Openfigi

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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive nature. The description adds behavioral context: probes multiple models, returns per-model scores/confidence/signals/raw_response, and notes that Anthropic probing requires a BYO API key. This enhances transparency beyond the annotations.

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

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

Description is a single paragraph of 4 sentences, front-loaded with the main purpose. It is concise and covers key points without unnecessary detail. Could be slightly more structured, 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?

Despite no output schema, the description outlines the return structure (per-model fields + combined view). It covers the tool's purpose, usage, parameters, and output format. For a fairly complex tool with 4 parameters, this is adequately 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%, so baseline is 3. The description adds meaningful context: explains the default model (workers-ai), clarifies that _apiKey is only needed for anthropic, and states that 'context' helps disambiguate. This provides additional guidance 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?

Description clearly states the tool's purpose: probing LLMs for knowledge about entities and scoring visibility (0-100). It uses specific verbs like 'probe' and 'score', and distinguishes from sibling tools by focusing on AI visibility audits, which is unique among tools like 'deep_research' or 'bet_research'.

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

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

Description provides clear usage contexts: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and the optional API key for Anthropic, guiding when to use which models. However, it does not explicitly mention when not to use this tool or name alternative 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 provide readOnlyHint, openWorldHint, idempotentHint. The description adds context: routes to vast tool network, fills arguments, returns structured answer with stable citation URIs. No contradiction. Adds value beyond annotations.

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

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

The description is front-loaded with a clear directive, then provides examples and usage guidelines. It is comprehensive but not verbose. Could be slightly more structured (e.g., bullet points for alternatives), but it remains readable and efficient.

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

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

For a router tool with one effective parameter, the description fully covers the scope of questions, alternatives, and examples. It also hints at the return format (structured answer with citations). No missing necessary context given the 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 description coverage is 100% with one main parameter 'question' and many aliases. The description adds example questions, which provide concrete semantics beyond the schema. Baseline 3 due to high coverage, but the examples earn an extra point.

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 routes factual questions to a large set of authoritative sources with citations, and distinguishes itself from sibling tools like ask_pipeworx_grounded and deep_research. It uses specific verbs like 'routes' and 'returns' and specifies the resource as a system of 5,245 tools across 1369 sources.

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 this tool (for factual questions, prefer over web search) and when to use alternatives: ask_pipeworx_grounded for hallucination-resistant single answer, deep_research for broad/multi-part questions. Provides many examples of appropriate queries.

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

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

Annotations already cover readOnlyHint, idempotentHint, destructiveHint, and openWorldHint. The description adds context about the experimental edge, candidate testing history, and that it's a full working router with no fallback, enhancing transparency.

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

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

Description is somewhat lengthy but each sentence adds value, clearly explaining the beta nature, current state, and usage. Could be slightly more concise, but 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?

Fully explains the tool's purpose, current equivalence to ask_pipeworx, experimental rationale, and fallback behavior. No output schema, but the description covers all necessary contextual aspects.

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 described in the schema. The description mentions 'same arguments' but does not add new semantic information beyond what the schema provides.

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

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

Clearly identifies as a beta version of ask_pipeworx with identical functionality, distinguishing it from siblings like ask_pipeworx and ask_pipeworx_grounded. States it's a universal router with the same tools and arguments.

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 to use it like ask_pipeworx when wanting the newest routing, and explains that results are compared against the stable router. Mentions that currently no candidate is active, so it matches ask_pipeworx.

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: describes the extraction mechanism (only from tool result), the refusal reasons (not_in_source, no_tool_match, etc.), and the extra LLM call cost, all consistent with readOnlyHint and idempotentHint.

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

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

The description is well-structured with a front-loaded purpose, detailed behavior, and usage guidance. Each sentence is informative, though slightly verbose for a short definition. Still, no unnecessary fluff.

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

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

Given no output schema, the description fully explains the return format (including fields like evidence, confidence, refusal_reason) and refusal reasons. It also covers cost and comparison with sibling, making it complete for high-stakes use.

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

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

All 6 parameters are aliases for 'question', and the schema already describes them. The description adds 'Your question in natural language' and lists aliases, which is adequate but does not deeply enrich beyond schema coverage.

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

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

The description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' that extracts answers only from tool results, distinguishing it from the sibling ask_pipeworx by emphasizing groundedness.

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 (answers that will be quoted, cited, or acted on; financial, legal, medical, public statements) and when not to (prefer ask_pipeworx for casual lookups), and notes the extra cost.

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

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

The description adds extensive behavioral context beyond annotations: market resolution, classification, fan-out logic, response shapes, resolver contract, parent event extraction, news fields safety, closed market handling, wide spread warnings, and cancellation rule parsing. Annotations declare read-only, open-world, idempotent, non-destructive, which the description complements without contradiction.

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

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

The description is very long and detailed, but well-structured with capitalized sections like RESPONSE SHAPES, RESOLVER CONTRACT, etc. While every sentence adds value, the length slightly reduces conciseness, making it less ideal for quick scanning.

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

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

Given the tool's complexity and lack of output schema, the description thoroughly covers response shapes, edge cases (low-confidence, closed markets, wide spreads), safety mechanisms, and cancellation rules. It provides a complete picture for agent 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 descriptions for all 3 parameters. The tool description adds meaning by explaining the effect of depth (quick vs thorough) and include_raw (recommended false) with rationale, and provides examples for market parameter, going 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 researches a Polymarket bet by pulling Pipeworx data. It specifies the verb 'research' and the resource 'Polymarket bet', with explicit examples like 'should I bet on X'. It distinguishes from siblings by emphasizing one-call fan-out and evidence packet return.

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 for should I bet on X, etc.' and provides category-specific fan-out examples. It does not explicitly state when not to use or compare to siblings, but the usage context is well-defined and 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 confirm read-only, idempotent behavior. The description adds detailed behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific metrics pulled, handling of fiscal year offsets, result sorting, and citation URIs. No contradictions with annotations.

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

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

The description is relatively long but front-loaded with example queries and core purpose. Every sentence adds value, though some redundancy (e.g., 'ALWAYS PREFER' could be integrated). Still well-structured and informative.

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

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

Covers return format (paired data + citation URIs), type-specific details, and sorting. Missing error handling or pagination, but acceptable for this tool type. No output schema, so description adequately compensates.

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

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

Schema has 100% coverage with descriptions, but the description adds significant meaning: explains data sources per type, financial metrics for companies, adverse event counts for drugs, and sorting behavior. This goes beyond what the schema provides.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs with examples of trigger phrases. It explicitly distinguishes from sequential single-entity lookups, making the purpose unambiguous.

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

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

Provides explicit examples of when to use the tool (comparison queries) and states preference over sequential lookups. However, it does not explicitly list when not to use it or limitations (e.g., non-financial metrics for companies).

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 account requirement, paid plan for 'thorough', parallel execution, return format with gaps and contradictions, semantic excerpting, and expected response times. Annotations already declare readOnlyHint and idempotentHint, and description 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?

The description is detailed but every sentence earns its place, covering usage, behavior, and parameters. It is front-loaded with critical account info. Slightly verbose but well-organized 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?

Given no output schema, the description fully explains the return format (findings packet with evidence, gaps, contradictions) and time expectations. No additional context needed 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%, but description elaborates on the 'depth' parameter enum values with behavioral implications and explains 'question' as natural language for broad/multi-part queries, adding 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?

The description clearly states the tool performs grounded multi-source research across Pipeworx's structured data sources, decomposes questions into facets, and returns a findings packet. It distinguishes from siblings like ask_pipeworx by explicitly contrasting use cases.

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

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

Provides explicit guidance on when to use deep_research (broad/multi-part questions over structured data) vs ask_pipeworx (single lookups, breaking news), and notes account requirements with fallback.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool returns top-N most relevant tools with names, descriptions, and full input schemas (with examples), ready to call directly. It also implicitly confirms no side effects. No contradictions; the description enriches the behavioral understanding.

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) and well-structured. It starts with the core purpose, then provides usage guidance with examples, and ends with a key note about calling it first. Every sentence adds value, with 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 there is no output schema, the description adequately explains what is returned (tool names, descriptions, schemas) and the format (ready to call). It covers input parameters, usage context, and output format. For a tool of this complexity (discovery tool with simple inputs and clear output), the description 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%, so baseline is 3. The description adds significant value by listing aliases, explaining the 'limit' parameter with defaults/max, and providing two concrete examples. This goes beyond the schema's property descriptions, which are already clear, by giving usage context.

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

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

The description clearly states the tool's purpose: to find tools by describing data or task. It lists numerous example domains (SEC filings, FDA drugs, etc.) and explicitly distinguishes itself from siblings by saying to call it first when wanting to see the option set, not just one answer. This is a specific verb+resource with clear differentiation.

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

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

The description provides explicit guidance on when to use this tool: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It implies when not to use it (when you already know the tool or want a single answer), though it doesn't list exclusions explicitly.

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 details data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), specific outputs, and notes the patents endpoint may sunset. Annotations already declare readOnly, idempotent, non-destructive, and open-world, and the description adds rich context without contradiction.

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

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

The description is detailed but front-loads the core purpose. While somewhat dense, it efficiently communicates many constraints and behaviors essential for correct 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?

Without an output schema, the description thoroughly explains return fields and structure, covers input constraints, and mentions known limitations (patents sunset). It's complete for a read-only data retrieval tool.

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

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

Schema covers both parameters with 100% coverage. Description adds value by reiterating supported formats (ticker or zero-padded CIK) and advising against using names, plus noting type enum 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' with example queries. It distinguishes from sibling tools like resolve_entity and compares favorably to chaining single-source lookups.

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

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

Explicitly says to prefer this over chaining single-pack SEC/XBRL/news lookups for holistic views, and instructs to use resolve_entity first if only a name is available instead of ticker/CIK.

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 value by stating that the tool returns matching FIGIs, security descriptions, and market sector, which is useful context beyond the annotations.

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

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

The description is a single, well-structured sentence that conveys all necessary information without redundancy. It front-loads the action and parameters.

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 an output schema (which would detail the return format) and annotations, the description is complete enough. It covers inputs, optional parameters, and the nature of the 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?

With 0% schema description coverage, the description fully compensates by explaining that idType and value are the identifier type and value, and that exchCode and currency are optional narrowing parameters. This adds meaning beyond the raw 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 filters OpenFIGI instruments by identifier type and value, with optional narrowing by exchange code or currency. It distinguishes itself from sibling tools by specifying the domain (OpenFIGI) and the specific use case.

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

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

The description explicitly says when to use the tool: to filter by identifier type/value. However, it does not mention when not to use it or provide alternatives from the sibling list, leaving some ambiguity.

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 context that it deletes 'previously stored memory', but does not elaborate on consequences or error cases 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 action, no unnecessary words. Every sentence is informative and 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 tool with one parameter, full schema coverage, and annotations covering behavioral traits, the description is sufficient. Could mention idempotency explicitly, but not required.

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 for the 'key' parameter. Description merely repeats 'by key' without adding new meaning, so baseline score applies.

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

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

Description explicitly states 'Delete a previously stored memory by key', clearly specifying verb and resource. Differentiates from siblings 'remember' and 'recall' 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?

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also mentions pairing with 'remember' and 'recall', providing guidance on 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, idempotent, non-destructive behavior. The description adds context: fetches the page, extracts title/description/key links, and emits standard markdown. 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 purpose, then process, then use cases. No redundant information. Every sentence earns its place.

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

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

Given the tool's simplicity (2 params, no output schema), the description fully covers what the tool does, how it works, and what output to expect. 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% (both parameters documented clearly). The description does not add additional semantic meaning beyond the schema, which is already sufficient. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for any URL, with specific use cases listed. It distinguishes from sibling tools by focusing on a unique output format and 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 explicit use cases (e.g., getting a client's site indexed, drafting for own project, auditing a competitor). However, it does not explicitly state when not to use this tool or compare it to alternatives like ai_visibility_check.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns specific fields and defaults to active subscriptions. It does not contradict annotations and adds value by listing return fields and default behavior.

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

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

Two sentences: first for purpose and return fields, second for usage. 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?

For a simple tool with one optional param, rich annotations, and no output schema, the description is sufficient. It covers purpose, return values, and context of use.

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

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

Schema coverage is 100% with one optional parameter. The description adds context: 'active subscriptions' implies default false for include_inactive, and the use case supplements meaning beyond the schema 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 states 'List the caller's active subscriptions' with specific verb and resource. It lists return fields (id, type, params, created_at, last_fired_at, fire_count), clearly distinguishing 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?

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use (before subscribing/unsubscribing) and implicitly contrasts with subscribe and unsubscribe.

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

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds no additional behavioral context beyond this, so it meets the baseline but does not enhance transparency.

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

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

The description is a single sentence of 7 words, fully front-loaded with the tool's purpose. Every word carries meaning, and there is no redundant or extraneous content.

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

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

Given the tool's simplicity (one parameter, clear input/output), annotations cover safety, and an output schema exists, the description is mostly complete. It could mention batch processing or error conditions, but the current level is sufficient for a well-annotated 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 has 100% coverage with a description for the sole parameter 'jobs'. The tool description does not add any further meaning or detail about the parameters beyond what the schema provides, so the baseline score of 3 applies.

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

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

The description clearly states the action ('Map'), the resource ('batch of instrument-id queries'), and the output ('to FIGIs'). It is specific and distinct from sibling tools, none of which perform identifier mapping.

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

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

The description does not provide explicit guidance on when to use this tool over alternatives. Usage is implied by the tool's purpose, but no when-not or alternative tools are mentioned, which would help in distinguishing from many sibling tools.

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

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

The description adds behavioral context beyond annotations: rate-limited to 5 per identifier per day, free, and not counting against tool-call quota. It also mentions the team reads digests daily and signal 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?

The description is concise and well-structured: first sentence states purpose, then gives usage conditions, then specific instructions. Every sentence is informative and earns its place without unnecessary fluff.

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

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

Given the tool's simplicity, the description fully covers purpose, usage, behavioral constraints, and parameter semantics. No output schema is needed for a feedback tool. The description is complete for an AI agent to select and invoke correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. The tool description adds value by clarifying that the message should be specific and not include end-user prompts, and by explaining the meaning of each feedback type beyond the schema's enum 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 (broken, missing, needs to exist) and distinguishes between types (bug, feature, data_gap, praise). It differentiates from sibling tools by being a feedback submission tool, not a query or action tool.

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

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

The description explicitly specifies when to use each feedback type: bug for wrong data, feature/data_gap for missing tools, praise for positive experiences. It also instructs to describe issues in terms of Pipeworx tools/packs and not to paste end-user prompts, providing clear usage boundaries.

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

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds value by revealing the data source (CF analytics-engine), caching behavior (5min-1h), and that no PII is collected. 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 well-structured paragraphs with a front-loaded first sentence. Every sentence adds value, though slightly verbose. Could be tightened slightly, 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?

With no output schema, the description adequately explains return values (top tools, top packs, total call volume). It covers inputs and behavioral notes, though omitting output format details (e.g., structure) prevents a perfect score.

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 enum description. The tool description reinforces window semantics (shorter for hot, longer for steady) and adds caching details, providing extra context beyond the schema.

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

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

The description clearly states 'Returns the top tools, top packs, and total call volume' with a specific verb and resource. It distinguishes itself from siblings like 'discover_tools' by focusing on trending data derived from AI agent usage.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning use cases) and explains window trade-offs. Provides clear guidance on when to use this tool vs. alternatives.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds substantial behavioral context: explains monotonicity checks, partition_sum checks, semantic anchor, partition filter, fill check, and response structure. No contradictions with annotations.

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

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

Description is relatively long but well-structured with key information front-loaded. Every sentence adds value, though denser than necessary for a quick read. Minor redundancy (e.g., explaining event and topic in both description and schema) 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 high complexity (two modes, multiple internal checks), 100% schema coverage, and no output schema, the description is remarkably complete. It covers edge cases (e.g., placeholder filters, fill check conditions) and clearly describes the response structure and terms like 'suggested_trade' and 'realizable_edge_pp'.

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 significant meaning beyond the schema: explains that event mode uses slugs or URLs, topic mode uses seed questions, and details internal logic for each parameter (e.g., partition_check, cross-event scanning, Jaccard similarity).

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

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

Clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Describes three invocation modes (no args, event, topic) and what they do. Although it doesn't explicitly distinguish from sibling tools, the purpose is specific and unambiguous.

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

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

Provides explicit guidance on when to use each mode (e.g., 'recommended for a specific market' for event mode). Implies when not to use by recommending alternatives like a trending scan, but does not explicitly exclude sibling tools or state when NOT to use the tool at all.

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

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

The description extensively discloses behavioral traits beyond the annotations: detailed model methodologies (lognormal barrier from FRED, GDELT volume ratio, partition overround with sport-specific bias correction), filtering logic (placeholder slugs, concentrated longshot gates), edge calculation (net of slippage, Kelly capped at 0.25), diagnostics, caching (1h KV). Annotations already indicate read-only, idempotent, and non-destructive, and the description complements without contradiction.

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

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

The description is structured with a clear purpose statement and logical sections (model families, knobs, output layout). However, it is overly verbose, especially in the model family details and diagnostics explanation. Some sentences could be shortened without losing meaning, making it less efficient for an LLM to parse quickly.

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

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

Given the tool's complexity (9 parameters, no output schema, rich model families), the description provides comprehensive context: input knobs with operational guidance, output structure with segments and diagnostics, edge calculation details, caching policy, and explanations for empty segments. This enables an agent to use the tool effectively without additional information.

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?

All 9 parameters have schema descriptions (100% coverage), so the baseline is 3. The description adds value by grouping parameters into tradeable-edge knobs (max_spread_pp, min_liquidity, min_partition_leg_kelly) and explaining their interaction (e.g., min_kelly does not filter partition arbs, but min_partition_leg_kelly does). This operational context elevates the score above baseline.

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

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

The description clearly states the tool's purpose: scanning Polymarket markets for mispricing opportunities where Pipeworx data disagrees with market price. It specifies three model families and mentions it's for 'what should I bet on today'. However, it does not explicitly differentiate this tool from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, which have overlapping functionality.

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

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

The description provides clear usage context: it is designed for discovering betting opportunities without paging through hundreds of markets. It explains tradeable-edge knobs and caching behavior. However, it does not specify when not to use this tool or mention alternatives, leaving the agent to infer exclusivity from 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?

Beyond annotations (readOnlyHint, etc.), the description adds crucial behavioral details: uses latest snapshot, tracks time-series, provides trend/decay, handles expired opportunities, and notes limits like 60-day TTL and snapshot gaps.

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-loaded with purpose, and each sentence adds value. It efficiently covers response fields and limitations without being verbose.

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

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

Despite no output schema, the description comprehensively explains the response (tracked[], expired[], snapshot_dates[]) and limitations, covering edge cases like gaps and TTL. It's fully 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 default values and clarifies the window options and response structure, going beyond the schema's parameter descriptions.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' from snapshots. It answers specific questions about edge age and trend, distinguishing it from sibling tools like polymarket_edges (raw 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?

The description explicitly says when to use the tool (to understand how long an edge has existed and if it's shrinking) and implies alternatives via sibling context. It could be more explicit about 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?

The description adds substantial behavioral context beyond annotations: it walks the ladder, returns specific fields (top_of_book, vwap_fill_price, etc.), and highlights the risk of forced directional risk in basket mode. 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 with clear sections (OVERVIEW, REQUIRES, mode details) and front-loaded with the main purpose. While slightly long, every sentence adds value and there is no redundancy.

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

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

Despite no output schema, the description lists all expected return fields for both modes, covers parameter behavior and edge cases, and explains the risks of partial fills. It provides a complete understanding of what the tool does and returns.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds operational context for size_usd (max spend on buys, target proceeds on sells) and explains default side behavior in basket mode. This provides meaningful interpretation 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 defines the tool as a realizable-vs-theoretical edge check against live order-book depth, distinguishing single-market and basket modes. It specifies exactly what it computes (top_of_book, vwap_fill_price, slippage_pp, etc.) and differentiates itself 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?

Explicitly states when to use this tool: before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. Explains the reasoning (theoretical overround on thin books is not capturable, partial basket fills convert arb into directional position) and warns against misuse.

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

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description details safety fields (compatibility_warning cases, temporal_alignment, skipped_cross_type/subtype), and explains when spreads are meaningful. No contradictions with annotations.

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

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

The description is dense but well-structured: core purpose first, then modes, then response format, then safety details. Every sentence adds value with no redundancy. Appropriate length for the tool's complexity.

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

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

Despite having no output schema, the description thoroughly explains the response (leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters). All relevant behavioral details are covered.

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 describes all parameters with 100% coverage. The description adds context about mode overriding, and provides examples, slightly exceeding the schema's baseline. Baseline 3 is elevated due to added 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 computes cross-venue spread between Kalshi and Polymarket for the same question, explicitly distinguishes from sibling tools like polymarket_arbitrage, and details two modes (topic shortcuts and explicit pairs).

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 each mode, provides examples, and warns that most pre-mapped topics currently return compatibility warnings, setting accurate expectations. Lacks explicit exclusion of alternative tools but provides sufficient context.

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

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

Annotations already indicate readOnly/destructive hints; description adds scoping to identifier and pairing with remember/forget, enhancing transparency without contradiction.

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

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

Three sentences, front-loaded with main action, 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?

For a simple tool with one optional parameter and no output schema, description adequately covers behavior and pairing. Could mention return behavior for missing key, but still complete enough.

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 100% with parameter description; description reinforces that omitting key lists all keys. Adds minimal new meaning but still useful.

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 verb 'retrieve' and resource 'memory values saved via remember', with examples (target ticker, address). Distinguishes from sibling tools remember and forget.

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

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

Explicitly says when to use (look up context stored earlier) and when to omit key (list all). No explicit when-not or alternatives, but context is clear.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds transparency about the mark_read parameter's side effect (flagging events as read so subsequent calls show newer ones) and confirms the data source is a persisted feed. No contradictions with annotations.

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

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

The description is front-loaded with the core purpose and key returns, and is structured logically (purpose, returns, filtering, mark_read, alternative access). It is concise at 5 sentences, but could be slightly tighter.

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

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

Given 5 parameters, no output schema, and annotations present, the description covers returned fields, parameter usage, and alternative access. It lacks detailed response structure but is sufficient for a read tool with good annotations and parameter docs.

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 meaningful examples like 'sec_8k' for the type parameter and clarifies the effect of mark_read ('so the next call only shows newer ones'). It also reinforces the ISO timestamp format for since. This goes beyond the schema descriptions.

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

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

The description clearly states the tool pulls fired events from the user's subscription feed and distinguishes it from siblings like list_subscriptions by focusing on event retrieval. It specifies key returned fields (source, citation_uri, raw payload), making the purpose unambiguous.

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

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

The description provides context on when to use the tool (pulling recent alerts) and notes that polling works fine. It also mentions an alternative REST endpoint for scripts/dashboards, but does not explicitly state when not to use the tool or compare with other tools like search or filter.

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 significant behavioral context: it fans out to multiple sources, explains GDELT→GNews fallback, notes USPTO soft-fail, and describes the return structure (changes grouped by source, total_changes, pipeworx:// URIs). No contradictions with annotations.

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

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

The description is well-structured, starting with example queries, then explaining sources, parameters, and return format. It is informative but slightly lengthy; however, every sentence adds value. A minor improvement could be tightening slightly, but overall it is above average.

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 covers the return structure adequately. It addresses purpose, parameters, behavioral details, limitations (USPTO soft-fail), and provides an alternative tool. For a tool with 3 required parameters and clear behavior, this is very 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 adds useful context beyond the schema: examples of relative time formats for 'since' ('7d', '30d', '3m', '1y'), a recommendation for monitoring ('30d' or '1m'), and clarification that 'value' can be ticker or CIK. This justifies a 4 rather than 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 that the tool returns a change feed for a company in a specified time window, listing example queries and summarizing sources (SEC EDGAR, GDELT/GNews, USPTO). It also distinguishes from the sibling tool entity_profile, making the purpose unambiguous.

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

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

The description provides explicit guidance on when to use this tool vs. the alternative entity_profile for static profiles. It also includes parameter tips like 'Use "30d" or "1m" for typical monitoring' and explains the two acceptable formats for 'value'.

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 value beyond annotations by disclosing storage as key-value pairs, scoping by identifier, and persistence differences (persistent for authenticated users, 24-hour for anonymous). This complements the idempotentHint annotation and provides essential behavioral context for an AI agent.

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 three sentences, front-loaded with purpose, then usage, then technical details. Every sentence adds value with no redundancy or fluff.

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

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

Given the simple parameters and lack of output schema, the description is nearly complete. It covers purpose, usage, behavior, and pairing with sibling tools. Minor omissions (e.g., behavior on duplicate keys) are acceptable for a simple tool.

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

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

Schema coverage is 100% with descriptions for both 'key' and 'value'. The description adds examples of keys and mentions 'any text' for values, but this does not significantly exceed the schema's own descriptions. 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 tool's purpose: 'Save data the agent will need to reuse later'. It uses specific verbs ('save', 'store') and resources ('data', 'key-value pair'). It distinguishes itself from sibling tools like recall and forget by describing its role in a trio of memory operations.

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: 'Use when you discover something worth carrying forward' with concrete examples. It does not explicitly state when not to use, but the positive guidance is strong and clear.

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

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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds value by revealing internal cascading lookups and stating it replaces 2-3 manual calls, providing deeper behavioral context without contradiction.

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

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

The description is well-structured with examples and clear sections, but it is slightly lengthy. However, every sentence adds value, and there is no fluff. A slightly tighter structure could improve readability without losing information.

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

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

Despite no output schema, the description fully explains return formats for both types, including citation URIs. It covers all necessary context for an agent to expect correct output, making it self-contained and 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%, but the description significantly enriches meaning by detailing the return values per type (e.g., ticker+CIK+company_name for company) and providing concrete input examples, going far 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 explicitly states the tool resolves user-spoken names to canonical/official identifiers, using examples like 'ticker for...' or 'CIK for...'. It clearly distinguishes from sibling tools like entity_profile or compare_entities, which have different purposes.

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

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

The description provides explicit when-to-use guidance: 'Use FIRST whenever you have a name but need an ID.' It also lists supported types and what each returns, making it easy for an agent to decide 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?

Description reveals that the tool probes each entity with ai_visibility_check internally and returns a ranked list with score, confidence, and signal density. Annotations already indicate safe, idempotent read operation, and description adds context without contradiction.

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

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

Four sentences, front-loaded with main purpose, no filler, each sentence adds distinct information (purpose, mechanism, use case, output).

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?

Describes output format despite no output schema, explains reliance on ai_visibility_check, and covers all parameters. Missing explicit mention of default model behavior, but overall sufficient for a tool of this complexity.

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

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

Schema coverage is 100%, but description adds value by specifying that the first entity in 'entities' is treated as the subject for narrative, and explains the role of 'models' and '_apiKey' in calling external APIs.

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 side-by-side, ranks them, and surfaces most/least recognized. It explicitly mentions using ai_visibility_check, distinguishing it from the sibling tool 'ai_visibility_check' which handles single entities, and from 'compare_entities' which is broader.

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 a concrete use case ('competitive AI-marketing audits') with an example question. Implicitly suggests usage over ai_visibility_check when multiple entities are involved, but does not explicitly state when not to use 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?

Provides rich behavioral details beyond annotations: fans out across two services, partial failures degrade gracefully with sources_failed listing timeouts, bundlephobia first measurement takes 5-30s. All consistent with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false.

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 well-structured: front-loaded with the main purpose, then details each component. Every sentence serves a purpose, though could be slightly tighter. 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?

Despite no output schema, the description fully explains what is returned: summary block fields, per-advisory detail, links, and alternative versions. Also covers edge cases like partial failures and ecosystem scope. Complete for an agent to use effectively.

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

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

Schema coverage is 100% with clear parameter descriptions. Description adds value: scoped packages are accepted, version defaults to latest when omitted. While minor, it provides additional context beyond the schema.

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

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

The description clearly states it's a composite check for evaluating npm packages, encompassing license, advisories, version history, bundle size, and ESM/tree-shake support. It distinguishes itself from sibling tools like scan_competitor_ai_presence or validate_claim by being specifically for npm package evaluation.

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

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

Explicitly tells when to use: for questions like 'is X safe / popular / small' or 'what does adding lodash cost me'. Also states NPM ecosystem only and directs other ecosystems to deps.dev:version directly. Mentions partial failure handling and bundlephobia delay, guiding proper invocation.

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 does not contradict these and adds no additional behavioral context. It is adequate given the annotations, but fails to explain aspects like pagination or result format.

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

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

The description is extremely concise (4 words), but this sacrifices content. It is front-loaded, but does not earn its place as every sentence should; a single phrase is not enough for a tool with 5 parameters.

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 5 parameters, an output schema, and is a search tool, the description is severely incomplete. It fails to explain filtering behavior, optional filters, or the structure of results. Essential context is missing for effective use.

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

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

Schema description coverage is 0%, and the description provides no explanation of any parameter (e.g., query, currency, exchCode). The agent must infer meaning from parameter names alone, which is insufficient, especially for domain-specific fields.

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

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

The description 'Text search across instruments' clearly states the tool's function (search) and targets (instruments). It distinguishes from sibling tools like deep_research or search_within. However, it could be more specific about what constitutes an 'instrument'.

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?

No usage guidelines are provided. There is no indication of when to use this tool over alternatives like deep_research or search_within, nor any prerequisites or constraints.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds crucial details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, a 200K char limit with truncation and flagging, and output format (passages with offsets and similarity scores). This fully informs the agent about behavior beyond the annotations.

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

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

The description is well-structured: first sentence gives purpose, second explains when to use, third mentions pairing, fourth gives technical details. It is front-loaded and clear, though slightly verbose with multiple examples. Each sentence adds value.

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

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

Given there is no output schema, the description explains return values (passages with offsets and scores) and algorithm details. It also covers limits and truncation. Missing error handling for edge cases like empty text, but overall comprehensive for a focused tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description provides query examples and restates the max text length, but adds minimal additional meaning beyond the schema. The examples are helpful but not essential.

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' with specific verb and resource, and distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded. It gives concrete examples (SEC 10-K body, article) that clarify the 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?

Explicitly states 'use when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded. It does not explicitly mention when NOT to use, but the context is clear enough 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 indicate a write operation (readOnlyHint=false) and idempotent (idempotentHint=true). The description adds critical context: subscription creation returns an id, details on delivery channels, rate limits (10/day SMS cap), webhook signing, and auto-disable on failures. No contradiction with annotations.

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

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

The description is front-loaded with the primary purpose and action. It is dense with information but organized logically by types and delivery channels. Slightly lengthy but justified by the complexity of the 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?

Given the tool's complexity (3 params, nested objects, no output schema), the description covers prerequisites, all types, parameter semantics, delivery details, rate limits, account verification, and post-subscription behavior (auto-disable for webhooks). No gaps remain.

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 significantly adds value by providing concrete examples for each subscription type (e.g., sec_8k items, polymarket_edge topic, fred_series series_id) and thoroughly explaining delivery options (email validation, SMS verification, webhook HMAC signing).

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

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

The description uses a specific verb ('create'), names the resource ('proactive monitoring subscription to a live-data event stream'), and states the return value ('new subscription id'). It clearly distinguishes from sibling tools like list_subscriptions 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 specifies prerequisites (Pipeworx OAuth account) and explains when this tool is appropriate (proactive monitoring). It does not explicitly list alternatives or when not to use, but the context is clear enough for most agents.

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. The description adds important behavioral context: it returns example questions with tool+argument shapes drawn from the live catalog, and explains that calling with no arguments gives a full spread while a topic focuses results. There are 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, well-structured paragraph. It front-loads the key purpose with alternative phrasings, then details the output and usage instructions. Every sentence adds value, and there is 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 the tool's simplicity (one optional parameter, no output schema), the description is fully complete. It explains what the tool returns, how to call it, and when to use it. It even mentions that examples are drawn from a live catalog, which is useful context.

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

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

The input schema already documents the optional topic parameter with a description. The description adds value by explaining that omitting the topic gives a full spread and provides concrete examples of topic values (finance, pharma, etc.). This enhances 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 is the onboarding entry point, returning category-bucketed example questions with tool+argument shapes. It distinguishes itself from sibling tools like ask_pipeworx and discover_tools by specifying its role as a first step when the agent does not know what Pipeworx can do.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and gives guidance on using with or without the topic parameter. However, it does not explicitly list alternatives or when not to use other tools, though the context implies that for actual questions, other tools like ask_pipeworx are appropriate.

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 the annotations: it explains that the row is deactivated (not deleted) and historical events remain available via recent_alerts. This aligns with the idempotentHint and destructiveHint annotations, adding valuable transparency.

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

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

Two sentences, no wasted words. Front-loaded with the core action, followed by essential behavioral details. Highly efficient.

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

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

The description fully covers the tool's behavior given its simplicity: one required parameter, no output schema, no nested objects. It explains the effect and links to recent_alerts, making it complete.

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

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

Schema coverage is 100% and the description reiterates the id parameter, adding context about its origin ('returned by subscribe') and ownership enforcement, which enriches the schema 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 explicitly states the action ('Cancel a subscription by id'), specifies the resource (subscription), and includes the ownership constraint, clearly distinguishing it from sibling tools like subscribe and list_subscriptions.

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

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

The description clearly indicates when to use the tool ('you can only cancel your own subscriptions'), providing a clear context. It does not explicitly mention when not to use it or name alternatives, but the ownership constraint is a strong guideline.

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 significant behavioral context: the tool returns a verdict (with five possible values), a grounded or structured actual value with a pipeworx:// citation, and reasoning. It also explains the replacement of multiple sequential calls. No contradiction with annotations.

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

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

The description is moderately long but well-structured: it starts with trigger phrases, then states the purpose, details the two pipelines, and concludes with return values. Every sentence provides useful information, 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 complexity (two parameters, no output schema), the description is thorough. It covers return values (verdict, value, citation, reasoning), the dual-pipeline behavior, and the replacement of multiple calls. The annotations provide safety context, and the description fills remaining gaps 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?

The input schema already describes both parameters (claim and tolerance_pct) with clear descriptions. The description adds value by specifying that tolerance_pct overrides implied wording, is capped at 5 by default, and can be set to 1-2 for strict hallucination detection. This extra context helps the agent choose appropriate 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 explicitly states that the tool verifies natural-language factual claims against authoritative sources, with trigger phrases and two distinct pipelines (SEC EDGAR for financials, grounded pipeline for others). It clearly distinguishes itself from the broader set of tools by specifying its exact 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 gives clear guidance on when to use the tool ("whenever the agent needs to check…") and outlines the two pipelines. However, it does not explicitly mention when NOT to use it or name alternative sibling tools for related tasks, which would enhance the guidance.

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