climate

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: default model details, cost implications of providing an API key (BYO key, pay Anthropic directly), and the return structure (per-model {score, confidence, signals, raw_response} + combined view). 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 and front-loads the primary purpose. It is efficient but could be slightly more concise (e.g., combining the first sentence). No unnecessary information, but the second sentence could be integrated.

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

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

Given the tool's complexity (4 parameters, no output schema), the description covers the main behavioral aspects: what it does, return shape, model options, and cost. It lacks explicit differentiation from siblings like 'scan_competitor_ai_presence' and does not discuss rate limits or pagination, but annotations cover safety. Reasonably complete for its purpose.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema; it reiterates the default model and that the API key is passed straight through, but these are already hinted in the schema. No significant new parameter semantics are provided.

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

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

The description clearly states the tool's action ('Probe one or more LLMs for what they know'), the resource (business/brand/product/topic), and the output (visibility score 0-100 per model). It distinguishes from sibling tools by focusing on AI visibility scoring, a unique capability among the listed siblings.

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

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

The description provides clear usage context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains when to use the default model vs. when to provide an API key for Anthropic. However, it does not explicitly mention when not to use this tool or direct alternatives among the 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 indicate read-only and idempotent behavior. The description adds valuable context: it routes to 3,745 tools across 884 sources and returns structured answers with stable citations (pipeworx:// URIs). This goes beyond annotations and clarifies the tool's internal operation.

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 key guidance to prefer over web search, then lists use cases and examples. It is efficient but slightly dense; every sentence adds value, though a bit more structure (e.g., bullet points) could improve readability.

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

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

For a tool that queries a vast database, the description provides sufficient context: it explains the routing, citation format, and gives examples. It does not detail the exact output format but the annotation (readOnlyHint) and description imply a structured answer. The sibling 'ask_pipeworx_grounded' is listed but not distinguished, a minor shortfall.

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% as all six parameters are documented as aliases for 'question'. The description includes helpful examples but does not add significant meaning beyond the schema. 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 that the tool is preferred over web search for factual questions about current or historical data across many domains. It lists specific categories and provides examples, making the purpose unmistakable and distinguishing it from generic web search.

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 to prefer this tool over web search for factual queries and gives numerous examples of when to use it. However, it does not differentiate from the sibling tool 'ask_pipeworx_grounded', leaving a minor gap in 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 readOnly, openWorld, idempotent, non-destructive. Description adds behavioral details: exact refusal reasons, output format, and the fact that it uses only tool result content. 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?

Two sentences covering purpose, mechanism, output format, use cases, and trade-offs. Every sentence adds value with no redundancy. Front-loaded with key phrase 'hallucination-resistant answer mode'.

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

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

Despite no output schema, description fully specifies return format for both success and failure. Explains routing, extraction, refusal reasons, and cost comparison with sibling. Complete for a complex multi-source query 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 each parameter described as an alias for question. Description adds value by clarifying that it accepts natural language questions and that all parameters are aliases, reinforcing 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 this is a hallucination-resistant answer mode for high-stakes reads, distinguishing it from the sibling ask_pipeworx by emphasizing answer extraction from tool results only. The purpose is specific and unambiguous.

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

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

Description explicitly says to use when answers will be quoted/cited/acted on and the agent must not invent facts, and to prefer ask_pipeworx for casual lookups due to extra cost. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds extensive behavioral context: safety mechanisms, low-confidence handling, closed markets, wide spreads, resolution-rule risk. 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?

Front-loaded with purpose, but description is very long and dense with examples. Could be more concise without losing critical details.

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

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

Despite no output schema, description covers response shapes, evidence structure, safety, and edge cases comprehensively. Fully compensates for missing output schema.

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

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

Schema coverage is 100%. Description adds rich meaning: explains market input flexibility (slug, URL, question), depth parameter with quick/thorough distinction, include_raw with size implications. Exceeds baseline expectations.

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 researches a Polymarket bet by pulling Pipeworx data, with examples of inputs. It does not explicitly differentiate from sibling tools, but the use cases are 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?

Provides explicit use cases ('should I bet on X', 'what does the data say about Y'), classifiers, and fan-out examples. Lacks explicit when-not-to-use or 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?

Beyond annotations (readOnly, idempotent), the description details data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting behavior, and return format with citation URIs. No contradiction with annotations.

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

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

The description is longer than necessary but well-structured with example queries upfront. Information is front-loaded, and every sentence adds value. Minor redundancy in listing query patterns could be trimmed.

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 covers inputs, behavior, data sources, sorting, and output format (paired data + citations). It adequately prepares the agent to use this tool correctly for comparisons.

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 valuable context: examples of values (tickers/CIKs for company, names for drug), min/max items, and explains how the 'type' parameter selects data source. This goes beyond the schema's enum descriptions.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2–5 companies or drugs in one parallel call. It provides example queries and explicitly distinguishes from sequential single-pack lookups.

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

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

Explicitly advises to always prefer this tool over sequential lookups when comparing entities. While not exhaustive about when not to use, the guidance is clear and actionable.

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 (readOnlyHint, idempotentHint): it returns 'side-by-side daily mean temperatures' and mentions purpose 'to assess model agreement and uncertainty'. Annotations already indicate safe read/idempotent behavior; description complements with output specifics.

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

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

Two concise sentences front-load the purpose and output. Every sentence adds value: first states what it does, second states what it returns and why. No unnecessary words.

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

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

Given 4 parameters, existence of output schema, and annotations (readOnlyHint, idempotentHint), the description is complete: it explains the tool's function, input scope, and output purpose. No missing behavioral or contextual 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 description coverage is 100% and each parameter (latitude, longitude, start_date, end_date) has clear descriptions. The tool description does not add additional parameter-specific meaning beyond the schema, so 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 compares temperature projections across climate models for the same location and date range, distinguishing it from siblings like 'get_climate_projection' (single model) and 'compare_entities' (non-climate). Specific verb 'compare' and resource 'temperature projections' with scope 'across climate models'.

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

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

The description implies when to use (comparing models at a location/date range) but does not explicitly state when not to use or list alternatives like 'get_climate_projection' for single models. Context from sibling tools provides some guidance but the description lacks explicit 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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes beyond, detailing parallel execution, returns of 'verbatim evidence, confidence, source, fetched_at, pipeworx:// citation', and explicit handling of gaps (gaps[]). It also discloses account requirements and plan limitations for depth: 'Requires a Pipeworx account... depth:'thorough' requires a paid plan.' No contradictions.

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

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

The description is long but efficiently packed with information. Every sentence adds value, from core functionality to usage guidance to limitations. The front-loaded statement 'Grounded multi-source research in ONE call' immediately clarifies the tool's benefit. Minor redundancy in repeating 'Requires a Pipeworx account' but overall well-structured.

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

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

Given the absence of an output schema, the description fully explains the return value: 'structured findings packet' with specific fields (evidence, confidence, source, etc.) and mentions gaps handling. It also covers expected execution time ('15-60s'), authentication, and plan restrictions. No gaps remain.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds context: depth levels are explained with explicit counts ('quick=3, standard=5, thorough=8') and the question parameter is noted to accept broad/multi-part input. This adds 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 performs 'grounded multi-source research' and decomposes questions into sub-questions. It explicitly contrasts with sibling tool ask_pipeworx for single lookups, establishing a distinct purpose.

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

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

The description explicitly advises using this for 'broad or multi-part questions' and provides examples ('compare X and Y'). It directly says to use ask_pipeworx for single lookups, offering clear guidance on when not to use this tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by stating that results include 'full input schemas (with curated examples)' and are 'ready to call directly, no second schema lookup needed.' This supplements the annotations with useful 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 relatively long but efficiently packed with needed information. It front-loads the purpose and includes examples. While every sentence adds value, it could be slightly more concise without losing clarity.

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

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

Given the tool's complexity (discovery with multiple params and aliases, no output schema), the description covers core functionality, return content, and usage. It lacks explicit error handling or pagination details, but the description provides sufficient context for an agent to use the tool effectively.

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

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

Schema coverage is 100%, and the description adds meaning by listing aliases for the query parameter and providing example queries. It also explains the limit parameter's default and max. The description adds practical usage context beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It includes specific examples of use cases (SEC filings, FDA drugs, etc.) and explains what it returns (top-N relevant tools with schemas). This clearly differentiates it from sibling tools which are specific data retrieval or analysis tools.

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

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

The description explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance. It does not explicitly state when not to use or mention alternatives, but the context implies direct tool calls when the tool is already known.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about parallel execution, soft-failure of patents, and specific data sources. However, it could mention response size or pagination.

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 examples and then provides structured details. It is longer than minimal but each sentence adds useful information 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 lists return fields and data sources comprehensively. It also mentions limitations (patents soft-fail, name unsupported). Minor omission: no mention of error handling or rate limits, but overall complete.

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

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

The schema covers 100% of parameters with descriptions. The description adds value by clarifying that names are not supported and that value must be ticker or zero-padded CIK, which is not fully explicit in the schema.

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

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

The description clearly states the tool's purpose: creating a full cross-source profile of a US public company in one parallel call. It provides concrete examples of user queries and lists the specific data sources and return fields, distinguishing it from more granular lookup tools.

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

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

The description explicitly says to prefer this tool over chaining single-pack lookups for holistic views, and advises using resolve_entity first if only a name is provided. This provides clear when-to and when-not-to 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 destructiveHint=true and idempotentHint=true. The description reinforces these by stating 'Delete' and 'clear sensitive data', adding context about why a memory would be deleted (stale, task done). No contradiction with annotations.

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

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

Three sentences covering purpose, usage guidance, and sibling relationships. No wasted words. Each sentence adds value and is front-loaded with the action.

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 destructive tool with one parameter, the description fully covers purpose, usage context, parameter semantics, and relationship to siblings. No output schema needed; completeness is achieved.

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 described in the schema as 'Memory key to delete', with 100% schema coverage. The description does not add extra detail beyond the schema, but the overall tool description mentions 'by key', which aligns. Baseline 3 is appropriate given 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 states 'Delete a previously stored memory by key', specifying the verb (delete) and resource (memory). It explicitly distinguishes from sibling tools 'remember' and 'recall', which store and retrieve memories respectively.

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

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

Provides explicit guidance on when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data'. Also names alternatives to pair with ('remember and recall'), clarifying complementary roles.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral details: fetches the page, extracts title/description/key links, and emits standard format. 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 (4 sentences), front-loaded with purpose, and well-structured. Every sentence adds value with no fluff.

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

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

Given the tool simplicity (2 params, annotations present, no output schema), the description covers purpose, process, output format, and use cases completely. It explains what the output is (single text blob) and where to place it.

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

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

Schema coverage is 100% with both parameters described. The description does not add additional detail beyond what the schema provides, so baseline of 3 is appropriate.

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

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

The description clearly specifies the verb 'generate', the resource 'llms.txt file', and the context for AI crawlers. It distinguishes itself from sibling tools by being uniquely focused on this specific output.

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 lists explicit use cases (indexing client site, drafting own llms.txt, auditing competitor) but does not explicitly state when not to use or name alternatives among siblings. It provides clear context, earning a 4.

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 and idempotent behaviors. The description adds value by specifying the output format (daily forecasts with temperature, precipitation, weather conditions) and the full time range (1950–2050), providing 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?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words.

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

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

Given the presence of a comprehensive output schema (not shown but indicated as present) and clear annotations, the description sufficiently summarizes what the tool returns. No additional details are needed.

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

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

Schema description coverage is 100%, so the baseline is 3. The description repeats information already in the schema (date range and location) without adding significant new semantic details.

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

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

The description clearly states the tool fetches historical and future temperature and precipitation projections for a location with a specific time range. It distinguishes itself from unrelated sibling tools, making its purpose unambiguous.

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

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

No explicit guidance on when to use this tool versus alternatives is provided. While the sibling list shows many unrelated tools, the description does not address potential confusion with similar climate or data retrieval tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. Description adds detail about return fields and that it lists active subscriptions (with optional inactive inclusion). No contradictions, and behavior is well-described.

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

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

Two short, informative sentences. Every word serves a purpose—no fluff.

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

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

Despite no output schema, the description lists all returned fields. Combined with annotations and clear usage guidance, the context is complete for a simple list tool.

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

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

Schema covers all parameters (100% coverage) with descriptions. The tool description does not add further parameter-specific meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

Clearly states the tool lists subscriptions and specifies the exact return fields. Distinguishes from siblings like subscribe/unsubscribe by focusing on listing active subscriptions.

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

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

Explicitly advises using the tool to review subscriptions before adding more or to find an ID to cancel, providing clear usage context and alternatives.

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

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

The description adds behavioral context beyond annotations: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. Annotations already indicate no read-only or destructive behavior, but the description provides useful operational constraints.

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 efficiently written: one paragraph with a clear front-loaded purpose, followed by usage details and behavioral notes. Every sentence adds value, making it concise and well-structured.

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

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

Given the tool's simplicity (sending feedback) and the presence of annotations, the description covers key aspects: purpose, usage, rate limits, and quota. No output schema is needed. It is sufficiently complete for an agent to use correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add additional parameter semantics beyond what the schema provides; it reiterates the enum values but does not offer new insights. Thus, a 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 verb ('Tell the Pipeworx team') and the resource (feedback about tools). It distinguishes itself from sibling tools by focusing on reporting bugs, feature gaps, or praise, which are not covered by the other query/research tools.

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

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

The description explicitly specifies when to use (bug, feature, data_gap, praise) and provides clear guidance: describe in terms of Pipeworx tools/packs, don't paste the end-user prompt. It also mentions rate limits and that it's free, helping the agent decide when to invoke.

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

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

Annotations include readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This discloses data source, privacy, and caching behavior, going beyond annotations.

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

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

The description is concise with no wasted words. It front-loads the central purpose, then lists use cases, then technical details. Every sentence is informative.

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

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

For a simple read-only tool with one parameter and no output schema, the description explains what is returned (top tools, packs, call volume) and caching behavior. It is fully sufficient for an agent to understand and invoke correctly.

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

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

One parameter 'window' with 100% schema coverage. Description adds semantic context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances understanding beyond the schema.

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

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

The description states: 'Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d).' It uses a specific verb and resource, and clearly distinguishes from sibling tools like discover_tools or ask_pipeworx.

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

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

Explicit use cases are provided: 'Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need.' This tells the agent precisely when to use this tool.

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

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

Despite annotations that already indicate safe, read-only behavior, the description adds extensive detail: algorithmic checks (monotonicity, partition-sum), semantic anchor with Jaccard similarity, partition filter, fill check against live CLOB depth, and critical warnings like not trading when realizable_edge_pp ≤ 0. 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 with clear sections (requirements, mode descriptions, algorithm details, response format). Every sentence adds necessary information, though some details could be slightly condensed without losing clarity.

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

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

Given the tool's complexity (two modes, multiple algorithmic checks, no output schema), the description is remarkably complete. It explains the response structure, the fill check logic, and even mentions a related tool for custom sizing. No gaps identified.

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 descriptions. The tool description adds substantial value by explaining the recommended use for each parameter (event for specific markets, topic for cross-event scanning) with concrete examples, and detailing the algorithms associated with each.

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 arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes between two modes (event and topic) with specific use cases, effectively differentiating from sibling tools like polymarket_fill_risk and polymarket_edges.

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

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

The description explicitly requires one of `event` or `topic`, warns against calling with no args, and provides guidance on when to use each mode. It gives concrete examples of inputs. However, it does not explicitly state when not to use the tool or list alternatives beyond mentioning polymarket_fill_risk.

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, open-world, idempotent, non-destructive behavior. The description goes beyond annotations by detailing caching (1h at KV level), output structure (by_segment, diagnostics), and specific model behaviors (e.g., placeholder-slug filter, concentrated_longshot gates). 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 comprehensive but verbose, containing multiple paragraphs with detailed technical specifications (e.g., exact alpha values, funnel counters). While well-structured with sections, it could be more concise for quick understanding; every sentence earns its place but the length may overwhelm.

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

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

Given the complexity (9 parameters, no output schema), the description is exceptionally complete. It covers output structure (by_segment, diagnostics), edge characteristics (edge_pp_net, kelly_fraction), tradeable-edge knobs, and caching behavior. It explains why Fed candidates are excluded. 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?

All 9 parameters are described in the input schema (100% coverage). The description adds extra context beyond schema, such as explaining why slippage_pp defaults to 0.3 (Polymarket fees) and the rationale behind min_partition_leg_kelly (partition arbs don't respond to min_kelly). This adds meaningful 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's purpose: to scan Polymarket markets and return opportunities where Pipeworx data disagrees with the market price. It includes the use case 'what should I bet on today' and distinguishes itself from siblings like polymarket_arbitrage by focusing on edge detection.

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 use context ('built for agents to discover opportunities without paging hundreds of markets') and details the tradeable-edge knobs for filtering. While it doesn't explicitly state when not to use it or compare to alternatives, the context is sufficient for proper tool selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant detail: snapshot-based operation, response fields (tracked, expired, snapshot_dates), decay computation, limits on history depth, and that decay is based on daily closes not intraday.

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

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

The description is long but well-structured: purpose first, then response shape, then limits. It front-loads the core question. Some implementation details (e.g., cache-miss) could be trimmed, but overall it is efficient.

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

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

Without an output schema, the description fully explains the return value structure and field semantics. It covers limits, TTL, and edge cases. Given the tool's complexity, it is complete.

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

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

Schema coverage is 100% with descriptions. The description adds default values and clamp range for 'days' and lists explicit options for 'window' (24hr, 1wk, 1mo), which is helpful 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 states the tool provides 'edge persistence and decay telemetry' and answers a specific question about how long an edge has existed. It uses specific verbs and clearly distinguishes itself from the sibling 'polymarket_edges' by focusing on historical 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 implicitly guides when to use this tool (for historical persistence) by contrasting with fresh edges, but does not explicitly state when not to use it or name alternatives beyond the sibling name. The LIMITS section provides context on constraints.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by detailing that it walks the ladder, returns fill details, and warns about thin books and directional risk. No contradiction with annotations.

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

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

The description is dense but well-structured, covering modes, parameters, warnings, and usage guidance. While somewhat verbose, every sentence adds value and key terms are highlighted. Could be slightly more concise, but effectively communicates.

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

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

Given the complexity of two modes and no output schema, the description comprehensively covers inputs, outputs (including edge cases like thin_legs, forced_directional_risk), and behavior. It explains the dominant loss mode, making it complete for an agent.

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

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

Schema coverage is 100% with parameter descriptions. The description adds meaning beyond schema by explaining how size_usd is interpreted differently in each mode and how side defaults to auto for baskets. This contextualizes parameters without redundancy.

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 order-book depth. It distinguishes two modes (single-market and basket) and explicitly says to use this before acting on polymarket_arbitrage or polymarket_edges signals, differentiating it from sibling tools.

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

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

The description gives clear context: use before arb signals or trades above ~$500. It explains the risk of partial fills and unhedged positions. While it doesn't list exhaustive when-not-to-use scenarios, the guidance is specific and helpful.

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

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

The description extensively discloses behavioral traits beyond annotations: it details the two modes, response structure (leg-by-leg prices, matched spreads, safety fields), temporal alignment checks, and compatibility warnings. It also warns that most macro shortcuts are not tradeable. No contradiction with annotations.

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

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

The description is well-structured with section markers (TWO MODES, RESPONSE, SAFETY FIELDS) and bolded key terms. It front-loads the core concept. While slightly verbose, every sentence adds value, and the structure aids readability.

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

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

Despite no output schema, the description thoroughly explains the return values (leg prices, spreads, safety fields, temporal alignment) and covers edge cases (compatibility warnings, skipped pairs). It provides enough context for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100%, and the description adds significant context: it explains the 'topic' parameter's valid values, defines the override behavior for explicit tickers, and provides examples. This goes beyond the schema descriptions, justifying a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: computing cross-venue spreads between Kalshi and Polymarket for the same question. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing specifically on cross-venue comparisons, and explains the two operational modes (topic shortcuts vs explicit event pairing).

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

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

The description provides clear context on when to use the tool (to compare prices across venues), explains the two input modes, and sets expectations with a caveat that most pre-mapped topics yield compatibility warnings. However, it does not explicitly mention when not to use this tool or suggest 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 provide readOnlyHint, idempotentHint, destructiveHint. The description adds useful context about scope and pairing with remember/forget. No contradictions. It does not mention rate limits or auth details, but these are not critical for a read-only, idempotent tool.

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 (3 sentences), front-loaded with the core action, and every sentence adds value—no filler. It efficiently conveys purpose, usage, scope, and related tools.

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

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

Given the simple input schema and thorough annotations, the description provides all necessary context: how to use, what it returns (value or list), scoping, and relationship with siblings. No output schema is needed as the return is implied.

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% (one optional 'key' parameter). The description adds meaning by explaining that omitting key lists all keys and providing an example use case ('user target ticker, address, prior research notes'). This goes beyond the schema's minimal description.

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

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

The description uses a specific verb ('Retrieve') and resource ('value...saved via remember'), and explicitly distinguishes from sibling tools ('remember', 'forget'). It clearly states both modes: retrieve by key or list all keys. No tautology.

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

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

The description explicitly states when to use ('look up context the agent stored earlier...without re-deriving it from scratch'), and mentions scoping ('Scoped to your identifier'). It also pairs with remember and forget, 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?

Adds valuable context beyond annotations: mark_read:true causes a mutation (marking as read) and provides an alternative URL. Annotations say readOnlyHint=true, which is slightly contradicted by the mutable mark_read option.

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, every sentence provides value. No wasted words.

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

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

Covers output content, filter options, mark_read behavior, polling suitability, and alternative access. Missing error handling or pagination details, but sufficient for a read-mostly 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%, so the baseline is 3. The description adds examples (type 'sec_8k') and explains the effect of mark_read, improving understanding beyond schema descriptions.

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

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

The description clearly states 'Pull fired events from your subscription feed' with specifics about what each alert carries. However, it does not explicitly distinguish from sibling tools like list_subscriptions or recent_changes.

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

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

Provides clear context on filtering by type/since and mark_read behavior, and notes polling is fine. But no explicit guidance on when not to use this tool versus alternatives among siblings.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable context: fans out to multiple sources, fallback behavior, PatentsView API sunset (soft-fail), and return format with citation URIs. 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 fairly long but every sentence earns its place: front-loads example queries, then explains sources, parameters, return format, and sibling distinction. Not wasteful, but 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?

No output schema, but description covers return structure: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'. Explains source-specific behavior and fallbacks. Adequate for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning: examples for 'since' (ISO date and relative shorthand), typical monitoring value '30d' or '1m', and explanation of 'value' (ticker or CIK). Beyond schema but not exhaustive.

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

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

The description clearly states it returns a change feed for a company (SEC, news, patents) in a recent window, with explicit distinction from sibling tool entity_profile (static profile). Specific verb 'return structured changes[]' and resource 'change feed for a company'.

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

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

Provides multiple example queries, explains when to use (recent changes over a window) and when not (use entity_profile for static profile). Also details fallback between GDELT and GNews. Explicit alternative named.

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

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds lifecycle details: stored scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. This enriches behavioral context beyond annotations without contradiction.

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

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

Four sentences with no redundancy. Each sentence adds value: purpose, usage, storage details, and sibling pairing. Front-loaded with the core action, followed by necessary context.

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

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

Despite no output schema, the description fully covers purpose, usage, persistence, and pairing with recall/forget. For a simple key-value tool, this is complete and leaves 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?

Input schema covers 100% of parameters with descriptions. The description repeats the schema's value examples ('findings, addresses, preferences, notes') but adds no new semantic detail. Thus, 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 saves data for reuse across conversations/sessions, using a key-value pair. It specifies the purpose and resource, distinguishing it from sibling tools like 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 states when to use ('when you discover something worth carrying forward') and provides alternatives (recall, forget). Also clarifies scope and persistence rules, giving clear guidance on appropriate usage.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds behavioral detail about internal cascading lookups and efficiency, which provides useful context beyond the annotations.

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

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

Single paragraph with examples followed by supported types. Packed with information but could benefit from better structuring (e.g., bullet points for types) to improve readability.

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

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

Given no output schema, the description adequately explains return values for both entity types, including citation URIs. It covers main use cases but omits error handling or non-found scenarios.

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 expands on parameter meanings by detailing accepted input formats (ticker, CIK, name for company; brand/generic for drug) and auto-disambiguation for company type.

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 resolves user-spoken names to canonical identifiers, with specific examples like ticker, CIK, RxCUI. It distinguishes itself from siblings by indicating it should be used first when a name needs an ID.

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,' providing clear context. However, it doesn't specify alternatives or when not to use it beyond this general guidance.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it probes each entity with ai_visibility_check, returns a ranked list with score, confidence, signal density. This goes beyond annotations by describing the process and output, aiding agent understanding.

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

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

The description is two sentences plus a parenthetical example, totaling ~50 words. It front-loads the primary action and output, with no redundant statements. 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?

Given the tool complexity (4 params, 1 required, no output schema), the description fully explains the purpose, process (probing each entity), output format (ranked list with score, confidence, signal density), and effect of context. It is complete for effective selection and invocation.

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

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

Schema description coverage is 100%, so baseline is 3. The description mentions probing entities and ranking, but does not add new meaning to individual parameters beyond what the schema provides (e.g., 'models' supported values, '_apiKey' condition, 'context' disambiguation). No parameter-specific elaboration.

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

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

The description explicitly states the tool 'compares AI visibility across multiple entities side-by-side' with specific actions: probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized. It is distinct from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

The description provides a clear use case: 'competitive AI-marketing audits' with an example question. It implies when to use but does not explicitly state when not to use or name alternatives. Sibling list includes ai_visibility_check and compare_entities which could be alternatives, but the description does not reference them.

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 readOnly and idempotent hints. The description adds that partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. No contradiction with annotations.

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

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

Description is dense but every sentence adds value. Could be slightly more concise, but front-loads the main purpose and uses paragraph breaks effectively. No extraneous information.

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

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

Despite no output schema, the description details the return fields (summary block, per-advisory detail, links, recent alternatives) and warns about potential timeouts. Complete for a composite tool with two parameters.

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% and the description adds context: package is npm name, scoped packages accepted; version defaults to latest. It also explains how parameters feed into the composite check.

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 is a composite check for npm packages, covering license, advisories, bundle size, etc. The verb 'scan' and resource 'dependency' are specific, and the description distinguishes it from siblings by being a single call that fans out across multiple sources.

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

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

Explicitly says when to use: when an agent asks about safety, popularity, size, or cost of adding an npm package. Also mentions alternatives: for other ecosystems, use 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 are present (readOnlyHint, idempotentHint, etc.), but the description adds significant behavioral details beyond them: input truncation cap at 200K chars, embedding model (BGE-base-en), window size (500-char overlapping), similarity cosine, and output includes offsets. 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?

Every sentence serves a purpose. The description is front-loaded with the core function, followed by usage guidance, then technical details. No filler or repetition.

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

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

Despite no output schema, the description explains return values ('top-N passages with character offsets and similarity scores'). It covers input limits, query examples, and a caveat about truncation. Complete for a search tool with this complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description enhances understanding by elaborating on each parameter: 'text' is described as 'the document text to search inside', 'query' includes examples, and 'limit' is given a default range. This adds 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's purpose: 'Semantic search INSIDE a fetched record.' It uses specific verbs ('search') and resource ('fetched record'), and distinguishes it from sibling tools like ask_pipeworx_grounded by explaining how they pair. No ambiguity.

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: 'Use when the record is too big to cram into the prompt.' It also provides guidance on alternatives: 'Pairs with ask_pipeworx_grounded.' This helps the agent decide between tools.

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

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

The description discloses important behaviors: requires OAuth, rate limits (10/day for SMS), phone verification, webhook auto-disable after 10 failures, and delivery channel details. Annotations indicate idempotentHint=true, but the description does not mention idempotency; however, the annotations cover this.

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 efficiently packs detailed information about types, delivery, and constraints. Every sentence adds value, though it is slightly long but not wasteful.

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 creation tool with no output schema, the description covers all necessary aspects: types, parameters, delivery options, prerequisites, rate limits, and failure behavior. It is complete for the creation operation, though update behavior is not addressed.

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

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

Schema coverage is 100%, and the description adds significant value with concrete examples for each subscription type (e.g., sec_8k with ticker and items) and delivery channel options (webhook signing). This goes beyond the schema's brief 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 creates a proactive monitoring subscription and returns a subscription ID. It lists supported types and delivery channels, but does not explicitly distinguish from sibling tools like list_subscriptions or 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 mentions prerequisites (Pipeworx OAuth account) and provides type-specific examples, but does not explicitly state when to use this tool versus alternatives. It implies usage context but lacks explicit when-not or alternative tool references.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it returns example questions with tool+argument shapes and is meant for onboarding. It does not contradict annotations and provides additional 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 somewhat long but highly informative. It front-loads the purpose and includes all necessary details. While it could be slightly more concise, 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?

With no output schema, the description adequately explains the return format (category-bucketed example questions with tool+argument shapes). The tool is simple with one optional param, and the description covers usage, input, and output sufficiently.

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

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

Schema coverage is 100% with a single optional 'topic' parameter. The description adds meaning by listing possible topic values and explaining the effect of omitting vs. specifying the parameter, which goes beyond the schema's description.

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

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

The description clearly states the tool's purpose: it is the onboarding entry point that returns category-bucketed example questions. It distinguishes from siblings by being the first tool to call when unsure about Pipeworx's capabilities.

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

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

The description explicitly says to use this tool when you don't know what Pipeworx can do. It mentions calling with no arguments for full spread or with a topic to focus. Although it does not explicitly state when not to use it, the context 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?

Annotations note readOnlyHint=false (mutation), idempotentHint=true (safe to retry), destructiveHint=false (not destructive). The description adds that the row is deactivated (not deleted) and historical events remain via recent_alerts. This explains the non-destructive mutation behavior 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?

Three sentences, all valuable. Front-loaded with the core action. No unnecessary words.

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

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

For a simple 1-param mutation tool with no output schema, the description covers ownership enforcement, the soft-deactivation behavior, and where historical data remains. 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%, so the description adds no extra meaning beyond the schema. It restates that 'id' is a subscription id returned by subscribe. 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 states 'Cancel a subscription by id.' with a specific verb ('cancel') and resource ('subscription by id'). It clearly distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

The description explicitly says 'Ownership is enforced — you can only cancel your own subscriptions.' This provides a clear precondition and guidance on when to use. It does not explicitly mention alternatives or when not to use, but the constraint is clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: data source (SEC EDGAR + XBRL), return values (verdicts, structured form, actual value with citation, percent delta), and the fact that it condenses multiple steps. No contradictions with annotations.

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

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

The description is a dense single paragraph but contains all necessary information without redundancy. It is front-loaded with usage hints, then purpose, then details. Could be slightly better structured (e.g., bullet points) but remains effective and 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 lacking an output schema, the description comprehensively lists the return types (verdict, structured form, actual value, citation, percent delta) and domain limitations. For a single-parameter tool, this provides sufficient context for an agent to understand and invoke it 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?

The input schema has 100% description coverage for the single parameter 'claim', with a clear natural-language example. The description reinforces this with additional examples and specifies the expected format. It adds value beyond the schema by illustrating plausible 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?

The description explicitly states the tool's purpose: natural-language claim verification against authoritative sources for company-financial claims. It provides example invocation phrases ('Is it true that…', 'fact check') and clearly distinguishes it from sibling tools by specifying its domain and replacing multiple sequential calls.

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

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

The description advises using the tool whenever the agent needs to check factual accuracy of a user's statement. It clarifies the domain (company-financial claims for US public companies) but does not explicitly mention when not to use it or provide alternative tool names. The guidance is clear but lacks contrast with siblings.

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