Ipify

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds transparency about the default model, API key forwarding, and the returned structure per model. 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?

Three concise sentences that front-load the core purpose, then detail model options and return types. Every sentence adds value without redundancy.

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

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

Given the tool's moderate complexity (4 params, no output schema but clear annotations), the description adequately covers usage, behavior, inputs, and return structure. Use cases are explicitly listed.

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

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

Schema description coverage is 100%, so the parameter descriptions in the schema are already informative. The description reiterates the purpose of `_apiKey` and `context` but does not add substantial new semantic meaning beyond the schema.

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

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

The description clearly states the tool probes LLMs for entity visibility and scores per model. It distinguishes itself from siblings like compare_entities and scan_competitor_ai_presence by specifying the scoring mechanism and supported models.

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 use cases (AI-marketing audits, brand checks, monitoring) and explains when to supply an API key for Anthropic. However, it does not explicitly exclude inappropriate usage or mention alternatives for similar 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 already declare readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds value by explaining the internal routing to 3,745 tools, argument filling, and stable citation URIs. No contradiction with annotations.

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

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

The description is thorough but efficient, front-loading the key directive. It includes examples and domain lists without being overly verbose. Slightly long but each sentence contributes useful information.

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

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

Given the tool's broad scope and lack of output schema, the description provides ample context: purpose, usage triggers, internal routing, and return format (structured answer with citation URIs). It is fully adequate for an agent to decide when to invoke this tool.

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

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

Schema coverage is 100% with all parameters documented as aliases for question. The description does not add further parameter-level details, but the schema itself is sufficient. Baseline 3 is appropriate since the description offers no additional parameter semantics 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 that the tool handles questions about structured data from many domains, distinguishes itself from web search, and names the action of routing to the appropriate tool. It provides specific verb and resource, and clearly differentiates from sibling tools like deep_research 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 explicitly says to prefer over web search for data like SEC filings, FDA data, etc., and provides clear when-to-use criteria with trigger phrases (e.g., 'what is', 'look up', 'find') and concrete examples. This gives excellent guidance for the agent.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds crucial behavioral details: the refusal mechanism with five specific reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the fact that it costs an extra LLM call, and the guarantee to extract only from tool result. No contradictions with annotations.

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

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

The description is dense and front-loaded with the core concept. Every sentence adds value: purpose, mechanics, return format, use cases, sibling differentiation. Could slightly reduce phrasing like 'Same routing as ask_pipeworx — picks the right tool...' but still efficient for the information density.

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 (3,745 tools, 884 sources, many siblings) and lack of output schema, the description fully specifies return structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and all refusal reasons. It also explains cost trade-off and use cases, making it complete for agent decision-making.

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

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

Schema coverage is 100% and describes the main parameter 'question' with aliases. The description does not add further parameter-level meaning beyond noting aliases and natural language acceptance. Baseline of 3 is appropriate as schema already provides complete parameter documentation.

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 for high-stakes reads. It specifies the exact process: routes to correct tool, fills arguments, fetches data, extracts answer only from tool result, and returns structured output. Distinguishes from sibling ask_pipeworx by noting identical routing but grounded extraction.

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

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

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with domain examples (financial, legal, medical). Also provides exclusion: 'prefer ask_pipeworx for casual lookups.' This is clear guidance on alternatives and contexts.

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

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

Annotations already indicate readOnly/ idempotent. Description adds extensive behavioral details: low-confidence short-circuit, closed market handling, illiquid spread notes, resolution-rule risk, resolver contract, parent event extractor. 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 quite lengthy with many details. Front-loaded with purpose, but includes dense paragraphs that could be trimmed or structured. Still valuable, but not as concise as possible.

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 3 params and no output schema, the description covers input examples, response shapes, resolver contract, safety mechanisms, edge cases (closed markets, illiquid spreads), and resolution-rule risk. Very thorough for a complex tool.

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

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

Schema coverage is 100% with descriptions. The description adds examples (slug, URL, question text) and explains include_raw use cases and depth options. Provides additional context beyond schema.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data in one call. It distinguishes from siblings like polymarket_arbitrage by focusing on single-bet research with data fan-out. Specific verb+resource+scope.

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

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

Explicit use cases are given: 'should I bet on X', 'what does the data say', 'is there edge'. Examples of fan-outs for different bet types provide clear context. Does not explicitly state when not to use, but siblings offer alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds behavioral context: data pulled (e.g., 10-K financials, FAERS counts), handling of off-calendar fiscal years, sorting by primary metric, and return format with citation URIs. No contradictions.

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

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

The description is a single dense paragraph that efficiently conveys purpose, usage, and details. It uses examples and directives ('ALWAYS PREFER') without wasted words. Slightly long but justified by the information density.

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 comprehensively covers input constraints, data sources for both entity types, sorting behavior, fiscal year handling, and return format (paired data with citation URIs). It leaves no significant gaps for an agent to guess.

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 significant value: for 'type', it elaborates on what each enum pulls (company financials vs drug trial data); for 'values', it gives concrete examples and format requirements (tickers/CIKs for company, names for drug). This goes beyond the schema's generic descriptions.

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

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

The description clearly states the tool does a side-by-side comparison of 2-5 companies or drugs in one call, with specific examples ('Compare X and Y', 'X vs Y'). It distinguishes from sibling tools by explicitly preferring over sequential single-pack lookups and details data sources (SEC EDGAR/XBRL for companies, FAERS for drugs).

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides example queries. It lacks explicit when-not-to-use statements but clearly implies its purpose for comparisons, which is sufficient for 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, idempotentHint, etc. Description adds process details (decompose, route, extract), evidence with citations, explicit gaps, and time expectation (15-60s). Does not conflict with annotations. Adds significant behavioral context.

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

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

Description is dense but well-organized. Front-loaded with core purpose and process. Each sentence contributes value (account, plan, time, output format). Could be slightly more concise but efficient for the complexity.

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

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

No output schema, but description explains return format: structured findings packet with evidence, confidence, source, citations, and explicit gaps. Also covers prerequisites (account, plan) and time expectation. Adequately complete for a complex tool.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: explains depth enum values (quick=3, standard=5, thorough=8) and that thorough requires paid plan. Also clarifies question parameter accepts broad/multi-part. Provides 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 'grounded multi-source research in ONE call' and explains decomposition and parallel tool routing. Distinguishes from sibling ask_pipeworx as single lookup. Specific verb (research) and resource (multi-source, structured findings).

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 for broad or multi-part questions' and contrasts with ask_pipeworx for single lookups. Also mentions account requirement and depth plan limitation. Clear when to use and when not.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds return format details (names, descriptions, schemas with examples) but no additional behavioral traits like rate limits or query limitations.

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 front-loaded purpose, then usage context, then return details. Slightly long but each 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?

Complete for a tool discovery tool: explains return format and usage. No output schema, but description compensates by describing what is returned.

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

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

Schema coverage is 100% with descriptions for each parameter. Description adds value by explaining aliases ('Accepts task, q, description, search as aliases') and providing examples 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 it finds tools by describing data or task, listing many domains. It distinguishes from siblings by being a meta-tool for tool discovery, explicitly saying 'Call this FIRST'.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. Does not explicitly state when not to use, but the guidance 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds context: fans out across multiple sources, returns specific fields, mentions patent soft-fail. No contradiction with annotations.

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

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

Description is dense but well-structured, front-loading with example queries and then enumerating outputs. Every sentence adds value, though slightly long. Could be broken into bullets for readability.

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

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

Given complexity (multiple sources, no output schema), description covers return fields, formats (URIs), limitations (patents sunset), and fallbacks (GDELT→GNews). Fairly complete for a tool of this scope.

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

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

Schema coverage is 100% and schema describes parameters. Description reinforces that value must be ticker or CIK, not names, and explains why (use resolve_entity). Adds meaning beyond schema.

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

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

Description uses specific verbs ('returns full cross-source profile') and resources ('US public company'), listing multiple data sources and fields. Clearly distinguishes from siblings like resolve_entity by specifying that names are not supported.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack lookups when the user asks for a holistic view' and advises using resolve_entity first if only a name is given. Provides clear 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 provide destructiveHint=true and idempotentHint=true. The description adds context about usage but does not disclose additional behavioral traits such as error handling or confirmation. It does not contradict annotations.

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

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

Two sentences: first states the action, second provides usage guidance. No redundant words. Highly concise and well-structured.

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

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

For a simple one-parameter tool with no output schema and adequate annotations, the description covers the core action and use cases. It could mention the return behavior or error handling, but 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?

The input schema has 100% coverage with a description for the 'key' parameter. The description does not add extra meaning beyond 'by key', so baseline 3 is appropriate.

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

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

The description clearly states the verb 'Delete', the resource 'previously stored memory', and the method 'by key'. It distinguishes itself from sibling tools 'remember' and 'recall' by explicitly pairing with them.

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

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

The description provides explicit when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions alternatives by pairing with 'remember' and 'recall'.

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, and non-destructive behavior. The description adds that the tool fetches the page, extracts title/description/links, and emits markdown, which aligns with annotations without contradiction. It does not detail potential issues like rate limits or authentication needs, but for a read-only operation this is sufficient.

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

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

The description is two sentences: first states purpose and output, second lists use cases. It is efficient, front-loaded, and contains no unnecessary words.

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

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

Given the simple nature of the tool (2 parameters, no output schema), the description is fully adequate. It explains the output format and purpose, leaving no gaps for an agent to understand when or how to invoke it.

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

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

With 100% schema description coverage, the baseline is 3. The description does not add significant meaning beyond the schema; it mentions output format but not parameter details. This meets the baseline without adding extra value.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a URL, specifying the verb (generate), resource (llms.txt), and context (AI crawlers). It is distinct 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 provides explicit use cases: getting a client's site indexed, drafting for own project, or auditing a competitor. However, it does not mention when not to use this tool or compare it to alternatives among siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description adds context by specifying 'gateway egress IP,' clarifying the exact IP returned. No additional behavioral traits are disclosed, but annotations cover safety.

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

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

The description is extremely concise—a single noun phrase—with no wasted words. It is front-loaded and clear.

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

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

Given the tool's simplicity (no parameters, rich annotations, output schema exists), the description is complete and tells the agent exactly what the tool does in one phrase.

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

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

The tool has zero parameters, and the schema coverage is 100%, so the description does not need to add parameter information. Baseline for no params is 4.

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

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

The description 'Caller's IPv4 (gateway egress IP)' clearly states the tool returns the caller's IPv4 address at the egress point. It uses a specific verb+resource and distinguishes from the sibling 'ipv6' 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 implies usage when needing the caller's IPv4 address, and the sibling 'ipv6' indicates the alternative for IPv6. However, there is no explicit when-not or detailed guidance beyond the tool's name.

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

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds behavioral context about using gateway egress IP and IPv4 fallback, which is 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?

A single sentence that is perfectly front-loaded with the key purpose. No wasted words; every word earns its place.

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

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

Given the tool has no parameters, explicit annotations, and an output schema, the description is sufficient to inform an agent about what the tool returns and its behavior (IPv4 fallback).

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

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

The tool has zero parameters, so the description does not need to explain parameters. It adds value by describing the output rather than repeating schema details.

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 the caller's IPv6 address, including a note about IPv4 fallback. It distinguishes from the sibling 'ipv4' 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 implies when to use this tool (need IPv6 address) versus the sibling 'ipv4' (need IPv4 address), but does not explicitly state alternatives 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?

Adds value beyond annotations by specifying returned fields. Annotations already indicate readOnly, openWorld, idempotent, non-destructive. 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?

Two sentences, front-loaded purpose, no wasted words.

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

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

For a simple tool with rich annotations and no output schema, the description covers purpose, return fields, and usage guidance completely.

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

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

Schema coverage is 100% and the description does not add extra parameter information; 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 'List the caller's active subscriptions' with specific verb and resource, and lists return fields. It distinguishes from sibling tools 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?

Provides explicit when-to-use context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Does not explicitly name alternatives but context is clear.

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

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

Annotations provide no safety hints, but the description discloses rate limiting (5 per day), zero cost, and that feedback affects roadmap. It does not contradict annotations and adds meaningful behavioral context.

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

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

Every sentence is purposeful: main action, usage cases, content rules, impact, rate limits, cost. Well-organized and front-loaded. No wasted words.

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

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

For a simple 3-param tool with full schema coverage and no output schema, the description covers all necessary aspects: purpose, parameter meanings, usage rules, and behavioral constraints. It could mention what happens after submission, but not essential for correct invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining each type in plain language and giving specific guidance on message content (e.g., reference tools/packs, avoid prompt paste). This goes beyond schema descriptions.

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

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

The description clearly states the tool is for sending feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes via specific use cases and contrasts with sibling tools like ask_pipeworx and discover_tools.

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

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

The description provides explicit when-to-use scenarios (bug, feature/data_gap, praise) and a clear don't (paste end-user prompt). It also mentions rate limits and free usage, but does not explicitly compare to 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, idempotentHint, and destructiveHint false. The description adds valuable context: caching behavior (5min-1h), data source (CF analytics-engine), and lack of PII. No contradictions with annotations.

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

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

The description is a single paragraph that efficiently covers purpose, use cases, data source, caching, and parameter semantics. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description explains return values (top tools, packs, volume). It also covers caching and data provenance. All necessary behavioral aspects are addressed.

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

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

Schema description coverage is 100% for the single parameter. The description adds meaning by contrasting 'what's hot right now' for shorter windows vs 'steady-state demand' for longer ones, which goes beyond the schema's 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 it returns 'top tools, top packs, and total call volume' over a window, with a specific verb ('returns') and resource ('what other AI agents are calling on Pipeworx'). It distinguishes from sibling tools like ask_pipeworx and discover_tools by focusing on aggregated trending data.

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

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

Explicitly lists three use cases, such as discovering hot data sources or confirming a canonical tool. It also hints at window choice (shorter for hot, longer for steady-state). However, it doesn't explicitly state when not to use this tool or mention alternatives.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds extensive behavioral context including algorithmic details (monotonicity violations, partition sum check, fill check with CLOB depth), semantic anchors (Jaccard similarity), and partition filtering logic, going far 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 well-structured, front-loading the key constraint and then detailing each mode and filter. While every sentence adds value, it is somewhat lengthy; a slight tightening could improve conciseness.

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

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

Given the tool's complexity and lack of output schema, the description is remarkably complete. It covers algorithm, filters (semantic anchor, partition filter), fill check, response structure, and references sibling tools for additional use cases.

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 substantial value: explains each parameter's purpose, provides examples of valid slugs, specifies when to use each mode, and adds constraints ('requires one of event or topic'). This far exceeds 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 clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, and distinguishes two modes (event and topic) with specific use cases. It explicitly differentiates from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

Provides clear context on when to use each mode (event for specific market, topic for cross-event scanning) and mentions that calling with no args fails. It references sibling tool polymarket_fill_risk for custom sizing, but lacks explicit exclusions or '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 declare the tool as read-only, idempotent, and non-destructive. The description expands significantly on behavioral traits: it details the caching strategy (1h KV-level), the model families used, the edge calculation logic, and the diagnostic information provided. No contradictions.

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

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

The description is long but well-organized with clear sections and bullet points. It front-loads the purpose and then dives into details. While every sentence adds value, it could be slightly more terse without losing information.

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

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

Given the complexity (no output schema, 9 parameters, multiple model families), the description covers everything an agent needs: response structure (by_segment, diagnostics), parameter usage, and edge interpretation. It compensates fully for the 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 description coverage is 100%, so the baseline is 3. The description adds extra value by grouping parameters into 'tradeable-edge knobs' and explaining interactions (e.g., min_kelly vs min_partition_leg_kelly). It also provides guidance on when to adjust defaults like slippage_pp.

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

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

The description clearly states the tool scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price. It uses a specific verb ('scan') and identifies a distinct resource ('top Polymarket markets'), which differentiates it from sibling tools like polymarket_arbitrage.

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

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

The description explicitly frames the tool for 'what should I bet on today' and mentions it saves agents from paging through hundreds of markets. It provides context on when to use it, but does not explicitly mention alternatives or when not to use it.

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

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

Adds significant context beyond annotations: reads from snapshots, returns time-series, explains decay computation, limits (60-day TTL, no intraday). 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?

Efficient front-loading of purpose, but description is dense. No wasted words, but could benefit from clearer sectioning for readability.

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

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

No output schema, but description thoroughly explains response structure (tracked, expired, snapshot_dates) and their fields. Covers limitations and computation details.

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%. Description repeats schema info (defaults, max) but doesn't add new meaning. 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?

Description clearly states it provides edge persistence and decay telemetry, answering a specific question about edge age and trend. It distinguishes from siblings like polymarket_edges (raw edges) and polymarket_arbitrage.

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

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

Implies when to use: when needing historical edge behavior (new vs old). Does not explicitly state when not to use or name alternatives, but context with sibling tools provides guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it accesses 'live CLOB order-book depth', 'walks the ladder', and returns a 'verdict' (clean|degraded|cannot_fill). This provides additional behavioral context beyond annotations, though annotations already cover the 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?

The description is dense and well-structured with caps for modes (SINGLE-MARKET, BASKET) and bold usage note. Each sentence adds value, but it could be slightly trimmed. No waste, but not extremely 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?

No output schema, so the description must explain return values. It does comprehensively: for single-market lists top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict; for basket lists theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill, thin_legs, max_clean_notional_usd, forced_directional_risk. Also explains why the tool is needed (dominant loss mode). Complete given no output schema.

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

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

Schema description coverage is 100% for all 4 parameters. The description adds context beyond schema: for size_usd, 'max spend on buys, target proceeds on sells' in single-market mode and 'settlement notional S' in basket mode. For side, it explains default behavior in basket mode. Parameter semantics are enriched significantly.

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 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes single-market and basket modes. It lists specific outputs like top_of_book, vwap_fill_price, slippage_pp, etc., and contrasts with sibling tools (polymarket_arbitrage, polymarket_edges). Verb+resource+scope is precise.

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

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

Explicitly states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and explains why (theoretical overround not capturable, partial fills cause unhedged positions). Provides clear 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds rich behavioral context: two modes, response structure, safety fields (compatibility_warning, temporal_alignment, skipped_cross counters), and caveats about non-equivalent bet shapes. No contradiction with annotations.

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

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

The description is dense with valuable information but somewhat verbose. It front-loads the purpose and modes, then details safety fields. While every sentence earns its place, minor trimming could improve conciseness 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?

Despite having no output schema, the description comprehensively covers input modes, output structure, safety features, and limitations (e.g., rare tradeable spreads, temporal alignment). It fully informs the agent about what to expect, making it complete for this complex tool.

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

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

Schema coverage is 100%, but the description adds significant meaning beyond the schema by explaining mode behavior (topic vs explicit tickers, override semantics), providing examples, and clarifying how each parameter affects the lookup. This adds high value.

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

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

The description clearly defines the tool as a cross-venue spread calculator between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on cross-venue comparisons and explicitly stating that it detects when bet shapes are non-equivalent.

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 two modes: topic shortcuts for pre-mapped macros and explicit tickers for custom pairings. It also warns that most pre-mapped topics return a compatibility_warning and are not tradeable, helping agents decide when to use the tool or avoid false expectations.

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 context beyond annotations: retrieval from remembered values, listing keys, scoping, and pairing. Annotations already declare read-only and idempotent, so 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?

Two sentences, each earning its place. Front-loaded with purpose, 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?

Fully describes tool behavior for a simple retrieval tool: retrieval, listing, scoping, pairing. No gaps 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% and description explains key parameter behavior (omit to list all keys), providing additional semantic 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 'Retrieve a value' or 'list all saved keys', specifying verb and resource. 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?

Explains when to use (look up stored context) and what scoping applies (per identifier). Mention sibling tools (remember, forget), but lacks explicit 'when not to use' scenarios.

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

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

Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable behavior: it details that returned events carry source, citation_uri, and raw payload; explains the mark_read flag allows subsequent calls to show only newer events; and confirms polling is fine. No contradiction.

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

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

The description is concise—three sentences total—with the purpose upfront, then key details, then a note on polling and alternative access. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description explains what the return contains (source, citation_uri, raw payload). It covers all 5 parameters, usage patterns (mark_read, polling), and even mentions an alternative access method. For a tool with this complexity, it is complete.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds more meaning: it explains the use of filters (type, since), the effect of mark_read, and the limit parameter implicitly. It provides examples like 'sec_8k' for type and clarifies that since is an ISO timestamp.

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

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

The description starts with 'Pull fired events from your subscription feed,' which is a specific verb ('pull') and resource ('fired events'). It clearly distinguishes from siblings like list_subscriptions or subscribe by focusing on events/alerts.

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

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

The description explains filtering options and notes that polling works fine, also providing an alternative direct API endpoint for scripts. This gives context on when to use this tool versus alternatives, though it does not explicitly list when not to use it.

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

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

Annotations already provide safety hints (readOnly, openWorld, idempotent, non-destructive). The description adds multi-source fan-out behavior, fallback on rate limits/5xx, USPTO soft-failure, and parameter syntax details without contradicting annotations.

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

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

Single paragraph front-loaded with example queries, dense with information (sources, fallback, parameter details, return structure), no redundant sentences.

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

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

Covers return format (changes[] grouped by source, total_changes count, citation URIs), error handling (GDELT→GNews fallback, USPTO soft-fail), and parameter options despite no 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?

Despite 100% schema description coverage, the description adds valuable context: `since` accepts ISO or relative shorthand with examples and typical usage recommendation, `type` only supports 'company', `value` can be ticker or CIK with example.

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 change feed for a company in a time window, listing specific sources (SEC EDGAR, GDELT→GNews, USPTO) and distinguishing 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?

Explicit example queries ('What's new with X', 'latest on Y') and direction to use entity_profile for static profiles regardless of window, plus fallback behavior for GDELT→GNews.

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 idempotentHint=true and destructiveHint=false. Description adds scope details: 'scoped by your identifier', persistent memory for authenticated users, 24-hour retention for anonymous sessions. This goes beyond annotations to clarify durability and session scoping.

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 core action. No redundant information. Every sentence adds value and is efficiently worded.

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

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

Given two simple parameters and no output schema, the description comprehensively explains what the tool does, when to use it, and what happens to stored data. No gaps remain for an agent to understand correct invocation.

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 standard descriptions. Description enhances by providing example formats for key (e.g., 'subject_property', 'target_ticker') and value ('any text — findings, addresses, preferences, notes'), clarifying the expected usage pattern.

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

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

Description clearly states the tool's purpose: 'Save data the agent will need to reuse later' and provides concrete examples like 'a resolved ticker, a target address, a user preference'. It distinguishes from sibling tools recall and forget by referencing them for retrieval and deletion.

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 mentions pairing with recall and forget. Provides clear context for when to invoke this tool versus 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, openWorldHint, idempotentHint, and destructiveHint as safe. The description adds value by noting internal cascading through multiple lookups and detailing output structures per entity type (ticker, CIK, RxCUI, etc.), 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 front-loaded with example queries and a clear purpose statement, followed by structured details per entity type. It is slightly lengthy but every sentence provides essential information. Minor redundancy could be trimmed, but overall 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 absence of an output schema, the description fully explains what each call returns (including citation URIs) and notes the internal cascading behavior. It covers the prerequisite of having a name and positions the tool within the workflow of needing IDs for other tools, making it self-sufficient for an AI 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 description coverage is 100%, but the description enriches parameter meaning significantly: for 'type' it lists supported values with context, for 'value' it specifies acceptable input forms per type (ticker, CIK, name for company; brand/generic for drug) and adds 'auto-disambiguated' behavior for company input. This far exceeds basic 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 immediately uses example queries ('What's the ticker for…') to convey the tool's purpose, explicitly states it resolves names to canonical identifiers, and lists supported entity types with specific outputs. It clearly distinguishes the tool's role as an ID resolver without relying on the name alone.

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

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

The description advises 'Use FIRST whenever you have a name but need an ID,' providing a clear primary use case. It does not explicitly mention when to avoid the tool or suggest alternatives, but the context of sibling tools like 'entity_profile' might be inferred. The guidance is strong but could be more comprehensive.

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 read-only, idempotent, non-destructive. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density per entity, which is valuable behavioral context beyond annotations.

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

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

Two concise sentences with front-loaded purpose. No unnecessary words; every sentence contributes meaning.

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

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

The description fully explains the tool's operation: it probes entities, ranks them, and returns specific metrics. Despite lacking an output schema, the description sufficiently details the return structure and usage context.

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

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

Schema coverage is 100%. The description adds semantic value by noting that the first entity is treated as the 'subject' for narrative and that omitting models defaults to workers-ai. This supplements 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 compares AI visibility across multiple entities side-by-side. It specifies the verb 'Compare' and the resource 'AI presence' for multiple entities, distinguishing it from single-entity 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?

The description explicitly says 'Useful for competitive AI-marketing audits' and implies when to use. It does not explicitly state when not to use or directly name alternatives, but the context signals and sibling list provide some 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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), description adds that it fans out to external services, handles partial failures gracefully, and lists timeout behavior for bundlephobia. Adds significant context.

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

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

Description is thorough but slightly long; however, every sentence earns its place with specific details. Well front-loaded with primary purpose.

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

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

Despite no output schema, description specifies return fields, per-advisory details, links, and alternative versions. Covers partial failures and fallback behavior. Complete for agent understanding.

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

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

Schema coverage is 100%, but description adds meaning: explains scoped package acceptance for 'package' and default behavior for 'version'. Goes 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 is a composite check for adding npm packages, listing specific checks (deps.dev, bundlephobia) and output fields. Distinguishes from sibling tools by focusing on dependency 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?

Explicitly gives use cases ('is X safe / popular / small', 'what does adding lodash cost me') and limitations (NPM only, fallback for other ecosystems). 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 already declare safe, read-only, idempotent. Description adds rich behavioral details: truncation at 200K chars with flag, BGE embeddings, cosine similarity, overlapping windows, offset returns. Exceeds annotation coverage.

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 efficiently cover purpose, usage, pairing, and technical details. Front-loaded with core function, no unnecessary words.

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

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

No output schema, but description clearly states return values (passages with offsets and scores). Covers truncation behavior, embedding model, and windowing. Complete for tool 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 covers 100% of parameters. Description adds meaningful context: character cap for 'text', default and range for 'limit', and illustrative examples for 'query'. Adds value beyond schema.

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

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

Clearly states semantic search inside a record and when to use (record too big for prompt). However, does not explicitly distinguish from all sibling tools, only mentions one 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 clear usage context ('Use when the record is too big to cram into the prompt') and a specific pairing (ask_pipeworx_grounded). No explicit exclusions, but the guidance is strong.

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

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

The description adds significant behavioral details beyond annotations: requires OAuth, returns subscription id, explains delivery channels (always-on feed, email, sms with verification and cap, webhook with HMAC and auto-disable after 10 failures). This is rich context not captured by readOnlyHint or idempotentHint annotations.

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

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

The description is well-structured and front-loaded with the core action and return, followed by prerequisites, supported types, and delivery options. Every sentence adds value without redundancy, making it informative yet concise.

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

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

Given the complexity of the tool (3 parameters, nested objects, multiple types), the description is comprehensive. It covers all supported types and delivery channels, includes constraints (OAuth, phone verification, SMS cap, webhook auto-disable), and explains the always-on feed. No output schema is needed as the return is a simple subscription id, which is mentioned.

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

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

Although schema coverage is 100%, the description adds meaning by providing concrete examples for each type and parameter (e.g., sec_8k items, polymarket_edge topic, fred_series series_id). It also explains the behavior of delivery channels, including constraints like phone verification and webhook signing, which enhance 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 the tool creates a proactive monitoring subscription to a live-data event stream. It specifies the verb 'Create' and the resource 'subscription', and distinguishes itself from siblings like list_subscriptions and unsubscribe by its purpose.

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

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

The description provides clear context by stating the prerequisite of a Pipeworx OAuth account and mentions that anonymous/BYO cannot persist subscriptions. However, it does not explicitly define when not to use this tool or directly compare to alternatives, though the sibling names implicitly guide usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds useful context: it returns drawn-from-live-catalog examples, with optional topic filtering. It does not contradict annotations and provides extra behavioral detail about the output's structure and source.

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 long but front-loaded with common user phrases. Every sentence serves a purpose: examples, purpose, parameter details. Could be slightly tighter but overall well-structured.

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

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

Given the low complexity (1 optional param, no output schema, rich annotations), the description fully covers what the tool returns, when to use it, and how to use it. It provides complete guidance for both first-time and focused 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% with one parameter described. The description adds value by listing explicit topic options (finance, pharma, etc.) and explaining the effect of omitting vs. passing the argument, going beyond the schema's minimal description.

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

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

The description clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions. It uses specific verbs ('returns') and resource ('example questions with exact tool + argument shape'), distinguishing it from siblings like ask_pipeworx or discover_tools.

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

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

The description explicitly advises 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. This provides clear when-to-use guidance and implies when not to use (when already familiar).

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 beyond annotations: explains soft deletion ('row is deactivated, not deleted') and that historical events stay available, aligning with destructiveHint=false. Ownership enforcement is also detailed. 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?

Two concise sentences: first defines purpose, second explains constraints and effects. No unnecessary words, 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?

For a simple one-parameter tool, the description covers purpose, ownership constraint, side effect (soft delete), and relation to recent_alerts. Adequate for selection and invocation.

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

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

Schema coverage is 100% with parameter id described. Description does not add extra parameter details beyond what schema already provides. 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?

Clearly states 'Cancel a subscription by id', specifying the verb (cancel) and resource (subscription). Distinguishes from sibling tools like subscribe and list_subscriptions. Also adds nuance about deactivation vs deletion.

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 ownership enforcement ('you can only cancel your own subscriptions'), giving a clear condition for use. Mentions that historical events remain via recent_alerts, providing context for post-use behavior. Could be more explicit about when not to use, but overall strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context: it explains the return format (verdict, structured form, actual value with pipeworx:// citation, percent delta), mentions the v1 scope and data sources, and notes that it replaces multiple sequential calls. No contradictions with annotations.

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

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

The description is moderately long but well-structured: it opens with natural-language triggers, then states the core purpose, followed by scope details and return format. Every sentence contributes value, though a slight tightening (e.g., merging the first two sentences) could improve conciseness without losing information.

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

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

The description fully covers what the tool does, when to use it, what types of claims it handles, and what it returns. Despite lacking an output schema, the return format is clearly described. Given the single required parameter and high schema coverage, the description is sufficient for an agent 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?

Schema coverage is 100%, so the baseline is 3. The description enhances the single 'claim' parameter by providing concrete examples ('Apple's FY2024 revenue was $400 billion') and clarifying the acceptable claim types (company-financial). This goes beyond the schema's generic description, adding meaningful guidance for correct usage.

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

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

The description uses vivid natural-language examples ('Is it true that…', 'fact check', 'verify the claim that…') and explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It further specifies scope (company-financial claims via SEC EDGAR + XBRL), distinguishing it from sibling tools like bet_research or 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?

The description clearly says when to use the tool ('when the agent needs to check factual correctness') and notes it replaces 4–6 sequential calls, implying efficiency. However, it does not explicitly state when not to use it (e.g., non-financial claims), which would strengthen the guidance.

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