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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context: that probing Anthropic requires a personal API key and incurs direct costs from Anthropic, and that the tool returns per-model scores and a combined view. 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 only two sentences, front-loading the core purpose in the first sentence and providing essential details and use cases in the second. Every sentence is meaningful; no redundancy or wasted words.

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

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

Despite the absence of an output schema, the description explains the return structure (per-model object with score, confidence, signals, raw_response, plus combined view). All four parameters are described in the schema, and the description adds context for their usage. Use cases are given. The tool is fully understandable.

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

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

Schema coverage is 100% with good parameter descriptions. The tool description adds value beyond the schema by explaining the default model, the key-passing mechanism for Anthropic, and that the 'context' parameter disambiguates common names. This extra context raises it above the baseline of 3.

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

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

The description clearly states the tool's action ('probe one or more LLMs'), resource ('what they know about a business / brand / product / topic'), and output ('score visibility (0-100) per model'). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general visibility scoring rather than competitive scanning.

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

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

The description provides clear usage contexts: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains the default free model and the optional Anthropic probe with BYO key. However, it does not explicitly state when not to use this tool or list 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 read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it routes to 5,354 tools, returns pipeworx:// citation URIs, works on every tier, and is 'one fast call'. This gives the agent a clearer sense of the tool's behavior without contradicting annotations.

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

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

The description is long but each section serves a purpose: preference over web search, use cases, examples, tier escalation, and alternatives. It is front-loaded with the most important directive ('PREFER OVER WEB SEARCH') and structured logically, though slightly repetitive ('Use whenever' and 'START HERE' overlap).

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, the description covers purpose, usage, alternatives, examples, behavioral traits, and output format (structured answer with citations). The absence of an output schema is mitigated by explicitly stating the return format. This is complete for an agent to invoke the tool correctly.

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

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

Schema coverage is 100% with all parameter aliases documented, providing a solid baseline. The description adds value by giving concrete examples of valid questions (e.g., 'current US unemployment rate', 'Apple's latest 10-K'), which clarify the expected format and scope of the question parameter beyond the schema's dry description.

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

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

The description clearly states the tool's purpose: it routes factual questions to one of 5,354 sources and returns structured answers with citations. It explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, making it easy to identify when to use this 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 provides explicit usage guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and 'Step up only when needed' for grounded or deep_research. It lists alternative tools and their specific use cases, giving clear when-to-use and when-not-to-use instructions.

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

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

Annotations already declare safe read-only, open-world, idempotent behavior. The description adds valuable context: the beta status, live candidate routing changes, current inactivity (retired 2026-07-26), and that it falls back to nothing (a full working router). This goes beyond annotations without contradicting them.

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

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

Four sentences, front-loaded with the beta identity and core functionality. While information-dense, the repeated emphasis on 'matches ask_pipeworx exactly' and 'experimental edge' is slightly redundant, though each sentence contributes meaningful context.

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 beta variant of an existing tool, the description covers identity, current behavior, usage guidance, and fallback expectations. It lacks examples of questions or output format, but 'same response shape as ask_pipeworx' suffices given sibling context and thoroughly annotated schema.

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

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

The input schema already provides 100% coverage, documenting the required 'question' parameter and all five aliases. The description only vaguely says 'same arguments' and does not add parameter-level detail, but schema coverage makes this acceptable per 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 identifies the tool as a beta version of ask_pipeworx, a universal router over 5,354 tools with the same arguments and response shape. It distinguishes itself from the stable ask_pipeworx by noting candidate routing improvements, though it does not use a direct verb like 'answers' or 'routes'.

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 it exactly like ask_pipeworx when you want the newest routing,' providing clear when-to-use guidance. It also notes current equivalence to ask_pipeworx and comparison against the stable router, but does not mention alternative tools like ask_pipeworx_grounded or exclusions.

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

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

Beyond the readOnly and idempotent hints in annotations, the description adds the exact return structure (answer, evidence, confidence, source, fetched_at, refusal_reason), lists possible refusal reasons, and discloses the extra LLM call cost. This is substantial behavioral context that annotations alone do not provide.

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

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

The description is dense but well-structured, with two paragraphs covering mechanism, return values, refusal handling, and usage guidance. Every sentence adds value, and the key purpose is front-loaded.

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

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

Despite lacking an output schema, the description fully specifies the response shape, failure modes, routing behavior, and cost implications. The single required parameter is well-documented in the schema, making the tool complete and invokable without 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?

The schema covers all 6 parameters with full descriptions, including alias definitions for 'question'. The description does not add parameter-specific information beyond what the schema already provides, so the baseline of 3 is appropriate.

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

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

The description clearly defines the tool as a 'hallucination-resistant answer mode' that extracts answers strictly from tool results, distinguishing it from the sibling ask_pipeworx. It specifies the verb (answers/extracts) and the resource (high-stakes reads), 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 explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on' and when not: 'prefer ask_pipeworx for casual lookups'. It also names the alternative (ask_pipeworx) and provides a cost trade-off, giving clear decision 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 and destructiveHint=false. The description adds extensive behavioral details: market resolution via classifiers, fan-out patterns, response shapes, safety checks (low-confidence, closed markets, wide spreads), and resolution-rule risk. This goes beyond annotations without contradicting them.

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

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

The description is lengthy with many detailed examples and technical specifics (e.g., fan-out examples, resolution rules). While necessary for completeness, it lacks clear section headers or bullet points, making it dense. Front-loaded with purpose, but overall could be more structured.

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

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

With no output schema, the description thoroughly explains return shapes (result.market, analysis, evidence, resolver contract, parent event, news fields) and error/safety behaviors (low-confidence, closed markets, wide spreads). Covers all aspects given complexity and parameter count.

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 value: examples for 'market' (slug, URL, question text), explains 'depth' options (quick/thorough with default), and provides use-case guidance for 'include_raw' (default false, when to set true). Enhances schema meaning.

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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call,' specifying the verb and resource. It distinguishes from siblings like polymarket_edges and polymarket_arbitrage by explicitly targeting research and providing usage examples ('should I bet on X').

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

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

The description gives explicit when-to-use examples: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It does not directly state when not to use or mention alternatives, but the sibling tools and context imply boundaries. Overall clear usage context.

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

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

Adds details beyond annotations: data sources (SEC EDGAR/XBRL, FAERS), fiscal year handling, primary metric sorting, citation URIs. Annotations already declare readOnly, openWorld, idempotent; description complements well.

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

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

Well-structured with example queries front-loaded. Every sentence adds value; slightly lengthy but justified by 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?

Complete given parameters and annotations: covers entity types, data sources, result ordering, and citation URIs. No output schema, but description explains return format.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: clarifies type enum values, explains values parameter format (tickers/CIKs for company, names for drug), and data fields retrieved.

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 side-by-side comparison of 2-5 companies or drugs, pulling specific data from SEC EDGAR and FAERS. Distinguishes from siblings like entity_profile and get_company.

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

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

Explicitly instructs to prefer over sequential single-entity lookups. Provides example queries. Lacks explicit 'when not to use', but context is clear.

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

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

Annotations already declare read-only, open-world, idempotent hints, but the description adds rich behavioral context: account requirements, depth-dependent behavior (gap recovery, contradiction scanning), return packet structure, citations, semantic excerpting, expected latency, and the explicit promise to never invent answers. 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 long but each sentence carries unique information: account requirements, alternatives, mechanism, depth semantics, citations, contradictions, excerpting, timing. It is front-loaded with the most critical usage distinction (NOT open-web search) and remains well-organized, though it could be tightened by consolidating related 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 the tool's complexity and the lack of an output schema, the description is exceptionally complete. It covers the return format (findings packet), behavioral edge cases (gaps[ ], contradictions[]), depth options, account limitations, expected latency, and when to use alternatives, leaving no significant gaps 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?

Schema covers 100% of parameters, but the description adds significant meaning beyond field names. It explains how 'depth' values map to research behavior ('quick=3... standard=5... thorough=8'), the gap-recovery hops, and that 'question' accepts broad or multi-part natural language, which is the tool's core purpose.

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

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

The description states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1395 STRUCTURED data sources' in one call. It clearly distinguishes from sibling tools, explicitly noting 'this is NOT open-web search' and contrasting with ask_pipeworx for single lookups.

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

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

Provides explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx', plus 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. Also gives account and depth prerequisites.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds valuable context: returns tools with full schemas and examples, 'ready to call directly, no second schema lookup needed.' No contradictions.

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

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

Description is concise, front-loaded with purpose and examples, and every sentence adds value (domains, return structure, usage guidance). No verbosity.

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

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

Given no output schema, the description adequately explains what is returned (top-N tools with names, descriptions, schemas, examples). Could elaborate on ordering or relevance but sufficient for agent decision.

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

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

Schema coverage is 100%, and description adds context about alias parameters (query, q, task, search, description) and default limit of 20. This goes beyond the schema by explaining usage patterns.

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 'Find tools by describing the data or task' and enumerates many domains (SEC filings, financials, FDA, etc.), distinguishing it from sibling tools like search_companies or search_within which search for data, not tool discovery.

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

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Provides clear context for when to use, though does not mention specific alternatives or when not to use.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds context on source fan-out, return fields, and patent soft-fail after May 2025, but does not contradict annotations.

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

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

The description is dense with information but front-loaded with example queries and structured with semicolons for data sources. Slightly lengthy but each sentence adds value.

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

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

No output schema provided, but the description thoroughly explains all return fields (CIK, filings, fundamentals, patents, news, LEI) and includes limitations. Complete for the tool's complexity.

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

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

Schema coverage is 100%, but the description adds semantics: value can be ticker or zero-padded CIK, and notes that names are not supported; type is limited to 'company'.

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

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

The description clearly states it provides a 'full cross-source profile of a US public company in ONE parallel call', lists example queries, and distinguishes from chaining multiple lookups and sibling tools like resolve_entity.

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

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

Explicitly advises to prefer this over chaining individual lookups for holistic views, and directs users to use resolve_entity first if 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 declare destructiveHint=true and idempotentHint=true. Description adds that it deletes a memory, consistent with annotations. No extra behavioral context 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?

Two sentences, front-loaded with purpose, no wasted words. Efficient and clear.

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

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

No output schema; description does not mention return value or confirmation. For a delete tool, an agent may need to know what to expect after invocation. Slight gap in 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 covers 100% with description 'Memory key to delete'. Description adds no additional meaning beyond schema.

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

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

Clearly states 'Delete a previously stored memory by key.' Distinguishes from siblings remember and recall by describing deletion action.

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

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

Explicitly says when to use: when context is stale, task done, or to clear sensitive data. Also suggests pairing with remember and recall. Lacks explicit 'when not to use' but sufficient for simple tool.

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

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, etc.), the description adds behavioral details: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. It also notes the output is a single text blob ready for deployment. 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 appropriately sized: a few sentences that front-load the core purpose and then expand on use cases. It is efficient and avoids unnecessary words.

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

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

Given no output schema, the description adequately explains the output format (standard llms.txt markdown, single text blob). It covers what the tool does, how it works, and use cases. It could mention error handling or limits, but for a simple read-only tool, it is sufficiently complete.

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

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

Schema description coverage is 100% for both parameters (url, max_links), so the baseline is 3. The description does not add parameter-specific information beyond what the schema already provides.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the resource and action. It also lists specific use cases (getting a client's site indexed, drafting for own project, auditing competitor) that distinguish it from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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 enumerates three concrete use cases ('Useful for: ...'), giving clear context for when to use the tool. However, it does not explicitly state when not to use it or contrast with specific sibling tools, which would improve the 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 readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by detailing the return fields (names with history, addresses, companyForms, etc.) and multi-language code meanings, which goes beyond what annotations provide.

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

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

The description is one concise paragraph that efficiently covers purpose, format, example, return fields, and multi-language details. No extraneous information.

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

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

For a simple single-record lookup tool with one parameter and no output schema, the description is remarkably complete. It lists all major return components and explains the multi-language encoding, leaving no obvious 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 description for businessId. The description reinforces the format (NNNNNNN-N) and provides an example, adding extra clarity beyond the schema.

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

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

The description explicitly states it returns the full register record for a single Finnish company by businessId, with format and example. It clearly distinguishes from search_companies and other sibling tools by focusing on a single record lookup.

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

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

The description implies usage when you have a specific businessId, but does not explicitly contrast with alternatives like search_companies or mention when not to use it. No when-not or explicit alternative guidance is provided.

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

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds that it returns specific fields and implies only active subscriptions by default, but no additional behavioral traits beyond annotations.

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

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

The description is two concise sentences with no redundant information, front-loading the purpose and usage.

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 lists returned fields. Usage guidance and parameter are well-documented. Sibling tools provide context. The definition is complete for this simple read-only 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 input schema covers the single parameter 'include_inactive' with a description, achieving 100% coverage. The tool description does not add extra meaning, so baseline of 3 is appropriate.

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

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

The description clearly states the verb 'list', the resource 'subscriptions', and specifies scope 'caller's active'. It also lists returned fields, distinguishing it from siblings like subscribe and unsubscribe.

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

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

The description provides explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. It does not mention when not to use or alternatives, but the context is adequate.

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

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

Annotations provide no hints (all false), but description fully discloses behavioral traits: free, rate-limited to 5/day, no quota impact, team reads daily, and it affects roadmap. 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?

Single paragraph that front-loads purpose, then gives usage guidance, constraints, and operational details. Every sentence adds value; no wasted words.

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

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

For a 3-parameter tool with full schema coverage and no output schema, the description covers purpose, usage, constraints, and what to include. It's complete and fit for purpose.

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

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

Schema coverage is 100%, so baseline is 3. However, description adds value by explaining enum options with examples and specifying message length limit (2000 chars). This extra context justifies a 4.

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

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

Description clearly states the tool sends feedback to Pipeworx about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools by being the dedicated feedback channel.

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 (bug, feature/data_gap, praise) and provides guidance on describing issues, including what not to do. Also mentions rate limits and that it's free, giving clear operational boundaries.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds valuable behavioral details: data source ('CF analytics-engine'), no PII, caching behavior (5min-1h). 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 sentences, front-loaded with core output, followed by use cases and technical details. Every sentence adds value with no waste.

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

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

Given the tool's simplicity (one optional parameter, no output schema, comprehensive annotations), the description covers all necessary aspects: output, use cases, data derivation, privacy, and caching.

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

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

Schema coverage is 100% for the single parameter 'window'. The description adds extra meaning by explaining that shorter windows show hot trends while longer windows show steady-state demand, enriching the enum 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 returns 'top tools, top packs, and total call volume' with a specific verb 'Returns' and resource. It distinguishes from siblings by focusing on trending data based on other AI agents' usage.

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

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

The description explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning use case trends), providing clear context. However, it does not explicitly state when not to use it or mention alternative 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=false. Description adds realizable edge check (≤0 means no book edge), semantic anchor, partition filter, and response structure. Adds substantial context beyond annotations.

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

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

Well-structured with sections for modes, semantic anchor, partition filter, response, fill check. Slightly verbose but every sentence adds value. Efficient for a complex tool.

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 response fields (opportunities[], partition_check, fill_check) and behavior. Covers edge cases like placeholder filters, similarity checks, and realizable edge. Complete for a complex tool with 0 required params.

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

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

Schema coverage is 100% with descriptions. Description adds extra meaning: explains event vs topic modes, accepts URLs, and clarifies behavior like cross-event scanning and similarity threshold.

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 on Polymarket via monotonicity violations and partition-sum checks, with specific modes (no args, event, topic) and explains what each does. It distinguishes from siblings like polymarket_fill_risk.

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

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

Provides explicit when-to-use guidance: no args for trending scan, event for specific market, topic for cross-event scanning. Also mentions fill_check and refers to sibling polymarket_fill_risk for custom sizing, giving 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral details beyond annotations: caching (1h at KV level), segments, edge calculation, and diagnostics. No contradiction; all behavioral traits are accurately described.

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

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

The description is long but every sentence adds value. It is front-loaded with the core purpose, then structures details logically (segments, knobs, diagnostics). No redundancy; each part 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?

For a complex tool with 9 parameters and no output schema, the description is remarkably complete. It explains the output structure (by_segment, fed_candidates, _diagnostics), edge calculation, knobs, and caching. The agent has all necessary information to select and invoke the tool correctly.

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

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

Parameter schema coverage is 100%. The description adds extra context for key parameters (e.g., slippage_pp explains default rationale, max_spread_pp suggests a concrete value). While the schema already documents parameters well, the description provides additional operational guidance, raising it above baseline 3.

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

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

The description states a specific verb+resource: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It is explicitly built for 'what should I bet on today,' distinguishing 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 clearly indicates when to use the tool (discovering betting opportunities) and explains tradeable-edge knobs. It does not explicitly state when not to use it or mention alternatives, but the purpose is well-defined and self-contained.

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 substantial behavioral context: response structure (tracked, expired, snapshot_dates), interpretation of decay numbers (from daily closes, not intraday), and limits (60-day TTL, snapshot gaps). 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 somewhat lengthy but well-structured: it front-loads the core question, then details response fields, then lists limitations. Each sentence adds value, with no obvious fluff. Could be slightly more concise, but effective.

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

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

Given no output schema, the description provides thorough details on the return format (tracked, expired, snapshot_dates) and their meanings. It also covers limits and data freshness. For a read-only tool with 0 required params, this is highly 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 descriptions for both parameters. The description adds value by clarifying defaults and constraints (e.g., days default 14, max 30; window snapshot family). It also explains the response structure, which helps understand parameter impact.

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: providing edge persistence and decay telemetry from daily polymarket_edges snapshots, answering 'how long has this edge existed and is it shrinking?' This is a specific verb+resource that distinguishes it from siblings like polymarket_edges.

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

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

The description implies when to use via the example contrasting a fresh wide edge with a 3-week-old edge, but it does not explicitly state when not to use it or name alternative tools. However, the context is clear enough for an agent to infer 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?

The description adds significant behavioral context beyond annotations: it explains that the tool walks the ladder to check order book depth and returns a verdict (clean|degraded|cannot_fill). It also discloses forced_directional_risk in basket mode. Annotations already indicate read-only, idempotent, non-destructive, and the description is consistent.

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

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

The description is verbose and runs several paragraphs. While it front-loads the purpose and requirements, it could be more structured. Given the complexity of the tool, the length is somewhat justified but not optimally 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?

The tool has no output schema, so the description fully covers expected outputs for both modes (e.g., top_of_book, vwap_fill_price, verdict for single-market; theoretical_sum, realizable_sum, thin_legs for basket). It also explains risks and the rationale for usage, making it self-contained.

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

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

Schema coverage is 100%, but the description adds extra meaning: it explains the two modes (market vs event), default values for side and size_usd, and interprets size_usd as settlement notional for basket mode. It also mentions a clamp range (10–1,000,000) not in the schema.

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

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

The description clearly states the tool checks realizable vs theoretical edge against live CLOB order-book depth for Polymarket. It distinguishes between single-market and basket modes, and explicitly differentiates 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?

The description provides explicit guidance on when to use the tool: before acting on any polymarket_arbitrage signals or polymarket_edges trades above ~$500. It also warns about the risks of partial basket fills converting an arb into an unhedged directional position.

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

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

Annotations declare readOnlyHint, idempotentHint, openWorldHint true, and destructiveHint false. The description adds context about safety fields (compatibility_warning, temporal_alignment, skipped counters) and explains situations like non-equivalent bet shapes or temporal misalignment. 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 well-structured: summary sentence, mode explanation, response format, safety fields. Every sentence adds value, though length is slightly above average. Could be trimmed slightly but remains clear.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters). It covers potential pitfalls and edge cases, providing complete context for correct usage.

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

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

Schema coverage is 100% (all three parameters described). The description enriches semantics by explaining the two modes, how topic auto-maps, and how explicit tickers override the topic side. Examples in schema further aid understanding. This adds value beyond schema alone.

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

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

The description specifies the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

Two modes (topic shortcuts or explicit tickers) are clearly explained. It warns that most pre-mapped topics return compatibility_warning, guiding users to manage expectations. Conditions for compatibility_warning are detailed, providing explicit when-to-use and when-not-to-use guidance.

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

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

Annotations already mark it as read-only and idempotent. Description adds scoping details and behavior when key is omitted. No contradiction; consistent with read-only nature.

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

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

Three sentences, front-loaded with main action. Each sentence adds value (retrieval, examples, scoping). No fluff.

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

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

For a simple read-only tool with one optional parameter, the description covers usage, scoping, and relationship to sibling tools. Complete given lack of output schema.

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

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

Schema coverage is 100% for the single parameter. Description adds crucial behavior: omitting key lists all keys. This enriches understanding beyond 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 clearly states the tool retrieves a saved value or lists all keys (when key omitted). It uses specific verbs 'retrieve' and 'list' and distinguishes from siblings 'remember' and 'forget' by mentioning pairing.

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

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

Provides examples of when to use (look up stored context) and mentions scoping. Doesn't explicitly state when not to use, but the 'pair with' hints at alternatives. Overall clear enough for an agent.

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

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

The description adds significant behavioral context beyond annotations: it states that mark_read:true affects subsequent calls, explains return fields, and confirms polling behavior. No contradiction with annotations.

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

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

The description is informative and well-structured, with the main action stated first. It contains several sentences, but each adds value (filtering, mark_read behavior, alternative endpoint). Slightly longer than minimal but justified.

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

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

Given no output schema, the description adequately explains return values and provides context for polling and alternative access. It covers filtering, mark_read, and unread_only parameters. Slightly missing details on pagination or performance but overall 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 descriptions for all 5 parameters. The description reinforces the use of type, since, and mark_read but does not add new semantic details beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states it 'pulls fired events from your subscription feed' and specifies return fields (source, citation_uri, raw event payload). This verb+resource combination uniquely identifies its function among siblings like list_subscriptions and search_within.

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

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

The description explains when to use this tool (e.g., polling works fine) and mentions an alternative endpoint for scripts/dashboards. It does not explicitly list when not to use it, but the context is clear.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds details: fans out to SEC, GDELT (with fallback to GNews), USPTO soft-fail, and returns grouped changes with citation URIs. Behavior is well-described 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 dense but well-structured, starting with examples and then detailing behavior. It is not overly verbose, though a bullet list for sources might 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?

Given the tool's complexity (multiple sources, fallback, relative dates) and no output schema, the description covers key aspects: window parameter, data sources, return structure (grouped changes, count, URIs), and a sibling comparison. A minor gap is the lack of detail on result quantity or pagination.

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 value by clarifying type enum (only company), giving examples for 'since' (ISO date, relative shorthand, typical '30d'), and explaining 'value' as ticker or CIK. These enrich the schema definitions.

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

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

The description clearly states the tool provides a change feed for a company over a time window, covering SEC filings, news, and patents. It cites example queries and distinguishes from sibling 'entity_profile' for static profiles.

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

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

The description explicitly recommends entity_profile for static profiles, implying recent_changes is for dynamic updates. It provides query examples but does not exhaustively list when not to use.

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

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

Annotations show write operation, idempotent, non-destructive. Description adds value by specifying scoping by identifier and persistence differences between authenticated users and anonymous sessions.

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

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

Four sentences, front-loaded with main purpose, every sentence adds value. No wasted words.

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

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

Adequate for a simple write tool with no output schema. Covers when, what, scoping, and durability. No return value explanation needed.

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

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

Schema covers both parameters with descriptions (100% coverage). Description does not add additional parameter-level meaning beyond the schema, meeting 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 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes from siblings like recall and forget by mentioning they are paired.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and provides examples. It mentions pairing with recall and forget but does not include when-not-to-use guidelines.

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

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

Annotations already mark it as read-only, idempotent, open-world, and non-destructive. The description adds substantial behavioral detail: it cascades through multiple endpoints internally, replaces 2-3 manual lookups, and specifies exact return fields (ticker, CIK, RxCUI, citation URIs) for each type. 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 somewhat lengthy but well-organized: front-loaded with example queries, followed by supported types and their return values. Every sentence adds value, though minor redundancy exists (e.g., 'use FIRST' appears twice). Overall efficient for the information conveyed.

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

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

Despite lacking an output schema, the description fully compensates by detailing return values for each entity type, including citation URIs. It covers input formats, internal behavior, and use cases. For a 2-parameter tool with no output schema, the description is exceptionally 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%, so the baseline is 3. The description enriches parameter meaning with examples ('AAPL', '0000320193', 'ozempic') and clarifies that 'value' accepts multiple formats (ticker, CIK, name for company; brand or generic for drug). This goes beyond the schema's minimal 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 uses specific verbs ('resolve') and resources ('entity'), with concrete user query examples ('What's the ticker for...', 'find the CIK for...'), clearly identifying the tool's role of converting names to canonical IDs. It covers supported types (company, drug) and distinguishes itself as the go-to for name-to-ID, though sibling differentiation could be more explicit.

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 FIRST whenever you have a name but need an ID', giving clear context for when to invoke. Does not specify when not to use or list alternative tools, but the context is sufficiently clear for an agent to decide.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds output details (ranked list with score, confidence, signal density) and process (probes each entity), providing extra transparency beyond annotations.

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

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

Two-sentence paragraph is concise and front-loaded with purpose. Could be slightly more structured, but no redundancy. 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 rich annotations, full parameter descriptions, and no output schema, the description adequately covers purpose, process, output format, and use case. Lacks data freshness info but acceptable.

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

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

Schema covers all parameters (100% coverage). Description adds context on first entity treated as subject, context parameter purpose (disambiguate), and defaults for models. 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 the tool compares AI visibility across multiple entities, specifies the process (probes each entity with ai_visibility_check, ranks by score), and distinguishes it from the single-entity sibling ai_visibility_check.

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

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

Explicitly states use case for competitive AI-marketing audits with an example question. Implicitly suggests when not to use (single entity) via sibling context, but no explicit alternative mention.

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 readOnly, idempotent, non-destructive. Description adds critical behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. Also documents return structure, which is essential given no output schema.

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

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

The description is front-loaded with the main purpose and use case, then details. While relatively long, every sentence adds meaningful information (return fields, partial failures, ecosystems). Could be slightly more concise, but no redundancy.

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

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

Given no output schema, the description thoroughly explains return values (summary block fields, per-advisory detail, links, alternative versions) and handles edge cases (partial failures, timing). With only 2 parameters fully documented, the description is complete and leaves no critical gaps.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by noting that scoped packages (e.g., '@types/node') are accepted and that version defaults to latest when omitted. This provides practical usage details beyond the schema.

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

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

The description clearly states the composite nature of the tool, specifying it checks npm packages across deps.dev and bundlephobia. It uses a specific verb ('scan') and resource ('npm package dependency'), and distinguishes from siblings by limiting to npm ecosystem. The purpose is immediately understandable.

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

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

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also specifies that other ecosystems like PyPI fall under different tools (deps.dev:version directly), providing clear when-not-to-use and alternatives.

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

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

Annotations already cover readOnly, idempotent. Description adds result structure and pagination (page param). 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?

Concise and well-structured: 5 sentences, no fluff, front-loaded with purpose, then result format, then examples.

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 result shape, example usage, and sibling reference. Missing details like AND vs OR for multiple filters, but adequate for a search tool with good schema.

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

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

Schema covers all 6 parameters with descriptions (100% coverage). Description adds examples but little new semantics 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 searches the Finnish business register by name/filters. It explicitly distinguishes from sibling 'get_company' for exact businessId lookup.

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 when to use businessId here vs. get_company. Gives examples with filters. Does not compare to other siblings but is adequate for intended use.

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

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

Discloses algorithm details (BGE-base-en embeddings, cosine similarity, 500-char windows) and limits (200K char cap with truncation flag). Annotations already indicate safety, so this adds significant behavioral context.

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

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

Well-structured with clear sections: purpose, use case, pairing, technical details. A bit lengthy but every sentence is informative; minor room for tightening.

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

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

Despite no output schema, the description explains return values (top-N passages with offsets and scores). It covers all key aspects for effective use: purpose, usage, algorithm, constraints, and output.

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

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

Schema description coverage is 100% with descriptive parameter explanations. The description adds examples for 'query' and clarifies character limits for 'text', enhancing understanding beyond the schema.

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

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

The description clearly states 'semantic search INSIDE a fetched record' and specifies the use case when records are too large for the prompt. It distinguishes from sibling tools like ask_pipeworx_grounded.

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

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

Explicitly advises to use when the record is too big to fit in the prompt and suggests pairing with ask_pipeworx_grounded. This provides clear 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 include idempotentHint=true, but the description does not address idempotency. However, it discloses important behavioral details such as authentication requirements, delivery constraints (phone verification, 10/day SMS cap), and webhook signing. No contradiction with annotations.

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

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

The description is well-structured, starting with the purpose and return value, then prerequisites, supported types with examples, and delivery channels. Every sentence is informative, with no wasted words.

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

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

Given the complexity (3 params, nested objects, no output schema), the description covers purpose, return type, prerequisites, all parameter details, and delivery constraints. It lacks error handling or authentication details beyond OAuth, but overall is quite 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%, so baseline is 3. The description adds significant value beyond the schema by providing detailed examples for each type-specific params, explaining delivery channels with constraints, and clarifying valid 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's purpose: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses a specific verb ('create') and resource ('subscription'), and distinguishes from siblings like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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

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

The description provides guidance on when to use the tool, including prerequisites ('Requires a Pipeworx OAuth account') and supported subscription types with examples. It implicitly contrasts with sibling tools but does not explicitly state when not to use it or list all alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds useful context: that the tool returns category-bucketed example questions with exact tool+argument shape, which is beyond what annotations provide. 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 moderately lengthy but well-structured: it lists example queries, explains functionality, and gives usage guidance. Every sentence adds value, though it could be slightly more concise without losing clarity.

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

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

Given the tool's role as an onboarding entry point with only one optional parameter and no output schema, the description fully explains what the tool returns, how to use it, and when to use it. It is complete for its purpose.

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

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

Schema coverage is 100% with a concise description of the topic parameter. The description adds value by explaining the default behavior when omitted and giving examples of valid values, enhancing understanding beyond the schema.

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

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

The description clearly states it returns category-bucketed example questions about Pipeworx capabilities. The verb 'returns' and resource 'example questions' are explicit. It also distinguishes itself from sibling tools by being the onboarding entry point.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to narrow with the topic parameter. It also mentions learning how to call meta-tools, providing clear context for when to use this vs others.

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

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description adds ownership enforcement and deactivation vs deletion behavior, which are critical for safe invocation.

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

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

Two sentences, front-loaded with the primary action, no redundant words. Every sentence adds value.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, ownership, side effects, and references to related tools (subscribe, recent_alerts). It is 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 a clear description of the 'id' parameter. The description reiterates 'by id' but does not add new semantic details beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Cancel a subscription by id'), specifies the resource (subscription), and differentiates from sibling tools like subscribe and recent_alerts by explaining the deactivation behavior.

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

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

Provides clear context: when to use (cancel a subscription by id), ownership enforcement (only your own), and behavioral outcome (deactivation, not deletion). Does not explicitly list when not to use or name alternative tools, but the context is sufficient.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description goes beyond by explaining the two internal routing paths (EDGAR fast path vs grounded pipeline), the exact verdict types returned, and that it provides verbatim evidence with citations. No contradictions.

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

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

The description is longer but every sentence adds substance—examples, routing logic, return structure. It is well-organized with natural language examples and a clear separation of claim types. Slightly verbose but 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 complexity (two execution paths, no output schema), the description fully covers what the tool does, how it works, what it returns (verdict, value, citation, reasoning), and when to use which path. 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 both parameters described. The description adds value by clarifying that tolerance_pct overrides implied tolerance and is capped at 5 by default, and provides examples for the claim parameter. This complements the schema well.

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 'natural-language claim verification against authoritative sources' and lists example use cases. It distinguishes itself from siblings by claiming it replaces 4-6 sequential calls and explicitly handles company-financial claims via a fast path.

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 details when the fast path vs grounded pipeline applies. However, it does not explicitly mention when not to use this tool or suggest alternatives among siblings.

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