flights

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

Building on annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable context: it discloses that external LLM APIs are probed, the per-model output structure, and cost implications (free default vs. paid Anthropic). It does not mention rate limits or latency, but is sufficiently transparent.

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, front-loading the core function in the first sentence, followed by model details and then use cases. Every sentence adds value without redundancy.

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

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

Given no output schema, the description adequately explains the return value structure (per-model {score, confidence, signals, raw_response} + combined view). It also covers all four parameters through the schema and adds context about use cases and model selection. The description is complete for the tool's complexity.

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

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

The input schema already has 100% description coverage for parameters. The description adds beyond that by specifying the default model (Workers AI Llama-3.3-70b), the requirement for _apiKey when using Anthropic, and the purpose of the 'context' parameter. This adds meaningful guidance for parameter usage.

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

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

The description clearly states the verb 'probe' and the resource 'LLMs', defines the purpose (score visibility 0-100 per model for a business/brand/product/topic), and specifies the output format. It distinguishes itself from siblings by focusing on visibility scoring across multiple models, though not explicitly named.

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 guidance on when to use the tool (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains model options (default free vs. Anthropic with BYO key). However, it does not explicitly state when not to use it or contrast with specific siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by explaining the routing mechanism (3,775 tools) and the structured output with citation URIs, which is beyond annotation defaults.

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

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

The description is relatively long but front-loaded with the key directive 'PREFER OVER WEB SEARCH'. Every sentence adds value, listing domains and examples. Could be slightly more concise, but structure is good.

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 (single required parameter, no output schema, rich annotations), the description covers purpose, domain, usage, and behavioral details thoroughly, leaving no significant gaps.

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

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

Schema coverage is 100% with all aliases described. The description does not add significant parameter semantics beyond what is in the schema, but it provides example questions that illustrate usage, maintaining adequate clarity.

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

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

The description clearly states the tool answers factual questions by routing to verified sources, lists many specific domains (SEC filings, FDA drug data, etc.), and differentiates itself from siblings by explicitly preferring over web search. The verb 'ask' and resource 'Pipeworx' are well-defined.

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

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

The description gives explicit when-to-use guidance (e.g., for authoritative structured data, 'what is', 'look up') and provides examples. It lacks explicit when-not-to-use scenarios but strongly implies when to prefer it over 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 adds significant behavioral context beyond annotations: explains same routing as ask_pipeworx, extraction process using only tool results, and detailed success/refusal return formats. Annotations already indicate readOnly, openWorld, and idempotent, but description deepens 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?

Concise, front-loaded summary with no wasted words. Every sentence serves a clear purpose: purpose, mechanism, use case, and return format. Structured effectively.

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 missing output schema, description fully explains return format (answer, evidence, confidence, source, etc.) and refusal reasons. Covers input, behavior, and use case comprehensively for a complex tool.

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

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

Schema already covers all parameters (100% coverage) and documents the question parameter with its aliases. Description does not add new semantic information about parameter usage beyond restating aliases.

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 identifies the tool as a 'hallucination-resistant answer mode for high-stakes reads', explains its routing and extraction process, and distinguishes it from ask_pipeworx by noting cost and 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?

Explicitly states when to use ('when an answer will be quoted, cited, or acted on') and when not to (prefer ask_pipeworx for casual lookups). Also lists explicit refusal reasons, providing clear guidance.

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

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

The description goes far beyond the annotations (readOnlyHint, idempotentHint, openWorldHint, destructiveHint=false) by detailing safety mechanisms (low-confidence short-circuit, closed market handling, wide-spread liquidity), the resolver contract with confidence levels, parent_event extraction, news fallback fields, cancellation rule parsing, and blocking behavior for different resolution paths. 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 comprehensive but lengthy, covering many details in a dense structure. It is front-loaded with the primary purpose, but subsequent sections (classifiers, fan-out examples, response shapes, resolver contract) are all relevant. Every sentence adds value, though a reduction in length could improve scanability without losing critical information.

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

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

Given the tool's complexity (many classifiers, fan-out behaviors, response structures), the description is exceptionally complete. It explains the return shape in detail (result.market, result.analysis, result.evidence, resolver contract, parent_event), covers edge cases (low confidence, closed markets, wide spreads, cancellation rules), and describes fallback mechanisms (news fields, retry_after_sec). No output schema exists, so this thoroughness is essential and well-executed.

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 three parameters described. The description provides examples and context (e.g., depth values, include_raw trade-off) but these mostly align with or slightly extend the schema descriptions. Given the high schema coverage, the description adds marginal value, warranting a baseline score of 3.

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

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

The description starts with a clear verb-resource pair: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and outputs (evidence packet + market-model comparison). The classifiers and fan-out examples further illustrate scope. This distinguishes it from siblings like polymarket_edges (edge scanning) and polymarket_arbitrage.

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

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

Explicit use cases are given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It does not explicitly state when not to use or alternatives, but given the specific use cases and the existence of sibling tools for complementary tasks, the guidance is clear and sufficient.

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

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

The description adds significant behavioral context beyond the annotations: for 'company', it specifies data sources (SEC EDGAR/XBRL), fields (revenue, net income, cash, debt), and handling of off-calendar fiscal years with examples. For 'drug', it lists specific data types (FAERS counts, FDA approvals, trial counts). It also mentions result sorting and citation URIs, providing a comprehensive behavioral picture.

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

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

The description is a single dense paragraph that front-loads the purpose and usage. It contains no redundant sentences, but could be more structured (e.g., separate sections for company vs drug) for easier scanning. 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 has only 2 parameters, no output schema, but rich annotations, the description fully covers both entity types, data sources, sorting behavior, and return format (paired data + citation URIs). It is complete and leaves no critical gaps for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. However, the description adds meaning by detailing what data each 'type' retrieves and clarifying the format of 'values' per entity type with examples, going beyond the schema's enum and array 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 starts with clear verb phrases like 'Compare X and Y', 'X vs Y', and explicitly states 'side-by-side comparison of 2-5 companies or drugs'. It distinguishes itself from sibling tools by noting it replaces 8-15 sequential lookups, making the purpose unambiguous and specific.

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

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

The description gives explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides context for both 'company' and 'drug' types, but does not explicitly mention when not to use it or list alternative tools, which keeps it from a 5.

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

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

The description adds behavioral context beyond annotations: it explains the tool decomposes questions, returns structured findings with verbatim evidence, confidence, source, timestamp, and citations. It notes explicit gaps[] for unanswered facets and states 'never invented.' It also mentions expected duration of 15-60s. Contradicts no 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 front-loads the core purpose and follows with usage, prerequisites, and output details. Every sentence adds value, though some rephrasing could reduce length slightly.

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

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

Given the tool's complexity, the description covers: what it does, how it works, when to use, prerequisites, output format (structured findings packet with evidence), constraints (never invents, explicit gaps), and expected timing. No output schema, so the description adequately covers return value.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by clarifying depth values: 'quick=3, standard=5 (default), thorough=8 (paid plans).' This goes beyond schema descriptions.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research in ONE call.' It specifies the process of decomposing questions into sub-questions and routing them to 3,775 tools across 895 sources in parallel. It also distinguishes itself from the sibling 'ask_pipeworx' by noting that tool is for single lookups.

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

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

Explicit guidance on when to use: 'Use for broad or multi-part questions' and 'use ask_pipeworx for single lookups.' Also mentions prerequisites: Pipeworx account (with signup link) and paid plan for 'thorough' depth.

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

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

Annotations already mark the tool as readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by detailing the return format (top-N tools with names, descriptions, full input schemas) and the convenience of direct invocation. 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 (two sentences) and effectively front-loaded with the core purpose and then expands on features. Every sentence contributes essential information: what it does, when to use it, and what it returns.

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 (6 parameters, no output schema), the description sufficiently explains the input and output. It covers the query parameter with examples, the return structure, and the limit parameter. It could mention pagination or error handling, but overall it is comprehensive enough for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing numerous domain examples (e.g., 'analyze housing market trends') that illustrate the type of natural language query expected, going beyond the schema's generic description.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many specific domains (SEC filings, FDA drugs, etc.) and emphasizes that results include full schemas for direct calling. This distinguishes it from siblings like 'deep_research' or 'search_within' as the meta-discovery tool.

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

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

The description explicitly advises: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear usage context but does not list explicit alternatives or when-not conditions, though the 'FIRST' implies it is the starting point.

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 discloses the USPTO PatentsView API sunset and soft-fail behavior, and explains the parallel fan-out across sources. Even with annotations already indicating safe reads, this adds valuable 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 well-structured with example queries, then preference, then return details. A bit lengthy but each sentence adds value; could be slightly more concise.

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

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

Given no output schema, the description comprehensively explains what is returned (cik, filings, fundamentals, patents, news, LEI) and includes caveats. Complete for this tool's complexity and parameter count.

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

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

The input schema already has 100% coverage with descriptions. The description reinforces that names are not supported and provides extra context about the 'type' parameter, adding value beyond the schema.

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

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

The description clearly states it provides a full cross-source profile of a US public company, listing specific sources and returned data. It distinguishes itself from sibling tools like 'resolve_entity' by explicitly stating when to use each.

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

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

The description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and notes that names are not supported, advising to use resolve_entity first.

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 destructiveHint=true and idempotentHint=true. The description adds context that the tool deletes a 'memory' resource, which is not inferred solely from annotations. However, it doesn't describe side effects or permission requirements, so a small deduction.

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?

Extremely concise: two sentences, front-loaded with action and purpose, no unnecessary words. 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 low complexity (single parameter, no output schema, no nested objects) and rich annotations, the description covers purpose, usage, and behavior adequately. No gaps for this 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% and the description adds no new meaning beyond the schema's description of the 'key' parameter. With high coverage, 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 'Delete a previously stored memory by key,' which is a specific verb+resource pair. It also distinguishes itself from siblings like 'remember' and 'recall' by naming them explicitly.

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

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

Provides explicit when-to-use conditions: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also suggests pairing with related tools, offering clear guidance.

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

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

Annotations already declare read-only, open-world, and idempotent behavior. The description adds that the tool fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. It also mentions the output is a single text blob ready to place at site-root/llms.txt. No contradictions.

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

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

The description is concise and front-loaded: the first sentence states the main action, followed by a clear list of use cases. Every sentence earns its place; no wasted words.

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

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

Given the tool's low complexity (2 parameters, no output schema, rich annotations), the description adequately covers what the tool does and outputs. It could mention error handling or rate limits, but the combination of annotations and description is sufficient for an agent to use the tool correctly.

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

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

Schema coverage is 100% and both parameters have detailed descriptions. The tool description does not add additional meaning beyond what is already in the input schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's function: 'Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly.' It specifies the verb, resource, and process, and distinguishes from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing on file generation rather than analysis.

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

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

The description provides explicit usage scenarios: getting a client's site indexed, drafting for own project, or auditing a competitor. It does not include when not to use or alternative tools, but the listed use cases give clear context for when this tool is 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?

Annotations declare readOnly, openWorld, idempotent, and non-destructive. The description adds that it returns 'current position, altitude, velocity, and heading' in real-time. No contradictions, but no details on rate limits or data freshness.

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

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

Single, front-loaded sentence with an example and key output fields. Zero waste.

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

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

Given single parameter, rich annotations, and presence of output schema, the description fully covers what the agent needs to know about inputs and outputs.

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

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

Input schema has 100% coverage with a clear description and example. The tool description echoes the format but adds minimal additional value beyond the schema. Baseline 3 applies.

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

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

The description clearly states the verb 'Track', the resource 'aircraft', and the key differentiator 'by ICAO24 transponder code', distinguishing it from siblings like get_arrivals and get_departures.

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

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

The description implies when to use (have an ICAO24 code, want real-time data) but does not explicitly mention when not to use or provide alternatives, though the purpose is clear among siblings.

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

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint as false. The description adds minimal behavioral context, only stating that it returns arrival and flight info. No contradictions.

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

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

The description is two sentences with clear front-loading of purpose. Every word is necessary and no redundant information.

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

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

Given the high schema coverage, existence of an output schema, and clear annotations, the description provides sufficient context for a read-only retrieval tool. It does not elaborate on return format, but this is acceptable since output schema exists.

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

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

The schema already provides full descriptions for all three parameters. The description reiterates the parameter usage but adds no new semantic meaning 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 retrieves flights that landed at an airport during a time window, with specific details on airport code and time range. It distinguishes itself from the sibling 'get_departures' by focusing on arrivals.

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 no guidance on when to use this tool versus alternatives like 'get_departures' or other flight-related tools. It only states how to use the parameters, not the context of usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. Description adds that returns departure details and flight info, but no behavioral surprises beyond annotations.

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

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

Two concise, front-loaded sentences with 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?

For a simple 3-parameter tool with output schema, the description covers everything needed: what it returns and required inputs. No gaps.

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

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

Schema coverage is 100% with good parameter descriptions. Description reiterates 'ICAO code' and 'start/end times' but adds no new semantic value over 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 retrieves flights that took off from an airport within a time window, using specific verb 'Get' and resource 'departures'. It distinguishes from sibling tools like 'get_arrivals'.

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

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

Explicitly instructs to specify ICAO airport code and time window, though lacks explicit when-not-to-use or alternative references. Context makes usage clear.

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

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

Annotations already declare readOnly, idempotent, and not destructive. The description adds real-time context ('currently flying') and lists returned fields, providing useful behavioral insight beyond the structured metadata.

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

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

Two sentences with no wasted words: the first specifies the action and method, the second lists return fields. Information is front-loaded and efficiently structured.

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

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

For a straightforward query tool with 4 well-documented params and an output schema, the description captures all essential context: what it does, how (bounding box), and what it returns. 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 description coverage is 100% with each parameter having a clear description. The tool description adds no additional parameter-level detail, so a 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 it finds aircraft in a geographic area via bounding box, lists specific return fields, and inherently distinguishes from sibling tools like get_aircraft or get_arrivals that target single aircraft or airports.

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

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

The description implies usage for area-based queries but provides no explicit comparison to alternatives, no when-not-to-use guidance, and no prerequisites such as valid coordinate ranges.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint false. Description adds that it returns specific fields and defaults to active subscriptions, which is useful context beyond annotations.

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

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

Two sentences, front-loaded with purpose, no unnecessary words. Every sentence contributes value.

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

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

Despite no output schema, the description lists return fields. Annotations cover safety and idempotence. The description is complete for the tool's simplicity and provides usage context.

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

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

Schema covers the single parameter with a description, and the description does not add additional semantics beyond what is already in the schema. Baseline 3 is appropriate.

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

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

Clearly states the tool lists the caller's active subscriptions and specifies returned fields. Differentiates from sibling tools (subscribe/unsubscribe) by being a read-only list operation.

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

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

Explicitly tells when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Provides clear context for using the tool versus alternatives.

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

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

Annotations are minimal (all false), but the description adds rich behavioral context: rate-limited to 5 per identifier per day, free (no quota impact), and describes how feedback is processed (team reads digests daily, 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 a single dense paragraph that is highly efficient. It front-loads purpose, then covers use cases, then specific guidance. Every sentence earns its place 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 tool's simplicity (no output schema, 3 params, 2 required), the description covers all necessary aspects: what to submit, how to format it, constraints (rate limits, quota), and expected outcome (team reads daily). No gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the type meanings in more conversational terms and reinforcing the message content guidelines. This improves clarity beyond the schema.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It explicitly lists use cases (bug, feature/data_gap, praise) and distinguishes this feedback tool from the extensive sibling list, which contains no other feedback tool.

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

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

The description provides explicit when-to-use scenarios: 'when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives important usage tips like describing in terms of tools/packs, not pasting end-user prompts, and notes rate limits and free usage.

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

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

Discloses beyond annotations: data source (CF analytics-engine), no PII, caching duration (5min-1h depending on window), and that it's self-aggregating. Annotations only indicate readOnly, idempotent, not destructive; description adds crucial 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?

Front-loaded with core purpose, then context-rich details in a structured list. Every sentence serves a purpose: returns, use cases, technical details. No waste.

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

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

Given only one optional parameter, no output schema, the description fully covers what the tool does, why to use it, and how it works (caching, privacy, data source). 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 already describes window enum with 24h/7d/30d. Description adds semantic guidance: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds value beyond the schema's static 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 returns top tools, top packs, and total call volume over a window, with a specific verb 'returns'. It distinguishes from siblings like 'discover_tools' by focusing on what other AI agents are calling right now, providing unique value.

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 tool, aligning use case) and guides window selection (shorter vs longer windows). While it doesn't explicitly say when not to use, the use cases are clear and help differentiate from 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 extensive behavioral context: algorithmic details (monotonicity, partition checks), semantic anchor with Jaccard threshold, partition filter conditions, fill check with book depth analysis, and trade recommendations.

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

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

The description is thorough but somewhat lengthy. It is well-structured with clear sections and front-loaded requirement. Every sentence adds value, but could be slightly more concise while retaining completeness.

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

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

For a complex tool with no output schema, the description covers inputs, behavior, response structure, edge cases (placeholder slugs, low similarity), and references sibling tools for further steps. All necessary context for an agent to use the tool effectively is present.

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

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

Schema covers both parameters with descriptions, but the description adds significant depth: explains the role of event slugs/topics, provides examples, and clarifies the difference between modes. Schema coverage is 100%, yet the description still adds value.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic), each with specific use cases, and differentiates itself from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

Explicit guidance on when to use each mode: event for specific markets, topic for cross-event scanning. Warns that calling with no arguments fails. Directs users to polymarket_fill_risk for custom sizing, providing clear alternatives and exclusions.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds extensive behavioral context: caching (1h at KV level keyed on all knobs), diagnostic outputs for empty segments, and the Fed_candidates note about data reliability. 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 rich in detail but verbose (over 400 words). While well-structured with sections and bold terms, its length may reduce quick comprehension for an AI agent. Every sentence is informative, but conciseness is sacrificed for completeness.

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

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

Given 9 parameters, no output schema, and complex nested outputs (by_segment, diagnostics), the description thoroughly explains all output fields, model families, edge cases (Fed bets, placeholder filters), and caching behavior. It leaves little ambiguity about what the tool returns and why segments might be empty.

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

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

Input schema has 100% coverage with good descriptions. The description adds some value by grouping parameters as 'tradeable-edge knobs' and explaining their purpose (e.g., 'drop thin-book opportunities'), but it largely mirrors the schema's explanations without deeper semantic enrichment.

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

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

The description clearly states the tool scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price, targeting agents who need betting suggestions without paging through hundreds of markets. This distinguishes it from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread.

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

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

The description explicitly frames the tool as 'built for what should I bet on today' and details tradeable-edge knobs (min_liquidity, max_spread_pp, etc.) that guide usage. However, it does not explicitly exclude cases where the tool should not be used or compare it to alternatives beyond the sibling 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, idempotent, non-destructive. The description adds rich behavioral context: response structure (tracked, expired, snapshot_dates), computation of trend and decay, and limitations (60-day TTL, daily closes, not intraday). No contradiction with annotations.

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

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

The description is front-loaded with the core purpose and includes useful detail, but it is somewhat lengthy. Every sentence adds value, but could be slightly more streamlined without losing substance.

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?

In the absence of an output schema, the description adequately explains the response fields (tracked, expired, snapshot_dates) and their relevance. It covers decay computation and limitations, but could expand on concepts like 'edge_pp_net' or 'window family' for complete clarity.

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 fully. The description adds context beyond schema: default values (14 days, '1wk'), clamps (2-30 days), and explains how parameters affect the response (window family). This adds meaningful guidance despite high 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 specifies the tool's function: providing edge persistence and decay telemetry from daily snapshots. It uses a specific verb 'answers' and distinguishes itself from sibling tools like polymarket_edges by focusing on historical persistence rather than current values.

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

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

The description gives explicit guidance on when to use the tool: to differentiate fresh edges from old decaying ones. It mentions the median lifespan as a competition clock, implying when to act. However, it does not explicitly state when not to use it or name alternatives beyond the context.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint false. Description adds detailed behavioral context: walks the ladder, returns specific fields (top_of_book, vwap_fill_price, etc.), and warns about forced directional risk and thin legs. No contradiction with annotations.

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

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

Description is long but well-structured with uppercase mode markers and front-loaded purpose. Each sentence provides essential information, though some verbosity could be trimmed without losing clarity.

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

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

Given the absence of an output schema, the description compensates by enumerating all return fields and explaining risks (partial fills, thin legs). For a complex tool with two modes and four parameters, the description is fully comprehensive.

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%, meaning every parameter has a description. The description further clarifies parameter meanings (e.g., side defaults, size_usd interpretation in each mode), adding value beyond the schema.

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

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

The description clearly states the tool checks realizable vs theoretical edge against live CLOB order-book depth, distinguishes single-market and basket modes, and lists returned values. It explicitly references sibling tools polymarket_arbitrage and polymarket_edges, making its purpose distinct.

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

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

Provides explicit when-to-use guidance: before acting on arbitrage signals or trades above ~$500. Notes dependencies (requires market or event) and warns about thin books and partial fills. Missing explicit when-not-to-use, but context is clear.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. Description adds rich details: compatibility_warning conditions, temporal alignment, skipped cross-type/subtype counters, and implications for non-equivalent bet shapes.

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 packed with essential information, organized with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS). Slightly long but justified by 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?

Covers all aspects: modes, parameters, response fields, safety warnings, and caveats. No output schema, but description sufficiently explains return format and error conditions.

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 significant meaning by explaining two modes, listing allowed topic values, describing override behavior, and illustrating response structure.

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 spreads between Kalshi and Polymarket. It distinguishes from siblings by focusing on cross-venue comparison, with specific modes and response structure.

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

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

Provides clear contexts for use (topic vs explicit modes) and warns that pre-mapped topics often return compatibility warnings. However, lacks explicit alternatives or when-not-to-use statements.

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 value by clarifying scoping (by identifier) and the absence of side effects, which aligns with annotations. No contradictions.

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

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

The description is concise with just two sentences, front-loading the main purpose and including key usage details without any superfluous 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?

While the description covers purpose and usage well, it lacks details about the return format (e.g., returns a string value or a list of keys). Given no output schema, this information would be valuable for an agent.

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

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

The single parameter 'key' is fully described in the schema (100% coverage). The description adds semantic value by explaining that omitting the key lists all saved keys, which goes beyond the schema's basic description.

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

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

The description clearly states the tool retrieves a value stored via 'remember' or lists all keys if no key argument is provided. It distinguishes itself from sibling tools like 'remember' and 'forget' by naming them directly.

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 the tool (to look up context stored earlier) and mentions pairing with 'remember' and 'forget'. However, it does not explicitly state when not to use it or provide alternatives beyond sibling names.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior. Description adds valuable behavioral details: mark_read flag affects future calls, event structure, and filter capabilities. 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-load the main purpose, then provide key details. Every sentence adds value; no redundancy or filler.

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

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

For a read-only list tool with 5 optional parameters and no output schema, the description explains return fields, filtering, mark_read semantics, and even provides an alternative HTTP endpoint. Sufficient for agent to invoke correctly.

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

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

Schema covers all 5 parameters with descriptions (100% coverage). Description adds context for type and mark_read, but does not substantially enhance understanding of limit or unread_only beyond schema. Baseline 3 is appropriate.

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

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

Description clearly states the tool pulls fired events from subscription feed, with specific details about returned fields (source, citation_uri, raw payload). Differentiates from sibling tools like list_subscriptions by focusing on alert retrieval rather than subscription management.

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

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

Provides clear context: filters by type and since, mark_read behavior, and mentions HTTP endpoint as alternative. Implicitly distinguishes from polling and scripts. Lacks explicit when-not-to-use, but context is strong.

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

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

The description adds significant behavioral context beyond annotations: data source fan-out (SEC, GDELT, USPTO), fallback logic (GDELT→GNews), soft-fail for USPTO, date format details, and return structure. No contradictions found.

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 query examples and efficiently packs information about multiple data sources and fallbacks. While slightly long, every sentence adds value.

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

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

For a complex tool with 3 params and no output schema, the description explains the return structure (changes[] grouped by source, count, citation URIs) and mentions data source limitations, making it fairly complete. Lacks pagination details, but annotations cover idempotency.

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 value by explaining `since` formats in more detail (ISO vs relative), `value` accepts ticker or CIK, and `type` is limited to 'company'. 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 verb ('change feed') and resource ('for a company in the last N days/weeks/months'), and distinguishes from the sibling 'entity_profile' by specifying when to use the alternative.

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

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

The description gives explicit when-to-use examples and directly contrasts with 'entity_profile'. However, it does not explicitly state when not to use, but the context of sibling tools provides some guidance.

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

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

Annotations already indicate idempotentHint=true and non-destructive. The description adds context about scoping by identifier, persistence duration, and authentication behavior, which goes beyond annotations.

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

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

Two well-structured sentences. The first front-loads the purpose, the second adds context. No wasted words.

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

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

Complete for a simple storage tool with no output schema. Explains behavior, scoping, and persistence. 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%, but description adds naming conventions for keys and clarifies that value can be any text, which aids in proper parameter usage.

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

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

The description clearly states the tool saves data for reuse across conversations/sessions, with specific examples like tickers and preferences. It distinguishes itself from sibling tools recall and forget.

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

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

Explicitly says when to use: when discovering something worth carrying forward. Mentions pairing with recall and forget, but does not explicitly state when not to use it.

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

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

Annotations already indicate read-only, open-world, idempotent. Description adds that each call cascades through several lookup endpoints, replacing 2-3 manual lookups. No contradiction. Provides behavioral insight beyond annotations.

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

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

Description is moderately long but well-structured: starts with examples, then usage guidance, then supported types with details. No redundant sentences. Each part adds value, though slightly verbose relative to ideal conciseness.

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

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

Given no output schema, description adequately explains return values for both entity types, including citation URIs. Covers input formats, output details, and internal cascading behavior. Complete for a resolver tool.

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

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

Schema coverage is 100%, but description significantly enhances understanding by detailing what each type returns (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand for drug). This output semantics is not in schema. Examples for value parameter clarify acceptable inputs.

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

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

Description clearly states it resolves names to canonical IDs, provides concrete examples ('ticker for...', 'CIK for...'), and distinguishes itself from siblings by noting it replaces multiple manual lookups. Verb+resource (resolve entity) with specific 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 says 'Use FIRST whenever you have a name but need an ID.' Provides clear context but does not explicitly mention when not to use or contrast with specific alternatives (e.g., entity_profile).

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds that it probes with ai_visibility_check, treats the first entity as the subject for narrative, and returns a ranked list with score, confidence, and signal density. 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 the main action. Every sentence provides essential information without redundancy or fluff.

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

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

The description explains what the tool does, when to use it, and what it returns. It does not explicitly mention the entity count limit (2-8 from schema) or details about the ranking algorithm, but these are minor gaps given the richness of other 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?

Schema coverage is 100% with descriptions. The description adds meaning beyond schema by explaining that the first entity is treated as the subject, that models can be omitted for workers-ai default, and that _apiKey is only needed for anthropic.

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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check for probes, then ranks them. This differentiates it from the sibling tool ai_visibility_check which checks a single entity.

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

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

The description provides a concrete use case (competitive AI-marketing audits) and implies that for single entity checks one should use ai_visibility_check instead. However, it does not explicitly mention when to avoid this tool or list all alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds significant behavioral detail: composite fan-out across deps.dev and bundlephobia, partial failure graceful degradation (e.g., bundlephobia first measurement takes 5-30s, sources_failed listed on timeout).

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-organized: starts with purpose, then lists fields, then usage, then limitations. A bit long but all sentences earn their place. Could be slightly more concise 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?

Even without an output schema, the description fully explains the return format (summary block fields, per-advisory detail, links, alternative versions) and error handling (sources_failed list). No gaps in context for an AI agent to use this tool.

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

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

Schema already has 100% description coverage for both parameters. The description adds value by noting the default behavior for version (latest when omitted) and that scoped packages are accepted. No extra parameter-level meaning needed 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 starts with a clear composite action verb ('should I add this npm package to my project check') and specifies the exact resources (npm packages) and sub-checks (license, advisories, bundle size, etc.). It is distinct from all sibling tools, which are unrelated.

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

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

Explicitly states when to use: 'whenever an agent asks is X safe/popular/small or what does adding lodash cost me'. Also gives when-not-to-use: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly.'

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, etc., but description adds substantive behavioral details: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flagging, and character offsets for verification.

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

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

Single paragraph, front-loaded with purpose, every sentence adds value (including pairing info and algorithm details). No wasted words; clear and direct.

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

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

No output schema, but description explains return format (top-N passages with offsets and similarity scores). Covers algorithm, limits (200K chars, truncation), and pairing with another tool. Fully addresses tool complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds little beyond schema for parameters (e.g., examples for query, default for limit is already in schema). While it explains algorithm context, it doesn't provide critical new parameter info.

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. It distinguishes itself from siblings by noting when to use (record too big for prompt) and pairs with ask_pipeworx_grounded, making differentiation explicit.

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

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

Explicitly says 'Use when the record is too big to cram into the prompt', provides alternatives (ask_pipeworx_grounded), and explains benefits like saving context and returning only relevant passages. Also mentions input size cap and truncation behavior.

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

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

Annotations indicate this is a write operation (readOnlyHint=false) and idempotent (idempotentHint=true). The description adds significant behavioral context: returns a subscription id, requires OAuth, lists supported types, delivery channel behavior (webhook signing secret given once, auto-disable after 10 failures, SMS cap of 10/day), and phone verification requirement. 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 lengthy but well-structured, with clear sections on requirements, supported types, and delivery channels. It is front-loaded with the core purpose. However, it could be slightly more concise without losing essential information.

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

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

Given the tool's complexity (multiple subscription types, diverse parameters, optional delivery channels with constraints), the description covers all necessary aspects: type selection, parameter formats, authentication, delivery setup, and limitations (rate limits, verification). There is no output schema, but the description mentions the return value (subscription id), which suffices.

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

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

The schema description coverage is 100%, but the description adds meaningful context per subscription type (e.g., ticker and items for sec_8k, topic and min_spread_bps for polymarket_edge) and clarifies delivery requirements (e.g., phone must match verified account, webhook HTTPS only). This goes beyond the schema's generic descriptions.

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

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

The description clearly states the verb 'Create' and the resource 'proactive monitoring subscription', and mentions the return value 'new subscription id'. This distinguishes it 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 explicitly states the requirement for a Pipeworx OAuth account and explains the supported types with examples. It also details delivery channel constraints, but does not explicitly state when not to use the tool, though context with siblings provides sufficient guidance.

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

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

Annotations already set readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it is an onboarding entry point, returns example questions, and can be focused by topic. 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 fairly long but well-structured, front-loading common user questions. Every sentence adds value, though the list of topics could be slightly more concise.

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

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

Despite no output schema, the description fully explains what is returned (category-bucketed example questions) and covers the single parameter and usage scenario. It provides enough context for the agent to decide when to use this tool.

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

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

Schema coverage is 100% with a detailed description. The tool description adds context (omit for full spread) and clarifies usage ('Call with no arguments for the full spread, or pass topic'). This adds meaningful 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?

The description clearly states the tool returns category-bucketed example questions with exact tool+argument shapes. It explicitly distinguishes itself from sibling tools like ask_pipeworx and entity_profile, providing a specific verb+resource scope.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and details when to use with or without the topic parameter. It directly names alternatives (meta-tools) and provides clear context for when to use this versus other 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?

Discloses that the row is deactivated (not deleted) and historical events remain available via 'recent_alerts'. Adds value beyond annotations (readOnlyHint=false, destructiveHint=false) by specifying the exact mutation 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, no wasted words. First sentence states action and constraint, second explains consequence. Front-loaded and efficient.

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

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

For a simple 1-param tool with no output schema, the description covers action, ownership, and side-effect. No gaps.

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

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

Schema coverage is 100% with a description for 'id'. The description adds context that the id comes from 'subscribe', which is helpful. Baseline 3, plus slight improvement.

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 'Cancel a subscription by id' with a specific verb and resource. It also mentions ownership enforcement, 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?

Explicitly states 'you can only cancel your own subscriptions,' providing guidance on when to use the tool and implying when not to. Lacks explicit alternatives but is clear enough.

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

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

Describes return values including verdict types, structured form, actual value with citation, and percent delta. Notes the data source (SEC EDGAR + XBRL). Adds significant context beyond annotations (readOnlyHint, openWorldHint, idempotentHint, non-destructive). No contradiction.

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

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

Description is concise yet comprehensive, with each sentence adding value. Front-loaded with core functionality and examples, followed by domain and return details. No redundant information.

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

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

For a single-parameter tool, the description covers purpose, usage domain, return format, and efficiency benefit. It sufficiently equips the agent to select and invoke the tool correctly without needing external documentation.

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 detailed description of the 'claim' parameter. Description adds examples of natural-language claims, providing helpful context for the model to construct proper inputs.

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 performs natural-language claim verification against authoritative sources for company-financial claims. It provides specific action verbs and examples, distinguishing it from sibling tools by its focused domain.

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 whenever the agent needs to check whether something a user said is factually correct.' Also notes the supported domain (company-financial claims). Does not explicitly mention when not to use or list 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.