Klaviyo

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

Annotations already declare readOnlyHint=true, destructiveHint=false, so safety is implicit. The description adds value by detailing that the tool sends requests to specific LLMs (Workers AI free, Anthropic with BYO key), the per-model response structure (score, confidence, signals, raw_response), and that users pay Anthropic directly. No contradictory or missing behavioral traits are evident.

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 (four sentences) and well-structured: first sentence introduces the core function, second explains model options and costs, third details the return format, and fourth lists practical use cases. Every sentence adds value without redundancy or fluff.

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

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

Given the tool's complexity (4 parameters, no output schema, rich annotations), the description covers the essential aspects: purpose, parameters, behavior (model selection, API key usage), and return structure. It lacks details on error handling or edge cases, but for a probe tool, the provided information is sufficient for competent usage.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by explaining that 'workers-ai' is the free default, 'anthropic' requires an API key, and that '_apiKey' is passed directly to Anthropic. It also clarifies that 'context' disambiguates entities. This extra context justifies a slightly higher score.

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 ('probe', 'score') and clearly identifies the resource ('LLMs for what they know about a business/brand/product/topic') and the output ('score 0-100 per model'). It distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by explicitly framing its use case as AI-marketing audits, pre-launch brand checks, and competitive monitoring.

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 (AI-marketing audits, pre-launch checks, monitoring) and explains the default free model and optional Anthropic integration with API key. However, it does not explicitly state when not to use it or compare it to alternatives among the sibling tools, missing an opportunity for stronger 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 substantial context: routes to 3,756 tools across 887 sources, returns structured answers with stable pipeworx:// citation URIs. This goes beyond the annotations in explaining the tool's behavior, especially the breadth and citation format.

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

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

The description is long but front-loaded with the key directive 'PREFER OVER WEB SEARCH'. Every sentence adds value, including examples and the mention of 3,756 tools. It could be slightly trimmed, but the density of useful information justifies the length.

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 (many sources, no output schema) and the annotations (openWorldHint), the description adequately covers what the tool does and how to use it. It mentions structured answers with citations, which compensates for the lack of output schema, though a bit more detail on the return format would improve completeness.

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

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

The schema describes the main parameter 'question' and its aliases with 100% coverage. The description provides examples of valid questions, which adds practical guidance beyond the schema's bare descriptions. It also mentions that the tool 'fills arguments', hinting at parameter handling, but does not detail specifics.

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

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

The description clearly states the tool answers factual questions using structured data from many sources, with examples. It distinguishes itself from web search by explicitly preferring this tool for such queries. The verb 'ask' combined with specific domains like 'SEC filings' and 'FDA drug data' makes the purpose unmistakable.

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 'PREFER OVER WEB SEARCH' for factual questions and lists many example queries. It gives concrete examples of when to use it ('what is', 'look up', etc.). However, it does not explicitly state when not to use it, though the context implies it's for any factual question.

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

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

Annotations already declare safe, idempotent read operation. Description adds significant behavioral detail: routing, argument filling, extraction using ONLY tool result, and specific refusal reasons. 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?

Front-loaded with key purpose and usage. Structured with success/refusal details and cost comparison. While lengthy, each sentence adds value and is well-organized.

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?

Tool has 6 parameters, no output schema, but description fully explains return shape (fields), failure modes, and comparison to sibling. Compensates for missing output schema with thorough behavioral documentation.

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

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

Schema coverage is 100% due to aliases, so baseline is 3. Description only says 'Your question in natural language' and lists aliases, adding no additional semantics beyond the schema.

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

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

Clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' with verb 'answers' and resource 'Pipeworx tools/data'. Distinguishes from sibling 'ask_pipeworx' by specifying its grounded extraction behavior.

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

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

Explicitly says when to use (high-stakes, quoted/cited answers) and when not (casual lookups, prefer ask_pipeworx). Also mentions extra cost, providing clear decision criteria.

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, openWorld), the description richly discloses behavioral traits: resolution confidence levels, parent event extraction, news fallback (429 handling), safety short-circuits (low-confidence, closed markets), wide-spread warnings, cancellation rule parsing, and dead market handling. 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 very long and dense, front-loading purpose but then providing extensive details (classifiers, fan-out examples, response shapes, resolver contract, etc.). While every sentence adds value, the length may be overwhelming for an agent to quickly parse. More structured formatting could improve scannability.

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

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

Despite lacking an output schema, the description thoroughly documents the response structure: result.market, result.analysis, result.evidence, resolver data (match confidence, alternatives), parent event extraction, news fields, and safety behaviors. It covers edge cases and provides enough detail for an agent to correctly interpret and use the output.

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

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

Schema coverage is 100%, but the description adds significant meaning: depth parameter explains 'quick' vs 'thorough' behavior, market parameter elaborates on input forms, include_raw provides rationale. The description adds value beyond the schema's built-in 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: research a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question) and output (evidence packet + comparison). It distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage through the detailed fan-out and analysis description.

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

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

The description provides explicit use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and examples of fan-out. However, it does not explicitly state when NOT to use this tool vs alternatives, though the context and sibling names imply differentiation.

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 valuable behavioral context: it pulls data from specific sources, handles off-calendar fiscal years, returns sorted results with citation URIs, and replaces 8–15 sequential lookups. No contradiction with annotations.

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

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

The description is a single dense paragraph, front-loaded with example queries. It contains no unnecessary words, though it could be broken into shorter sentences for readability. Every sentence earns its place, providing essential details about data sources, constraints, and output format.

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 entity types with different data sources, no output schema), the description is remarkably complete. It explains return data (financials for companies, adverse events/counts for drugs), sorting behavior, citation URIs, and even handling of off-calendar fiscal years. It compensates fully for the lack of an 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% with two parameters, 'type' and 'values', well-described. The description adds meaning beyond the schema by providing usage examples (tickers like AAPL, drug names like ozempic), clarifying the data pulled for each type, and restating constraints (2–5 items). This adds value for agent selection.

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 performs side-by-side comparisons of 2–5 companies or drugs, using specific data sources (SEC EDGAR for companies, FAERS for drugs). It distinguishes itself from sequential lookups by explicitly recommending this tool over multiple single-entity queries.

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

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

The description provides explicit guidance on when to use the tool ('ALWAYS PREFER over sequential single-pack lookups when comparing entities') and gives concrete example queries. It does not explicitly state when not to use it or name alternatives, but the context implies it is only for 2–5 entity comparisons.

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

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

Discloses key behaviors: question decomposition, parallel routing, extraction with verbatim evidence and confidence scores, explicit gap reporting, citation format, and expected duration. No contradiction with annotations (readOnlyHint confirmed).

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 informative but slightly verbose; could be tightened. However, it is well-structured and front-loads key information.

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

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

Given no output schema, description sufficiently explains return format (findings packet with evidence, gaps, citations). Covers prerequisites, expected time, and scope.

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

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

Adds value beyond 100% schema coverage by explaining depth options (quick=3, standard=5, thorough=8) and that thorough requires paid plan. Also reinforces that question can be broad/multi-part.

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

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

Description clearly states the tool's purpose as 'Grounded multi-source research in ONE call' and elaborates on decomposition and parallel routing. It distinguishes from sibling ask_pipeworx, which is for single lookups.

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

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

Explicitly advises using for broad/multi-part questions and references ask_pipeworx as alternative for single lookups. Also mentions prerequisites (Pipeworx account, paid plan for thorough depth).

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds critical behavioral details: returns top-N tools with full schemas and examples, ready to call without a second lookup. No contradictions.

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

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

The description is concise with two sentences, but the first sentence is a bit long with a list of domains. It is front-loaded with the core purpose. Could be slightly more compact, but it's effective.

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

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

Despite having no output schema, the description thoroughly describes what is returned (top-N tools with names, descriptions, and schemas) and why it's useful. For a discovery tool, this is complete and actionable.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context that 'query' accepts natural language but does not explain parameters beyond what's in the schema. The examples in the input schema are helpful but not part of the description itself.

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: discovering tools by describing a data or task. It lists numerous example domains and distinguishes itself from siblings like 'search_within' or 'deep_research' by explicitly being a tool discovery mechanism.

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

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

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set.' This gives clear usage context. It could be improved by mentioning when not to use it, but the guidance is strong 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?

Annotations already indicate read-only, idempotent, open world. Description adds detail about multiple data sources (SEC, XBRL, USPTO, news, GLEIF), return fields, soft-fail for patents, and fallback mechanisms. 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?

Single paragraph but packed with useful info; front-loaded examples. Could be better structured (e.g., bullet points for returned fields), but no wasted sentences.

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

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

Given no output schema, description covers return fields, sources, limitations, and fallbacks. Missing explicit output structure but lists components comprehensively.

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 3. Description repeats schema info (ticker/CIK, no names) without adding new parameter semantics, though it reinforces the usage constraints.

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 profiles US public companies using ticker or CIK, with examples like 'Tell me about X' and 'company profile for Microsoft'. It distinguishes from siblings by explicitly saying 'ALWAYS PREFER over chaining single-pack...' when a holistic view is needed.

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 (holistic view) and when not (names not supported, use resolve_entity first). Provides clear alternatives to chaining individual lookups.

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

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

Annotations already indicate destructiveHint and idempotentHint. The description adds only 'clear sensitive data', which is minimal additional behavioral context beyond what annotations provide.

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

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

Two concise sentences: first defines the action, second provides usage context. No wasted words.

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

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

For a simple delete operation with one parameter and no output schema, the description covers purpose, usage, and relevant annotations. No gaps.

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

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

Schema coverage is 100% with a clear description for the 'key' parameter. The description does not add new 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 'Delete a previously stored memory by key' with a specific verb and resource. It also distinguishes from siblings 'remember' and 'recall'.

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 describes when to use (stale context, task done, clear sensitive data) and pairs with alternatives (remember, recall), 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?

Annotations already declare readOnlyHint (true), openWorldHint (true), idempotentHint (true), and destructiveHint (false). The description adds behavioral context: it fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown format. This goes beyond annotations to explain the process and output.

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

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

The description is concise: two sentences followed by a list of use cases. The first sentence front-loads the purpose and target, the second explains process and output, and the list provides context. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity (fetch and extract), annotations covering safety, and full schema coverage, the description is fairly complete. It explains the purpose, process, output format, and use cases. Minor gaps (e.g., error handling) are acceptable for this level of complexity.

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

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

Schema description coverage is 100% (both 'url' and 'max_links' have descriptions). The description does not add additional parameter semantics beyond what the schema provides. Baseline score of 3 is appropriate as schema does the heavy lifting.

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

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

The description uses specific verbs ('generate', 'fetches', 'extracts') and clearly identifies the resource ('llms.txt file') and target ('any URL'). It distinguishes the tool from siblings by stating its purpose for AI crawler indexing and providing concrete use cases.

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

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

The description explicitly lists three use cases (getting a client's site indexed, drafting for own project, auditing competitors), giving clear context for when to use this tool. While it does not explicitly state when not to use or name alternatives, the use cases imply differentiation from sibling tools like 'scan_competitor_ai_presence'.

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=false. Description adds that it returns performance stats and send history, which is useful but not critical 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?

One sentence that is concise and front-loaded with the core purpose, followed by a list of returned fields. No wasted words.

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

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

Given the presence of an output schema (not shown but indicated), the description effectively communicates the tool's purpose and key return data without redundancy. Complete for a retrieval tool.

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

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

Schema coverage is 100% for both parameters. Description does not add extra parameter details beyond what the schema provides, 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 action ('Get a campaign's full details by ID') and lists specific attributes returned. It distinguishes from sibling tools like klaviyo_list_campaigns and klaviyo_get_profile.

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

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

Implies usage for retrieving details of a specific campaign by ID. No explicit when-not or alternative guidance, but the context of siblings makes it clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's job is lighter. It adds value by listing the specific data returned (email, name, phone, custom properties, list memberships, subscription status), providing context beyond the annotations. No contradictions.

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

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

The description is a single sentence with no wasted words. It efficiently conveys the purpose and key return fields, making it easy to scan.

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 an output schema, the description need not explain return structure. The tool is simple (2 required params), and the description covers purpose, parameter implication (by ID), and return fields. Fully adequate for selection and invocation.

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

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

Schema coverage is 100% (both `_apiKey` and `profile_id` are described). The description doesn't add extra detail beyond the schema; 'by ID' is already implied by the parameter name. Baseline of 3 is appropriate.

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

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

The description clearly states 'Get a contact's full profile by ID' with a specific verb and resource. It lists the fields returned (email, name, phone, custom properties, etc.), distinguishing it from sibling tools like klaviyo_list_profiles (list) and klaviyo_get_campaign (different resource).

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

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

The description does not explicitly state when to use this tool versus alternatives. It implies usage when you have a specific profile ID, but lacks guidance on when not to use or mentions of alternative tools like klaviyo_list_profiles for listing all profiles.

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 safe, idempotent, read-only operation. Description adds valuable context about output fields (campaign IDs, names, status, send dates, performance metrics). 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?

Single sentence that is front-loaded and contains no superfluous information.

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

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

With output schema present, description effectively covers key fields. Annotations and sibling tools provide adequate context. No gaps.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning to 'filter' parameter by listing specific status values (draft, scheduled, sent), which is absent in schema example.

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

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

Clearly states verb 'search' and resource 'campaigns' with filtering by status. Distinguishes from sibling 'klaviyo_get_campaign' which retrieves a single campaign.

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

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

Implies usage for listing multiple campaigns filtered by status, but does not explicitly state when not to use or mention sibling tools as alternatives.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds return fields but no further behavioral traits (e.g., pagination, rate limits, authorization requirements). Meets baseline but misses opportunity with annotations covering safety profile.

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 sentence, 12 words, no redundancy. Every word contributes to 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?

For a simple list tool with output schema present, description adequately covers purpose and return data. 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 covers 100% of parameters with clear description for _apiKey. Description adds no extra semantic value beyond what schema provides. Baseline 3 applies.

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

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

Description clearly states the tool retrieves all email lists and specifies the returned fields (IDs, names, subscriber counts, creation dates). It distinguishes from siblings like klaviyo_list_profiles and klaviyo_get_campaign by resource type.

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?

Context is clear: use to get all lists. No explicit when-not-to-use or alternatives needed given siblings target different resources (profiles, campaigns). Slight deduction for lack of any usage caveat.

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

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

Annotations already mark it as readOnly, idempotent, and non-destructive. The description adds value by mentioning pagination support and specific return fields (profile IDs, emails, names, properties), which goes beyond the annotations and informs the agent about response structure.

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

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

Two sentences, front-loaded with the primary action, no filler words. Every sentence contributes meaning, making it highly efficient.

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

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

With an output schema present (implied), the description covers essential behavioral aspects (search fields, return values, pagination). It does not explain default/max page size, but those are in the schema, so the description is sufficiently complete for a list/search tool.

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

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

Schema coverage is 100%, and the description adds context by listing searchable attributes (email, name, custom attributes) that map to the 'filter' parameter, and pagination which maps to 'page_size'. This enriches the schema definitions 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 searches contacts by email, name, or custom attributes, and specifies return fields (profile IDs, emails, names, properties). However, it does not explicitly differentiate from siblings like 'klaviyo_get_profile' (single profile retrieval) or clarify if it operates on a specific list or all profiles, leaving slight 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?

No explicit guidance on when to use this tool versus alternatives (e.g., 'klaviyo_get_profile' for individual profiles). The description implies a search/filter use case but does not state exclusions or prerequisites, making it adequate but not exemplary.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false. The description adds specific return fields and the optional parameter behavior, providing useful context beyond annotations.

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

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

Two sentences, front-loaded with purpose, followed by usage guidance. No wasted words, efficient and clear.

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

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

For a simple tool with one optional parameter and no output schema, the description fully explains what the tool returns and when to use it. Complete for its 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% with a clear description for the single parameter. The tool description does not add additional parameter details, so baseline 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions' with specific verb and resource, and lists the return fields, distinguishing it from sibling tools like subscribe 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 provides explicit context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It does not mention alternatives but implies when to use it.

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

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

Discloses rate limit (5 per identifier per day), that it's free and doesn't count against tool-call quota, and that the team reads digests daily. Annotations (readOnlyHint=false, destructiveHint=false) are consistent; description adds valuable behavioral context beyond annotations.

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

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

Five sentences cover all critical information without waste. Front-loaded with purpose, then usage, then constraints. Efficient 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 straightforward nature (submitting feedback), the description is fully complete: what, when, how, constraints, and impact on roadmap. No need for 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 covers 100% of parameters with descriptions. Description adds guidance on message content ('describe issue in terms of Pipeworx tools/packs') and elaborates on type enum meanings, providing marginal extra value.

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

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

Description clearly states purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist' and lists specific use cases (bug, feature, praise). It distinguishes itself from all sibling tools, none of which are feedback-related.

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 provides when-to-use scenarios for each feedback type, tells what not to do ('don't paste the end-user's prompt'), and mentions rate limits and quota. No ambiguity.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by explaining the data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h), which go 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 three focused paragraphs: purpose, usage guidelines, and technical details. Every sentence serves a purpose with no redundancy, and critical information is front-loaded.

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

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

Given the low complexity (single optional parameter, good annotations, no output schema), the description covers purpose, usage, data source, and caching. A minor gap is the lack of explicit return format details, but it's sufficiently complete for most agents.

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 parameter 'window' is well-defined with an enum and description. The description adds nuance by explaining that shorter windows surface what's hot now and longer windows show steady-state demand, enhancing decision-making for parameter selection.

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

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

The description clearly states the tool returns trending data from other AI agents, specifically top tools, packs, and call volume over configurable windows. It distinguishes itself from siblings like discover_tools or ask_pipeworx by focusing on popularity metrics.

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 three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use cases) which guide when to use it. While it doesn't explicitly mention when not to use it or alternatives, 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?

Description goes far beyond annotations, detailing monotonicity checks, partition-sum logic, Jaccard similarity anchor, placeholder filtering, and real-time CLOB depth fill check. Annotations are readOnlyHint and non-destructive, and the description aligns with that (no mutations).

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 well-structured with clear sections (REQUIRES, SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and front-loads the requirement. While detailed and informative, it is somewhat lengthy; however, every sentence adds necessary value for a complex tool.

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

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

Despite no output schema, the description covers response fields (opportunities[], partition_check, fill_check) and edge cases (skipped similarity, placeholder slugs, thin legs). It references a sibling tool for custom sizing, making it complete for an arbitrage detection 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?

100% schema description coverage already present. Description adds semantic value: explains event accepts URLs, details cross-event topic mode, and highlights Jaccard similarity threshold. Provides context beyond the schema's basic descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It defines two distinct modes (event and topic), distinguishing it from siblings like polymarket_edges or polymarket_fill_risk.

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

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

Explicitly requires one of event or topic and warns about calling with no args. Provides context for when to use each mode (event for specific market, topic for cross-event scanning) and includes fill-check guidance advising not to trade if realizable_edge_pp ≤ 0. References sibling tool polymarket_fill_risk for custom sizing.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, confirming safe read-only behavior. The description adds extensive behavioral context: details about the five model families, response segments (by_segment), diagnostic fields, Fed bet exclusion reasoning, caching strategy (1h KV cache keyed on knobs), and tradeable-edge knobs. This goes far beyond the annotations and provides comprehensive transparency.

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

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

The description is thorough but verbose. It dives into detailed model explanations (e.g., per-sport α, placeholder filters) that may not be necessary for agent selection. While the front-loading is good (first sentence covers purpose), the overall length could be reduced without losing essential guidance. The structure is clear but not tight.

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

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

Given the tool's complexity (9 parameters, no output schema, rich response structure), the description is remarkably complete. It explains the response top-level fields (by_segment, fed_candidates, _diagnostics), caching behavior, edge cases (Fed bets, thin partitions), and tradeable-edge filters. Everything an agent needs to understand when and how to use the tool is present.

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

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

Schema coverage is 100%, so the JSON schema already describes all 9 parameters. The description adds value by explaining how parameters like min_liquidity, max_spread_pp, and min_partition_leg_kelly interact with the models (e.g., 'Partition arbs always return kelly_fraction_half=0...'). This behavioral context helps agents use parameters effectively, exceeding what the schema alone provides.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses a specific verb ('scan'), identifies the resource ('Polymarket markets'), and the outcome ('opportunities'). The phrase 'built for what should I bet on today' further clarifies the intended use. This distinguishes it from sibling tools like polymarket_arbitrage.

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

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

The description provides a clear use case: 'built for what should I bet on today' and 'agents discover opportunities without paging hundreds of markets.' This implies when to use it (for daily betting opportunities). However, it does not explicitly state when not to use it or point to alternative tools like polymarket_arbitrage or polymarket_edge_tracker, which could be relevant for similar tasks.

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 provides extensive behavioral context beyond the annotations: it explains the snapshot-based nature, TTL, data gaps, decay computation from daily closes, and the structure of the response (tracked, expired, snapshot_dates). Annotations already mark it as read-only and idempotent, and the description adds significant detail about what the tool does.

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

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

The description is moderately long but efficiently structured with a clear purpose statement, argument details, response fields, and limitations. It is front-loaded and every sentence adds value, though it 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?

With 2 parameters and no output schema, the description thoroughly explains the return structure (tracked[], expired[], snapshot_dates[]) and their fields, along with limits on history depth. This is highly complete for the tool's complexity.

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

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

The input schema already provides full documentation for both parameters (defaults, clamps, options), so the description adds little beyond rephrasing. It mentions 'snapshot family' for window but the schema already enumerates values. 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 it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge age. It uses a specific verb phrase 'edge persistence and decay telemetry built from...' and distinguishes its scope from sibling tools like polymarket_edges by focusing on time-series 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 implies when to use this tool by contrasting a fresh wide edge with a 3-week-old one, indicating its value for assessing edge age. It does not explicitly exclude alternatives or mention when not to use it, but contextual hints are present.

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. The description adds context: mandatory one of market or event, detailed mode behavior, output fields including risk warnings (thin_legs, forced_directional_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?

The description is well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET) and front-loaded purpose. Every sentence adds substantive guidance without redundancy.

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

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

Despite no output schema, the description lists all return fields for both modes, explains risk concepts, and covers edge cases (clamping, default sizes). Adequate 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 descriptions cover all 4 parameters. The tool description adds value by explaining mode-dependent semantics (e.g., side default auto for basket, size_usd as settlement notional) and usage context beyond schema.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It lists specific outputs, setting it apart from sibling tools like polymarket_arbitrage.

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

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

Explicitly instructs to use this tool before polymarket_arbitrage signals or polymarket_edges trades above ~$500. Provides rationale: theoretical overround on thin books is not capturable, and partial basket fills create unhedged directional 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?

Adds significant detail beyond annotations: explains compatibility warning, temporal alignment, skipped cross types. No contradictions with readOnly/idempotent hints.

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

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

Well-structured with front-loaded purpose and clear sections, though somewhat verbose. Every sentence adds value 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?

With no output schema, description fully explains response fields (leg prices, spreads, safety fields). Covers all 3 parameters and usage context thoroughly.

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

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

Schema coverage is 100% and description adds concrete details (list of topics, example tickers/slugs) that clarify parameter usage and override behavior.

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

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

Clearly states it computes cross-venue spread between Kalshi and Polymarket. Distinguishes two modes but does not explicitly distinguish from sibling 'polymarket_arbitrage'.

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

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

Explains when to use topic vs explicit mode and warns that pre-mapped topics often yield warnings. Does not explicitly say when not to use the 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 and idempotentHint. The description adds context: scoped to identifier, behavior of listing when key is omitted, and examples of stored data. Does not contradict annotations and provides useful behavioral nuances.

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 concise sentences, no fluff. Purpose is front-loaded, followed by usage guidance and scoping. 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 simple tool (one optional param, no output schema, annotations present), the description covers purpose, usage, parameters, and scoping. It does not detail return format but implies list or value, which is sufficient. Slightly more detail on output could be added, but overall complete.

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

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

Schema has one optional parameter 'key' with description. The description elaborates on its use: retrieve a specific key or list all when omitted. Since schema coverage is 100%, baseline is 3; the description adds value by clarifying the dual behavior.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys when the key parameter is omitted. It provides specific use cases like user's target ticker or address, and distinguishes from siblings 'remember' and 'forget'.

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

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

Explicitly states when to use (to look up context stored earlier) and when not to (avoid re-deriving from scratch). Mentions pairing with remember and forget, and that it's scoped to an identifier. Provides clear guidance on omitting key for listing.

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, and non-destructive. The description adds valuable context: it explains the persistence of the feed, the effect of mark_read, and that polling is acceptable. 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 front-loaded with the core purpose and each subsequent sentence adds critical information (returned fields, filter options, mark_read effect, polling advice, alternate access). 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?

The description covers the return fields (source, citation_uri, payload) and key behaviors, but omits mention of the 'unread_only' parameter and does not detail the raw payload structure. Given no output schema, a bit more detail would improve completeness.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by providing an example filter type ('sec_8k'), clarifying that mark_read advances the read cursor, and confirming ISO timestamps for since. This adds value beyond the schema descriptions.

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

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

The description uses a specific verb ('pull') and resource ('fired events from your subscription feed'), and clearly distinguishes itself from siblings like list_subscriptions by focusing on events rather than subscription management.

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

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

The description provides context for usage (polling works fine, alternative endpoint for scripts/dashboards) but does not explicitly state when not to use or compare with sibling tools. It implies suitability for real-time retrieval and custom script integration.

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

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

Discloses behavioral traits beyond annotations: fans out to multiple sources, fallback behavior from GDELT to GNews, USPTO soft-fail, return structure with changes grouped by source and citation URIs. No contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false).

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

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

Single paragraph is dense but efficient, front-loading the purpose and covering sources, parameters, and alternatives. Slightly long but 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 complexity (multi-source), description covers purpose, usage, parameters, sources, alternatives, and return structure. No output schema exists, but return format is described, making it complete.

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

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

Explains each parameter beyond schema: type only supports company, since accepts ISO date or relative shorthand with examples, value is ticker or CIK. Adds context on how parameters are used.

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

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

Description clearly states the tool provides a change feed for a company over a time window in one parallel call. It uses specific verb-resource combinations and distinguishes itself from sibling tool entity_profile.

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 provides when-to-use examples (What's new with X, latest on Y) and when-not-to-use (use entity_profile for static profile). Also mentions alternative tool entity_profile.

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

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

Adds key behavioral details: scoping by identifier, persistence differences (authenticated vs. anonymous), and 24-hour retention. Consistent 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?

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

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

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

For a simple key-value store with no output schema, the description covers all necessary context: storage, scope, duration, and how to use with siblings.

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 already describes both parameters well. Description adds context with key-value pair concept but doesn't provide new syntax or constraints.

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

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

Description clearly states the tool saves data for reuse later, with specific examples. It distinguishes from siblings 'recall' and 'forget'.

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

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

Explicitly says when to use: when discovering something worth carrying forward. Provides examples and links to paired 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 indicate readOnlyHint, idempotentHint, destructiveHint=false. Description adds that each call cascades through several endpoints internally, which is useful behavioral context beyond annotations. No contradictions.

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

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

Single paragraph is dense but front-loaded with query examples. It efficiently conveys key information, though a bulleted list for types could improve scannability. No wasted sentences.

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

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

Given no output schema, description adequately explains return values for both types and internal cascading behavior. Lacks mention of error handling (e.g., unfound entities) or case sensitivity, but covers core usage well.

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

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

Schema covers 100% of parameters, so baseline is 3. Description adds rich semantics: explains return fields per type (ticker, CIK, citation for company; RxCUI, ingredient, brand for drug) and auto-disambiguation for company name, greatly enhancing parameter understanding.

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 a user-spoken name to a canonical identifier, with specific examples like tickers, CIKs, RxCUI. It distinguishes from siblings by noting it replaces 2-3 manual lookups, making the purpose unambiguous.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID', providing clear context for when to invoke. It lists supported types and their outputs, but does not explicitly state when not to use (e.g., if ID 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 declare the tool read-only, idempotent, and non-destructive. The description adds valuable context: it probes each entity with ai_visibility_check, returns a ranked list with score/confidence/signal density, and notes the first entity is treated as the 'subject'. No contradiction with annotations.

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

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

The description is concise but not extremely tight—it uses two full sentences plus fragments. It front-loads the purpose and benefits, but could be slightly more compact without losing information.

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

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

Given 4 parameters (1 required) and no output schema, the description covers the tool's purpose, behavior, return structure, and key parameter nuances. It could mention the entity count limit (2-8) from the schema, but overall it is sufficiently complete for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds semantics beyond the schema: it explains that 'entities' first entry is the subject, lists supported models with defaults and API key requirements, and clarifies that 'context' disambiguates common names.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, which is a specific verb+resource. It distinguishes itself from the sibling 'ai_visibility_check' by being a composite tool that probes and ranks multiple entities.

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

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

The description provides a concrete use case ('competitive AI-marketing audits') and an example question. However, it does not explicitly state when to prefer this tool over the single-entity sibling 'ai_visibility_check', though that is implied.

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

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

Annotations already declare the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds rich behavioral context: it fans out across two external services, mentions that 'Partial failures degrade gracefully', specifically notes that 'bundlephobia's first measurement on a new version can take 5-30s' and that 'sources_failed will list it if it times out'. This goes well beyond the annotations.

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

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

The description is detailed but well-structured. It uses dashes and lists to separate information, and the most important usage guidance comes first. Some sentences are longer but they are clear. It's not overly verbose, though it could be slightly tighter without losing meaning.

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

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

Despite no output schema, the description thoroughly explains the return format: a summary block with specific fields (is_latest, license, etc.), per-advisory detail, links, and a list of alternative versions. It also covers error behavior and partial failures. For a composite tool that combines multiple sources, this is highly complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context: for 'package', it gives an example of scoped packages ('@types/node'), and for 'version', it clarifies default behavior ('Defaults to the latest published version when omitted'). This adds some value, but the schema already describes the parameters adequately. So 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 starts with a clear verb phrase 'Composite should I add this npm package to my project check in ONE call', specifying the exact resource and action. It explicitly distinguishes from sibling tools by stating 'NPM ecosystem only in v1' and contrasting with deps.dev for other ecosystems. This makes the purpose unambiguous and distinct.

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

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

The description explicitly states when to use the tool: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides exclusions and alternatives: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'. This gives clear guidance on when and when not to use it.

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

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

Adds behavioral details beyond annotations: uses BGE-base-en embeddings, 500-char windows, 200K char cap with truncation flag. 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?

Information-dense with front-loaded purpose. Slightly lengthy but every sentence adds value. Could be trimmed slightly but effective.

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

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

Covers input constraints, output format (passages with offsets and scores), use cases, limitations (truncation), and pairing with sibling tool. No output schema, but description compensates fully.

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

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

Schema coverage is 100%, but description adds examples for query parameter, char limit for text, and explains underlying search mechanism, enriching understanding.

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

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

Description clearly states semantic search inside a fetched record, gives concrete examples (SEC 10-K, article), and distinguishes from sibling ask_pipeworx_grounded.

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

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

Explicitly says when to use ('record too big to cram'), pairs with sibling tool, and implies when not to use (whole document context unnecessary).

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

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

Description discloses behavioral traits beyond annotations: returns subscription id, details delivery channels with rate limits (10/day SMS cap, webhook auto-disable after 10 failures). Annotations already state idempotent and non-destructive; description adds valuable context.

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

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

Front-loaded with purpose, uses clear separation for types and delivery channels. Slightly long but every sentence adds value. Could be tightened slightly.

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

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

Covers all aspects: purpose, prerequisites, supported types with examples, delivery options with limits, and return value (subscription id). No output schema, but return value is explained. Complete for a subscription creation 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?

Despite 100% schema coverage, the description adds significant meaning: explains type-specific parameter formats (e.g., sec_8k with item codes, fred_series with series_id) and delivery subfield details (e.g., webhook HMAC signing, phone verification). This goes well beyond the schema.

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

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

The description explicitly states 'Create a proactive monitoring subscription to a live-data event stream', providing a specific verb and resource. It clearly distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Description specifies when to use (proactive monitoring) and prerequisites (Pipeworx OAuth account, no anonymous/BYO). It could improve by explicitly noting when not to use, but context is clear.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive nature. The description adds behavioral context: the tool returns 'category-bucketed example questions' from a 'live catalog of thousands of tools'. It does not contradict annotations.

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

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

The description is a single paragraph but is well-structured with example queries at the start, then purpose, then output, then usage guidelines. Every sentence adds value, though slightly longer than necessary.

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

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

For a simple tool with one optional parameter, no output schema, and rich annotations, the description is thorough. It explains the output format, when to use, the topic parameter, and even references sibling meta-tools.

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

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

Schema covers the single 'topic' parameter with examples. The description adds value by explaining the impact of omitting vs. specifying topic: 'Call with no arguments for the full spread, or pass topic to focus'. This goes beyond the schema description.

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

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

The description uses specific verbs and resources: 'onboarding entry point', 'returns category-bucketed example questions' with exact tool+argument shapes. It clearly distinguishes itself from sibling tools like ask_pipeworx and discover_tools by positioning itself as the first step for new users.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It provides clear context on when to use, though it could be more explicit about when not to use it (e.g., if user already knows what to ask).

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

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

Discloses that the row is deactivated (not deleted) and historical events remain available via recent_alerts, adding context beyond the annotations (destructiveHint=false, idempotentHint=true). No contradictions.

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

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

Two sentences, front-loaded with the main action. Every sentence provides essential 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?

Completely describes the tool's behavior, constraints, and side effects for a simple one-parameter tool with no output schema.

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

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

Schema coverage is 100% and parameter id already has a description. The description adds ownership context but no new syntax or format details beyond the schema.

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

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

Clearly states 'Cancel a subscription by id' with a specific verb and resource. Uniquely identifiable among siblings 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?

Specifies ownership enforcement ('you can only cancel your own subscriptions'), providing clear context for when the tool is appropriate. Lacks explicit alternatives but the constraint is sufficient.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, and the description elaborates on the return (verdict, structured form, actual value with citation, percent delta). 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 and examples, but the description is fairly long (multiple sentences) and could be slightly tighter. Still effective.

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

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

Covers purpose, usage, input format, output, and scope. Lacks details on error handling or limitations, but given the complexity of claim verification, it is reasonably 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 has 100% coverage with a good description for the single parameter. The description adds further examples and context, going beyond the schema.

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

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

The description uses specific verbs ('validate', 'verify', 'check') and identifies the resource as 'natural-language claim verification against authoritative sources'. It distinguishes itself from siblings by noting it replaces 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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides scope (company-financial claims). No ambiguity.

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