported domains. 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 structured and front-loaded: starts with a clear verb+resource statement, then enumerates usage contexts, then describes output. Each sentence serves a purpose. It is slightly long but efficient for the amount of context provided.

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 thoroughly explains the return value: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' It also includes example queries in the input schema examples. This is complete for a discovery tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining aliases (task, q, search, description) for query, describing limit with default/max, and providing natural language examples. This goes beyond the schema's individual field descriptions.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It uses specific verbs (browse, search, look up, discover) and specifies the resource (tools). It distinguishes itself from sibling tools by being a meta-tool for discovery, not for performing specific data tasks.

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

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

The description explicitly says when to use: 'Use when you need to browse, search, look up, or discover what tools exist...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also implies when not to use (when you already know the tool), providing clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: multi-source fan-out, specific return fields, USPTO API sunset (soft-fail), and GDELT→GNews fallback. No contradiction.

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

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

The description is a single paragraph but is well-structured with front-loaded example queries, specific guidance, and detailed output expectations. Every sentence adds value; could be slightly more structured but is efficient.

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

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

Given the tool's simple input (2 parameters, no output schema) and complexity of aggregating multiple sources, the description covers all necessary context: query examples, input constraints, data sources, return fields, and fallback behaviors. No output schema required because description documents return values sufficiently.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds context: 'type' only supports 'company' (person/place coming soon) and 'value' must be ticker or CIK, not names—clarifying limitations and referencing resolve_entity for names.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It lists example queries like 'tell me about X' and enumerates specific data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), distinguishing it from siblings.

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

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

Explicitly advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also specifies necessary input format (ticker or CIK) and directs to resolve_entity when only a name is available.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description confirms delete action but adds no extra behavioral context (e.g., what happens if key doesn't exist). With annotations covering core aspects, description adds minimal value.

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

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

Two concise sentences with critical information front-loaded: action and resource. No unnecessary words.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, usage conditions, and relationships to siblings. Lacks details on error handling or return behavior, but acceptable for simplicity.

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

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

Schema coverage is 100% and schema already describes 'key' as 'Memory key to delete'. Description does not add any additional meaning, format, or constraints beyond that.

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

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

Description clearly states 'Delete a previously stored memory by key' – specific verb (delete) and resource (memory). Distinguishes from sibling tools like 'remember' and 'recall' by mentioning pairing and implying complementary actions.

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

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

Explicitly says when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also mentions pairing with 'remember' and 'recall' to guide tool selection.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description adds value by detailing the process (fetches page, extracts title/description/key links, emits markdown). 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 a single, well-structured paragraph that front-loads the purpose. It is concise at about 60 words, though a bullet list could improve scannability.

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

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

Without an output schema, the description compensates by stating the output format (single text blob ready for site-root/llms.txt). The tool has only 2 parameters, and the description covers the overall behavior adequately.

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

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

Schema description coverage is 100% for both parameters (url, max_links). The description does not add extra meaning beyond the schema, so baseline 3 applies.

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

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

The description clearly states the action (generate), the resource (llms.txt file for a URL), and the purpose (for AI crawlers to index the site). It is specific and distinguishes from all sibling tools, none of which perform this function.

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 (client site indexing, personal project drafting, competitor auditing). It does not explicitly state when not to use it or mention alternatives, but the context is clear and helpful.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description's addition of return fields (created_at, last_fired_at, fire_count) adds moderate value. No contradictions. The description does not disclose any additional behavioral traits beyond what annotations imply.

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

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

Two sentences: first defines purpose and output, second provides usage guidance. No wasted words. Structure is front-loaded with key information.

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

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

For a simple list function with one optional boolean parameter and no output schema, the description fully covers purpose, return fields, and usage context. It is complete 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?

Schema has 100% description coverage for the single parameter (include_inactive). The description does not add further meaning beyond the schema's explanation. Baseline score 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 'List the caller's active subscriptions' and enumerates the return fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes itself from siblings like subscribe and unsubscribe by focusing on listing active subscriptions.

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

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

Explicitly states 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context on when to invoke the tool. However, it does not explicitly mention when not to use it or provide alternatives.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true, covering safety. The description adds context about output fields but does not disclose any additional behavioral traits 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 a single, concise sentence that directly tells the agent what the tool does. No extraneous information.

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

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

Given the tool's simplicity (one parameter, no output schema, low complexity), the description fully explains its purpose and outputs. No additional details are 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%; the schema already describes the single parameter as 'The User-Agent string.' The description does not add further meaning beyond that.

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 precisely states the verb 'parse' and the resource 'User-Agent string', listing specific outputs (browser, OS, device type, bot flag). It clearly distinguishes from sibling tools, none of which are similar.

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 does not provide explicit guidance on when to use this tool vs alternatives, nor does it mention exclusions. However, the purpose is clear enough that no direct sibling competition exists among the listed 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?

Annotations indicate non-readonly, non-idempotent, non-destructive behavior. The description adds key behavioral traits: rate-limiting, free usage, team reads daily, and impacts roadmap. 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 front-loaded with purpose, followed by usage guidelines and constraints. Every sentence adds unique information with no redundancy. Length is appropriate for the tool's complexity.

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

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

Given the tool's simplicity, the description covers all necessary aspects: what it does, when to use it, how to provide feedback, constraints, and consequences. No output schema is needed for this feedback tool.

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

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

Schema coverage is 100%, with clear enum descriptions. The description adds value by specifying format expectations (be specific, 1-2 sentences, 2000 chars max) and how to use the context object, which goes beyond the schema.

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

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

The title 'Send Pipeworx Feedback' and description clearly state the tool's purpose: to report bugs, feature requests, data gaps, or praise. It distinguishes itself from sibling tools (like ask_pipeworx or discover_tools) by being a communication channel rather than an information retrieval tool.

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

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

The description explicitly tells when to use each feedback type (bug, feature, data_gap, praise) and what not to include (end-user prompt). It also provides rate limits (5 per identifier per day) and confirms it's free and doesn't count against quota.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds specific behavioral details: data source (CF analytics-engine), no PII, caching strategy (5min-1h), and that it's aggregated counts. No contradiction.

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

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

The description is concise, with a single paragraph and numbered use cases for clarity. Every sentence adds value, starting with the core function and ending with technical details.

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

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

Given a single optional parameter, rich annotations, and no output schema, the description covers return contents, use cases, data provenance, privacy, and caching. It is fully self-contained for agent understanding.

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

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

The schema describes the 'window' parameter with an enum, but the description adds meaningful semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This goes beyond the schema's basic 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 returns top tools, packs, and call volume, using specific verbs like 'returns' and 'discovering'. It differentiates from sibling tools by focusing on aggregate trends from other AI agents, not individual queries or discoveries.

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

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

The description explicitly lists three concrete use cases (discovering hot data sources, confirming canonical tools, aligning use cases). While it doesn't explicitly say when not to use it, the context is clear. Sibling tools exist but not directly compared.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds extensive behavioral details: internal logic (monotonicity, partition sums, semantic anchor, partition filter, fill check), response structure, and conditions for valid trade signals. 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 detailed and informative, but somewhat rambling without clear sectioning. Every sentence adds value, but better structure (e.g., bullet points for modes and constraints) could improve readability while maintaining completeness.

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

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

Given no output schema, the description fully explains the response structure (opportunities[], partition_check with fields, fill check details) and covers edge cases (low similarity, placeholders, fill check thresholds). The agent has enough context to understand and interpret results correctly.

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

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

Schema coverage is 100% with both parameters described. The description adds substantial meaning: explains the difference between event and topic, provides examples of slugs and seed questions, and details the behaviors triggered by each parameter (e.g., event walks child markets, topic searches related events).

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, specifies three invocation modes (no args, event, topic), and distinguishes it from sibling tools like polymarket_fill_risk by mentioning custom sizing through that tool.

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

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

The description explicitly advises when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It also recommends event mode and warns when not to trade based on fill check. However, it does not directly contrast with siblings like polymarket_edges.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds significant behavioral context: caching (1h KV keyed on knobs), tradeable-edge filters (min_liquidity, max_spread_pp), diagnostics for empty segments, and detailed edge metrics. 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 thorough but lengthy; it includes detailed explanations of model families, edge metrics, and knob effects. While structured with sections, the density of technical details could overwhelm agents. Slightly more concise phrasing would improve readability without losing essential info.

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 output structure (by_segment, diagnostics), edge metrics, tradeable-edge knobs, and caching. It explains what to expect from each segment and why segments might be empty. This is comprehensive and leaves 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 coverage is 100%, so parameters are well-documented. The description adds extra context beyond schema for several parameters, e.g., slippage_pp explains Polymarket's zero fees and typical spread, min_partition_leg_kelly clarifies its role in partition arbs. This improves understanding beyond basic schema descriptions.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, targeting 'what should I bet on today.' It distinguishes the tool by focusing on edge discovery without paging, which differentiates it from siblings like polymarket_arbitrage or polymarket_edge_tracker.

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 the tool is built for daily bet discovery and details three segments (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT). It mentions Fed bets are excluded from ranking. However, it does not explicitly compare this tool to alternatives like polymarket_arbitrage, leaving some ambiguity about when to use one over the other.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description reinforces this and adds detailed behavioral context: data source, snapshot gaps, decay computation, and limitations. 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 informative but lengthy. It is well-structured with clear sections (purpose, response format, limitations). Could be slightly more concise, but the detail aides understanding.

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

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

Despite no output schema, the description fully explains the response structure (tracked, expired, snapshot_dates) and their fields. This compensates for the lack of structured schema, making the tool's behavior 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?

Input schema covers both parameters with descriptions. The description adds clamping info for 'days' (2-30) and clarifies 'window' defaults, slightly enhancing schema coverage. However, schema already provides defaults.

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: providing edge persistence and decay telemetry from daily snapshots. It contrasts with a fresh vs. old edge, distinguishing its analysis from raw edge data provided by sibling 'polymarket_edges'.

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

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

The description includes limitations (60-day TTL, daily closes) and details about response structure that guide usage. While it implies when to use, it could be more explicit about when not to use in favor 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 indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=true. Description adds depth: walks ladder, returns verdicts (clean/degraded/cannot_fill), mentions forced_directional_risk. No contradictions. Could mention read-only nature explicitly but annotations handle it.

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

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

Description is long but well-structured with capitalization emphasis (REQUIRES, SINGLE-MARKET, BASKET). Each sentence adds value. Minor redundancy in parameter explanations but overall appropriate for 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?

Complex tool with two modes and no output schema, but description lists all return fields for both modes. Covers edge cases (thin books, forced directional risk). Could include example output, but sufficient for agent to understand expected results.

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

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

Schema covers all 4 parameters with descriptions. Description adds context: side default behavior in basket mode, size_usd clamp range, single-market vs basket parameter requirements. Adds value beyond schema.

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

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

Description clearly states it checks realizable-vs-theoretical edge against live CLOB order-book depth, and distinguishes between single-market and basket modes. This differentiates it from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicit instructions: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also explains consequences of partial fills, providing strong when-to-use guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context beyond these annotations, such as the two cases triggering compatibility_warning, the meaning of temporal_alignment, and the skipped cross-type counters. 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 lengthy but well-structured with clear sections for modes, response, and safety fields. Important information is front-loaded, and each sentence contributes useful detail. A slight reduction in length could improve conciseness, but the structure compensates.

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

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

Despite lacking an output schema, the description thoroughly explains all relevant response fields (venue-by-venue prices, matched spread, compatibility_warning, temporal_alignment, skipped counters). It covers the three parameters completely and provides context for interpreting results, making it self-sufficient.

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

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

Schema description coverage is 100%, but the description adds value by listing all 10 topic shortcuts, explaining that explicit parameters override topic-mapped sides, and providing examples. This complements the schema and enhances the agent's understanding of valid parameter values.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts vs explicit event identifiers) and outlines the response structure, setting it apart from sibling tools like polymarket_arbitrage which focus on intra-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 provides explicit when-to-use guidance for comparing prices across venues and details how to interpret response fields (compatibility_warning, temporal_alignment, skipped types). It warns that pre-mapped topics often yield warnings, effectively telling the agent when not to expect tradeable spreads. Alternative tools (e.g., polymarket_arbitrage) are implicitly distinguished.

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

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

Annotations already indicate readOnlyHint true, destructiveHint false, idempotentHint true. The description adds behavioral context: it retrieves previously saved values, lists all keys when key is omitted, and is scoped to an identifier. No contradictions.

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

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

The description is concise at four sentences, all essential information. It is front-loaded with the core action and uses clear, direct language without filler.

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

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

Given the tool's simplicity (one optional parameter, no output schema, descriptive annotations), the description fully covers what the tool does, how to use it, and how it relates to siblings. No gaps.

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

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

Schema coverage is 100% and the description adds significant meaning beyond the schema: it explains that omitting the 'key' parameter lists all saved keys, which is not stated in the schema itself. This greatly aids 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 retrieves a value saved via 'remember' or lists all keys if omitted. It distinguishes from siblings like 'remember' and 'forget' by explicitly mentioning pairing, leaving no ambiguity about its function.

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: 'look up context the agent stored earlier' without re-deriving. It also mentions scoping and pairing with 'remember' and 'forget', effectively guiding the agent on appropriate usage.

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

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

Annotations already indicate read-only, idempotent, open-world, non-destructive. The description adds that mark_read updates read status (a side effect) and mentions the same feed is available via a GET endpoint, providing useful context beyond annotations. No contradictions.

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

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

The description is a single short paragraph of four sentences, front-loaded with the core action. Every sentence adds value—no fluff. Structure is efficient and easy to parse.

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

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

Given that there is no output schema, the description adequately describes the return structure (source, citation_uri, payload). It covers all five optional parameters implicitly through usage context. Mentions polling and alternative endpoint. Sufficient for a read-only list tool with good annotations.

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

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

Schema has 100% coverage, so baseline is 3. The description adds value by explaining what each returned event contains (source, citation_uri, payload) and indirectly explains the effect of parameters like mark_read and unread_only. This semantic context is helpful beyond the schema descriptions.

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

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

The description clearly states the tool's purpose with a specific verb ('Pull fired events') and specifies the resource ('your subscription feed'), making it easy to understand. It differentiates from sibling tools like list_subscriptions by focusing on events rather than subscription management.

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 filtering by type and since, and explains the mark_read parameter for state management. Mentions polling works fine and gives an alternative access method. However, does not explicitly state when not to use this tool compared to 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?

Describes complex behavior including fan-out to multiple sources (SEC, GDELT, USPTO), fallback logic (GDELT → GNews), soft-failure modes (USPTO), and return structure. All annotations are consistent (readOnly, idempotent, openWorld) and description adds significant detail 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?

Front-loaded with common query examples, then covers functionality, parameters, and alternatives. Every sentence is informative, though slightly longer than necessary.

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 aspects needed for a complex tool with 3 required params, no output schema: explains return structure (changes[], total_changes, URIs), source behaviors, and fallback. Complete enough for an agent to invoke correctly.

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

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

Adds meaning beyond the schema: explains 'since' accepts both ISO dates and relative shorthand with examples ('7d', '30d'), clarifies 'value' can be ticker or CIK, and notes 'type' currently only supports 'company'. Schema coverage is 100%, so description provides valuable additional context.

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

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

The description clearly states the tool provides a change feed for a company in a recent time window, fanning out to multiple sources. It distinguishes itself from sibling 'entity_profile' by specifying it returns dynamic changes rather than a static profile.

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

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

Explicitly tells when to use (for 'what's new', 'latest', 'updates') and when not to ('use entity_profile instead for static profile'). Provides concrete query examples to illustrate 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?

Description adds value beyond annotations by explaining scoped key-value storage, persistence for authenticated users, and 24-hour retention for anonymous. Annotations show idempotentHint=true; no contradiction.

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

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

Description is concise (5 sentences) but packs essential details: purpose, examples, scoping, timeout, and sibling links. First sentence effectively front-loads the core idea.

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 purpose, usage guidelines, behavioral traits, and relationships with recall/forget. No gaps.

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

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

Schema coverage is 100% with clear descriptions for both 'key' and 'value'. Description provides usage context but doesn't add new semantic detail beyond the schema examples.

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

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

Description clearly states the tool saves data for reuse, with specific examples like 'resolved ticker' and 'user preference'. It distinguishes from siblings recall and forget.

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

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

Explicitly tells when to use: 'when you discover something worth carrying forward' and mentions pairing with recall and forget. Also provides session vs persistent storage context.

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

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds valuable context: internal cascading through endpoints, auto-disambiguation for companies, and return details (e.g., citation URIs). No contradictions with annotations.

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

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

The description is well-organized, front-loading the core purpose with examples, followed by usage guidance and type-specific details. Every sentence adds value without redundancy, achieving clarity without excessive 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?

Although there is no output schema, the description fully compensates by specifying exact output fields for each type (e.g., ticker, CIK, citation URI). It also explains internal cascading, making the tool's behavior and results completely transparent for an agent.

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

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

With 100% schema coverage, the description goes beyond by explaining acceptable inputs for each type (e.g., ticker, CIK, or name for company; brand or generic for drug) and detailing the output (e.g., ticker + CIK + company_name). This transforms the schema from mere structure to actionable guidance.

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: resolving user-spoken names to canonical identifiers. It provides specific examples (ticker, CIK, RxCUI) and enumerates supported types (company, drug), making it highly distinguishable from sibling tools like compare_entities or entity_profile.

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

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

The description explicitly instructs to 'Use FIRST whenever you have a name but need an ID,' providing clear guidance on when to invoke the tool. It also explains that the tool replaces multiple manual lookups, reinforcing its utility.

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

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

Annotations (readOnlyHint true, idempotentHint true, etc.) already indicate safe, non-destructive behavior. The description adds meaningful behavioral context: it probes each entity with ai_visibility_check, treats the first entity as the 'subject' for narrative, and returns a ranked list with score, confidence, and signal density. This goes beyond annotations.

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

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

The description is concise: three sentences with no unnecessary words. It front-loads the main purpose, then adds details about how it works and when to use it. Every sentence earns its place.

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

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

Given the tool's complexity (4 parameters, no output schema), the description covers all necessary aspects: it explains the return format (ranked list with score, confidence, signal density per entity), the behavior (probing with ai_visibility_check), and the special treatment of the first entity. No gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds some context: the first entity is treated as subject, and context is shared across probes. However, it does not significantly enhance understanding of parameters beyond the schema's descriptions. Thus, score remains at baseline.

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

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

The description clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb 'compare' and the resource 'AI visibility' across entities. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (likely other comparisons) by explicitly mentioning it probes each entity with ai_visibility_check and ranks them.

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

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

The description provides a clear usage context: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' This implies when to use the tool for multi-entity comparison. However, it does not explicitly state when not to use it or contrast with specific alternatives like compare_entities, leading to a slight deduction.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds crucial behavioral context: the tool fans out to two external services, bundlephobia's first measurement can take 5-30s, and partial failures degrade gracefully with sources_failed listing timeouts. This goes well beyond annotation signals.

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 dense paragraph that front-loads the core purpose. Every sentence adds value, but it could be slightly improved with bullet points for the return fields or clearer segmentation. However, it remains efficient and 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?

Despite no output schema, the description enumerates every return field in detail (summary block, per-advisory detail, links, alternative versions). It also explains partial failure handling and the time-out scenario. The tool's complexity is fully addressed.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds further value by noting that scoped packages are accepted for the package parameter and that version defaults to the latest when omitted. These details enhance the schema's description.

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

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

The description uses a specific verb-resource combination ('scan dependency') and clearly states the composite nature of the check. It distinguishes the tool from siblings by specifying it covers npm packages and fans out to two external services, which no other sibling tool does.

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

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

Explicitly states when to use ('when an agent asks is X safe / popular / small') and provides clear exclusion ('NPM ecosystem only in v1') with an alternative approach ('PyPI/Maven/Cargo/Go fall under deps.dev:version directly'). Also explains partial failure behavior.

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 details technical behavior: BGE-base-en embeddings, cosine over 500-char overlapping windows, 200K char cap with truncation flag, and passage offsets for verification. No contradiction with annotations.

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

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

The description is a single paragraph but efficiently packed with purpose, use case, and technical details. It is front-loaded with the main action. Minor improvement could be splitting into sections for readability, but overall concise.

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

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

Given no output schema, the description explains return values (top-N passages with offsets and scores) and truncation. It covers input constraints, workflow pairing, and technical details. Lacks error handling or edge cases, but sufficient for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying the max char limit for 'text', default and range for 'limit', and provides concrete examples for 'query', enhancing understanding beyond schema descriptions.

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

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

The description clearly states it performs semantic search inside a fetched record, with specific examples like SEC 10-K body. It distinguishes itself from siblings by mentioning pairing with ask_pipeworx_grounded for grounding over passages, making the tool's unique purpose clear.

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

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

The description explicitly says 'Use when the record is too big to cram into the prompt,' providing clear context. It also mentions pairing with ask_pipeworx_grounded for a workflow, but lacks explicit when-not-to-use or alternatives. The cap of 200K chars is noted, adding practical guidance.

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

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

The description adds extensive behavioral context beyond annotations, such as rate limits (10 SMS/day), phone verification requirement, webhook auto-disabling after 10 failures, and the one-time return of the webhook signing secret. No contradictions with annotations.

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

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

The description is well-structured using bullet points and clear sections, despite being lengthy. Every sentence adds value, and there is no redundancy or fluff.

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

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

Given the lack of an output schema, the description covers all necessary contextual information: return value, authentication requirements, rate limits, verification steps, and failure behaviors. It is complete for the complexity of the tool.

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

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

The description enriches the input schema with concrete examples for each subscription type and delivery channel, explaining parameters like 'params' and 'delivery' in detail. Since schema coverage is 100%, this goes beyond baseline by providing practical usage guidance.

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 creates a proactive monitoring subscription to a live-data event stream and returns the new subscription id. It distinguishes from siblings by listing specific subscription types and mentioning the requirement for a Pipeworx OAuth account, which sets it apart from other tools.

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

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

The description provides clear context for when to use the tool and mentions prerequisites (OAuth account), but does not explicitly state when not to use it or point to alternative tools like 'list_subscriptions' or 'unsubscribe'. This lack of exclusion guidance prevents a top score.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds context that it returns example questions with tool call shapes from a live catalog, which is useful beyond the annotations.

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

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

The description is relatively long but well-structured with a front-loaded list of example prompts. Every sentence adds value, though slight trimming could improve conciseness.

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

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

Given only one optional parameter, no output schema, and rich annotations, the description fully covers when to use, how to use, and what the output contains. No gaps.

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

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

Schema coverage is 100% with a single optional topic parameter. The description adds meaning by listing valid focus areas (finance, pharma, etc.) with context and explaining that omitting it gives a cross-category spread.

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 category-bucketed example questions with tool+argument shapes, serving as an onboarding entry point. It distinguishes itself from siblings like 'ask_pipeworx' (which answers questions) and 'discover_tools'.

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

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

Explicitly advises to use this tool 'FIRST when you do not yet know what Pipeworx can do for you' and specifies when to pass a topic or omit it. It also tells how it helps learn to call meta-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?

Beyond annotations, the description adds key behaviors: ownership enforcement, deactivation instead of deletion, and preservation of historical events. This provides critical context the agent needs.

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 that convey essential information without redundancy. Could be slightly more structured but highly efficient.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, constraints, and side effects. Missing explicit usage guidance slightly reduces completeness.

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

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

Schema coverage is 100% and already describes id as 'Subscription id (uuid) returned by subscribe.' The description adds no additional semantics beyond that.

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

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

The description states 'Cancel a subscription by id' with a clear verb and resource. It distinguishes itself from sibling tools like subscribe, list_subscriptions, and recent_alerts.

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

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

The description implies usage (cancel your own subscriptions) but does not explicitly compare to alternatives like list_subscriptions or recent_alerts. No when-not-to-use guidance.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context about the internal routing logic (structured vs. grounded pipeline) and the return format (verdict, value, citation, reasoning). It does not contradict annotations and provides useful behavioral details 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 relatively long but well-structured, starting with examples and then explaining the internal routing. Every sentence provides value, though it could be slightly more concise. It effectively front-loads key information about use cases.

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

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

Given no output schema, the description adequately explains the return value (verdict, actual value, reasoning) and the two processing paths. It covers the tool's scope and internal mechanics, making it fairly complete for an AI agent to understand invocation and results.

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

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

The input schema has 100% description coverage, so the schema already documents both parameters. The description mentions tolerance_pct briefly but adds little new meaning beyond the schema. Baseline 3 is appropriate as the description does not significantly enhance 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's purpose: verifying natural-language claims using authoritative sources. It provides multiple query examples and distinguishes between financial and other claims. However, it does not explicitly differentiate from sibling tools like ask_pipeworx_grounded, which may serve similar purposes.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two internal pipelines (financial vs. other) and mentions that it replaces multiple sequential calls. However, it doesn't specify when not to use it or provide alternatives.

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