Octopus Energy

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint all true/false appropriately. The description adds value by disclosing the default free model, BYO key requirement for Anthropic with direct payment, and the return structure. 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 paragraph of 4 sentences, each carrying essential information. Main action is front-loaded, with no redundant or extraneous content.

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

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

Despite no output schema, the description outlines the return structure (per-model score, confidence, signals, raw_response + combined view). Combined with 100% schema coverage and clear usage guidance, the description is fully adequate for a tool with 4 parameters.

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

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

Schema description coverage is 100%, but the description adds semantic context: explains the default model, the purpose of context for disambiguation, and the apiKey's pass-through nature. This goes beyond the schema descriptions.

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

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

The description clearly states the action ('Probe one or more LLMs'), the resource ('what they know about a business/brand/product/topic'), and the output ('score visibility 0-100 per model'). This distinguishes it from sibling tools like scan_competitor_ai_presence.

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

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

The description provides clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the _apiKey parameter. However, it does not explicitly contrast with siblings or state 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?

Beyond annotations (readOnly, openWorld, idempotent), description adds that it routes to 3,744 tools, fills arguments, and returns structured answers with citation URIs, providing valuable behavioral context.

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

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

Front-loaded with key instruction, uses lists and examples effectively, but slightly lengthy; still, every sentence earns its place.

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

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

Despite no output schema, description explains output format (structured answer with pipeworx:// URIs). Tool is simple (one required param) and annotations cover safety, so description is complete.

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

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

Schema coverage is 100% with all parameters described, so description adds minimal extra meaning. Schema already explains the question parameter and aliases thoroughly.

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 is for answering factual questions about specific domains (SEC, FDA, economic data, etc.) and explicitly distinguishes from web search, naming its routing capability across thousands of tools.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and lists concrete query types and examples (e.g., current unemployment rate, SEC filings), making it clear when to use this tool versus alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true, indicating a safe, read-only, idempotent operation. The description adds significant context: it routes through many tools, uses only tool result content, returns explicit refusal reasons on failure, and incurs an extra call cost. No contradiction with annotations.

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

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

The description is front-loaded with the core purpose and mechanism, then details return format and usage guidance. It is slightly long but every sentence is informative. Minor redundancy with the return field listing could be trimmed, but overall efficient.

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

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

Despite no output schema, the description thoroughly explains the return object and all possible refusal reasons. It also covers the internal routing logic and cost implication. For a tool of this complexity, the description is complete and leaves 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 all parameters being aliases for 'question'. The description only says 'Your question in natural language', which adds no meaning beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting, but no extra parameter context is provided.

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

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

The description clearly defines the tool as a hallucination-resistant answer mode that extracts answers solely from tool results, distinguishing it from the sibling ask_pipeworx. It uses specific verbs like 'extracts' and 'fetches', and details the return structure, leaving no ambiguity about its function.

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

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

Explicitly states when to use (high-stakes reads where facts must not be invented, with concrete examples like financial verdicts and medical lookups) and when not to (prefer ask_pipeworx for casual lookups due to an extra LLM call cost). This provides clear decision criteria for an AI agent.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description goes far beyond, detailing short-circuit behavior (low-confidence matches, closed markets), wide-spread tradeability notes, cancellation rule parsing, and fan-out patterns. 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 long but highly informative, with each sentence providing necessary detail. It is front-loaded with the purpose and examples. While it could benefit from more structured formatting (e.g., bullet points), it is not verbose—every sentence earns its place given the tool's complexity.

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

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

Given no output schema, the description comprehensively covers response shapes, resolver contract, parent event extraction, news fields, safety checks, and resolution rule risk. It addresses edge cases and provides enough detail for an agent to use and interpret results 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% for 3 parameters. The description adds meaningful context beyond the schema: it explains how the market parameter accepts slug, URL, or question text; it explains the depth enum (quick vs thorough); and it details the include_raw parameter's effect on response size. This adds value beyond the schema definitions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the input types and the output goal (evidence packet + market-vs-model comparison), which distinctly separates it from sibling tools like polymarket_edges or validate_claim.

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

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

The description explicitly states use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides classifiers and fan-out examples, giving context for when this tool is appropriate. It does not explicitly list when not to use, but the clear use cases compensate.

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

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

Annotations declare readOnlyHint, idempotentHint, etc. Description adds specifics: company data from SEC EDGAR/XBRL with fiscal year handling, drug data from FAERS, sorted results, and citation URIs. No contradictions.

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

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

Description is well-structured with front-loaded triggers and examples. While comprehensive, it could be slightly more concise without losing essential guidance.

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?

Thoroughly covers behavior for both entity types, explains sorting, return format (paired data + URIs), and performance benefit over sequential lookups. No output schema needed.

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

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

Schema coverage is 100% with clear parameter descriptions. Description adds examples and context (e.g., off-calendar fiscal years) but does not fundamentally clarify ambiguous parameter meanings beyond schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs in one parallel call, with explicit trigger phrases. It distinguishes from sibling tools like entity_profile which do 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 instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides natural language triggers, making when-to-use unambiguous.

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, read-only, idempotent behavior. Description adds valuable context: parallel decomposition across 3,744 tools/884 sources, explicit gaps for unanswered facets, pre-verified facts, expected time 15-60s, and citation format. No contradiction with annotations.

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

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

Description is somewhat long but well-structured, with each sentence providing distinct value. It's front-loaded with key purpose and ends with usage guidance and prerequisites. Could be slightly more concise 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?

Given the tool's complexity (2 params, no output schema), the description is remarkably complete. It explains the process, output format (structured findings packet with citation, confidence, gaps), prerequisites, and time estimate. Addresses all key aspects.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning by explaining depth values: quick=3, standard=5 (default), thorough=8 (paid plans). 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?

Description clearly states it performs grounded multi-source research by decomposing questions and routing to many tools/sources in parallel. It distinguishes from sibling tools like ask_pipeworx, specifying its unique capability for broad or multi-part questions.

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 when to use this tool (broad/multi-part questions) and when to use ask_pipeworx instead (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 safe read-only behavior; description adds that it returns top-N tools with full schemas and examples ready to call, providing useful 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?

The description is informative and front-loaded, but slightly verbose. However, all sentences contribute value, so it remains 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 complexity (multiple domains, rich output), the description thoroughly explains what it does, what it returns, and how to use it, making it complete.

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

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

Schema coverage is 100%, so description doesn't need to add much. It mentions query and limit but doesn't provide new information beyond what schema already specifies.

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 'Find tools by describing the data or task' and lists many domains and use cases, clearly distinguishing it from sibling tools that perform specific actions.

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?

It includes explicit guidance to 'Call this FIRST when you have many tools available and want to see the option set,' and describes when to use it for browsing/searching.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: it fans out across multiple sources, lists exact return fields (CIK, filings, fundamentals, patents, news, LEI), notes the patents API sunset and soft-fail behavior, and explains the parallel call nature. No contradictions with annotations.

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

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

The description is somewhat long but every sentence adds value. It is front-loaded with usage examples and a preference statement. Could be slightly more concise by removing redundant examples, but overall structure is 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?

Given the tool's complexity (multi-source profile), the description covers all necessary aspects: what it does, what data it returns, limitations (patents sunset, names unsupported), and source details. It is complete for an agent to understand and invoke correctly, especially with the rich annotations and 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 both parameters described. The description adds meaning: for 'type' it clarifies that only 'company' is supported and other types are coming; for 'value' it specifies ticker or zero-padded CIK format and reiterates names not supported, which goes beyond the schema's enum and description.

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

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

The description explicitly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It provides multiple example queries and clearly distinguishes from sibling tools like resolve_entity by noting that names are not supported and that this tool should be preferred over chaining individual 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?

The description gives clear when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also states when not to use: 'names not supported (use resolve_entity first if you only have a name).' This explicitly guides the agent.

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

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

Annotations already declare destructiveHint and idempotentHint, lowering the bar. The description adds 'clear sensitive data' as a reason but does not mention behavior on missing keys or confirmation of deletion, which a simple delete tool might need.

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

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

Three sentences with no redundant content: first sentence states purpose, second gives usage guidelines, third links to sibling tools. Front-loaded and efficient.

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

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

For a simple one-parameter delete tool with no output schema, the description covers purpose, usage, and sibling relationships. Missing edge case behavior (e.g., key not found) but otherwise complete.

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

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

Schema coverage is 100% with a clear description for 'key'. The main description adds no further parameter details beyond stating it operates 'by key', so baseline 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key', specifying the verb and resource. It distinguishes from siblings by mentioning 'remember and recall' as pairings, making the tool's role in the group obvious.

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 three use cases: when context is stale, task is done, or to clear sensitive data. It also advises pairing with 'remember' and 'recall', giving clear context and alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false, which cover the main behavioral traits. The description adds that it fetches the page and extracts info, but does not elaborate on error handling, rate limits, or security beyond what annotations imply.

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 at four sentences, front-loaded with the main action, and includes bullet-style use cases. 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?

The tool has two well-documented parameters and clear annotations. The description covers purpose, process, output format, and use cases. It lacks mention of error handling or URL validity, but for a simple read-only tool this is acceptable.

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 coverage is 100%, with clear descriptions for both parameters (url and max_links). The description adds no parameter-specific details beyond the schema, so baseline of 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifies the verb 'generate' and resource 'llms.txt', and distinguishes it from all sibling tools by its unique function.

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

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

Explicit use cases are given (e.g., getting a client's site indexed, drafting for own project, auditing competitors), providing clear context for when to use. No explicit when-not-to-use or alternatives, but the sibling tools are sufficiently different that this is not a major gap.

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

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

Annotations already declare readOnly, idempotent, and open world hints. The description adds value by explaining the return format and the group_id's role in tariffs, without contradicting annotations.

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

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

Two sentences that are front-loaded and contain no superfluous information. Every sentence earns its place.

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

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

For a simple tool with one parameter, rich annotations, and no output schema, the description fully informs the agent about what the tool does and returns.

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

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

Schema coverage is 100% and the schema already includes a description for the 'postcode' parameter. The description does not add new parameter information beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description uses the clear verb 'Map' and specifies the resource (UK postcode to GSP group), along with the output format. It distinguishes from siblings as no other tool has similar functionality.

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

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

The description provides context about GSP usage in tariffs, but does not explicitly state when to use vs. alternatives or any exclusions. Usage is implied but lacks direct guidance.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description complements this by specifying the return format (count, results with fields) and scope (UK energy products), which is useful context beyond annotations.

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

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

Two sentences: first states purpose with examples, second specifies return structure and usage. No redundant information. Front-loads the key action and resource.

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 enumerates return fields and mentions product_details. With 4 optional parameters fully described in schema and clear annotations, the description is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% with each parameter having a description. The description merely mentions 'Optional filters narrow the list' without adding details beyond the schema, 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 explicitly states 'List Octopus Energy UK energy products' with specific examples ('Agile Octopus', 'Flexible Octopus'). It clearly separates from sibling 'product_details' by noting that the 'code' from this tool feeds into that one.

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

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

The description explains that optional filters narrow the list and identifies that the resulting 'code' is used for product_details. While it does not explicitly list when to avoid using it, the guidance is sufficient for an agent to understand its role.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds caller-specific scope and return fields, which is useful but doesn't contradict annotations. A small but not necessary addition.

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 two concise sentences: first states purpose and output, second gives usage guidance. No unnecessary words, efficiently 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 simple tool with one optional parameter and no output schema, description is complete: lists returned fields, states caller scope, and provides usage guidance. 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 only parameter. Description does not add new semantics beyond what the schema already provides, scoring baseline 3.

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

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

Description clearly states it lists the caller's active subscriptions and specifies the returned fields. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by focusing on listing rather than creating or removing.

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

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

Description explicitly advises when to use: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and implies alternatives (subscribe, unsubscribe).

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

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

Annotations are all false, so no hints. Description adds transparency about rate limits (5 per identifier per day), that it's free and doesn't count against quota, and the team's reading habit. This adds value 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?

Concise at 4 sentences, front-loaded with purpose, then guidelines, constraints. 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 (feedback submission) and no output schema, the description covers purpose, usage, constraints, and expected user behavior. 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 description coverage is 100%, so baseline is 3. The description repeats some enum meanings but adds usage context (e.g., message length limit is in schema). No significant additional meaning beyond schema.

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

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

The description states the tool's purpose clearly: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies feedback types (bug, feature, data_gap, praise), distinguishing it from query/action tools in the sibling list.

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

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

Provides explicit guidance on when to use (for bugs, features, data_gaps, praise) and what not to do ('don't paste the end-user's prompt'). Also mentions rate limits, free usage, and impact on roadmap, giving clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds useful context about caching (5min-1h), data derivation (CF analytics-engine), and that no PII is included. No contradictions.

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

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

Description is concise with no wasted words. Front-loaded with the main output, followed by use cases and technical details in a logical order. Every sentence adds value.

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

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

For a simple tool with one optional parameter, the description is complete. It explains what is returned, use cases, data source, and caching. No output schema needed given the clarity.

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

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

Schema has 100% coverage with enum description. Description adds value by explaining the semantic difference between windows (shorter for hotness, longer for steady-state) and lists use cases. Exceeds baseline of 3.

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

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

Description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses a specific verb ('Returns') and resource ('top tools, top packs, and total call volume'), and distinguishes from siblings like ask_pipeworx and discover_tools.

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

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

Explicitly lists three use cases: discovering hot data sources, confirming a popular tool, and seeing alignment. Also provides context on when to use shorter vs longer windows and notes the data source and caching behavior, helping the agent decide when to invoke this tool.

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

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

Annotations indicate read-only, open-world, idempotent, and non-destructive. The description adds significant behavioral context: internal checks (monotonicity, partition_check), Jaccard similarity threshold, partition filter, fill-check logic, and response structure. No contradiction with annotations.

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

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

The description is well-structured with clear sections and front-loaded requirements. However, it is somewhat lengthy (c. 180 words) with some repetition of technical details. Could be tightened slightly, but overall informative and 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?

Despite lacking an output schema, the description thoroughly explains the response structure (opportunities array and partition_check object). It covers edge cases (fill-check, placeholder filtering) and references related tools, making the behavior fully understandable for an agent.

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

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

Schema coverage is 100%, but the description provides far more detail: examples of event slugs and topic phrases, explanation of single-event vs cross-event modes, and how internal algorithms use each parameter. This goes well beyond the schema descriptions.

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

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

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

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

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

Explicitly states requirements ('REQUIRES one of event or topic'), provides recommendations for each mode, and includes when-to-use guidance. Also warns about fill-check conditions where trading should be avoided, and references an alternative tool 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, openWorldHint, idempotentHint, destructiveHint. The description adds substantial behavioral context: it details the three model families, caching behavior (1h at KV level keyed on all knobs), the output structure with diagnostics, and why Fed bets are excluded. This goes far beyond annotations, providing full 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 verbose and dense, but it is well-structured with clear sections (overview, model families, knobs, response). It front-loads the purpose. While some detail could be trimmed, the complexity of the tool justifies the length. Minor marks for verbosity.

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

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

Given the complexity (9 parameters, no output schema, annotations present), the description is remarkably complete. It explains the output structure (by_segment, diagnostics), caching, edge cases (placeholder-slug filter, partition overround behavior), and data limitations (Fed bets). No gaps are evident.

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 detailed parameter descriptions. The tool description adds overall context but does not substantially enhance parameter understanding beyond what the schema already provides. For example, min_partition_leg_kelly is already explained in the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with a specific use case 'what should I bet on today'. It uses a specific verb-resource combination. However, it does not explicitly differentiate from the sibling tool 'polymarket_arbitrage', slightly reducing clarity.

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

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

The description implies usage via 'Built for what should I bet on today' and mentions Fed bets are excluded, but it lacks explicit guidance on when not to use the tool or alternatives. There is no comparison with siblings like polymarket_arbitrage or polmarket_kalshi_spread. Usage context is implied but not fully explicit.

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. The description adds significant behavioral context: data comes from daily snapshots of polymarket_edges, decay is computed from |edge_pp_net| daily closes, snapshots may have gaps on cache-miss days, and history is bounded by TTL. No contradictions with annotations.

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

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

Description is fairly long but well-structured, starting with the core question, then detailing response fields, and ending with limitations. Front-loads the key purpose. Some technical density could be trimmed, but overall efficient.

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

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

With no output schema, the description thoroughly explains the response fields (tracked[], expired[], snapshot_dates[]) and their meanings. It covers behavior, data sources, limitations, and parameter effects. Given moderate complexity and low parameter count, the description is complete.

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

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

Schema provides descriptions for both parameters (days and window) with defaults and allowed ranges. Description adds context for days (default 14, clamp 2-30), window (snapshot family, default '1wk'), and explains the response structure. With 100% schema coverage, baseline is 3; description adds modest extra meaning.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' and explicitly answers the question 'how long has this edge existed and is it shrinking?' It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis rather than current snapshots.

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

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

The description explains when to use this tool (to understand edge history vs. current edges) and implicitly when not to use it (for current edges, use polymarket_edges). It also notes limitations like the 60-day snapshot TTL and that data is daily, not intraday.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint false. The description adds behavioral context such as the risk of partial fills and the interpretation of results (verdict: clean|degraded|cannot_fill), which goes beyond the annotations.

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

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

The description is well-structured with sections for single-market and basket modes, and a use-case recommendation. It is front-loaded with the main purpose. While somewhat lengthy, every sentence adds necessary context, and the structure enhances readability.

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

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

Given the complexity of the tool with two modes and many return fields, and no output schema, the description lists all significant return fields (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict, theoretical_sum, realizable_sum, etc.). This provides a good understanding of what the tool outputs, though exact types are missing.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the difference between market (single-market) and event (basket) parameters, the default side behavior for baskets, and the interpretation of size_usd (max spend vs target proceeds vs settlement notional). It also mentions a clamp range not in the schema.

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

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

The description clearly states the tool's purpose as a 'Realizable-vs-theoretical edge check against live CLOB order-book depth', specifies single-market and basket modes, and distinguishes it from sibling tools like polymarket_arbitrage and polymarket_edges by instructing to use this tool before acting on signals from those.

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: before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500. It also explains why by highlighting risks like uncapturable theoretical overround and partial basket fills converting arbs into unhedged positions.

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 extensive behavioral context beyond annotations (readOnlyHint, etc.): explains compatibility warnings, temporal alignment, skipped leg comparisons, and when spreads are meaningful. No contradiction with annotations.

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

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

Description is quite long with multiple paragraphs. While front-loaded with purpose, it includes many details that could be condensed. Could be more concise.

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

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

No output schema, so description explains response structure (leg prices, matched spreads, safety fields) and compatibility warnings thoroughly. Complete for understanding what the tool returns.

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

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

Schema has 100% coverage. Description adds meaning by listing topic shortcuts, explaining override behavior, and giving examples. Adds value beyond schema.

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

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

The description clearly states 'Cross-venue spread between Kalshi and Polymarket' and explains two modes (topic shortcuts vs explicit pairing). It distinguishes from siblings by focusing on cross-venue spread.

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

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

Describes when to use (finding cross-venue spreads), provides two modes, and warns that most pre-mapped topics return compatibility warnings. However, it does not explicitly mention alternatives like polymarket_arbitrage for within-venue spreads.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds valuable behavioral context: includes specifics about tariff entries (codes, standing charges, unit rates inc/exc VAT) and tariff set types returned. No contradictions with annotations.

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

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

Two sentences, front-loaded with purpose, then details. No unnecessary words. Every sentence provides useful information.

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

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

With no output schema, description explains return values well: tariff types, entry components. Also guides agent to next step (tariff_unit_rates). Minor omission: structure of nested objects not fully detailed, but sufficient for agent understanding.

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

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

Schema coverage is 100% with a clear description of productCode. The description does not add new parameter info but reinforces that it expects one product code. Baseline 3 is appropriate as schema handles parameter meaning adequately.

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

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

Description clearly states it returns full detail for one product code with tariffs broken out by region and payment method. It differentiates from sibling tools like tariff_unit_rates and list_products by specifying the scope (single product) and the output (tariff entries with codes, charges).

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

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

Explicitly tells agent to use tariff_unit_rates with the tariff code, guiding when to use this tool vs. its sibling. Does not provide exclusions for other siblings but gives clear context for its specific use case.

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 meaningful behavioral context beyond annotations: scoping to identifier (anonymous IP, BYO key hash, account ID) and pairing with remember/forget. 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?

Four sentences, each serving a distinct purpose: purpose, usage, scope, and pairing. No wasted words, well-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?

Complete for a simple single-parameter tool without output schema: covers what, when, how, and relationships 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?

Schema already describes parameter well, but description reinforces usage (omit to list all keys) and adds real-world examples, adding value beyond schema.

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

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

The description clearly states it retrieves a saved value or lists all keys, with specific verb-resource pairing. It distinguishes itself from sibling tools 'remember' and 'forget' by name.

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

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

Provides clear context for when to use (look up stored context) and mentions scoping, but does not explicitly state when not to use or name alternatives besides remember/forget.

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 as true. The description adds context that mark_read:true flags events as read, altering future calls. This does not inherently contradict idempotentHint (maybe applies to reads without mark_read) but provides necessary behavioral nuance beyond annotations.

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

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

The description is a single paragraph of five sentences, each carrying meaningful information. It is front-loaded with the core purpose and follows with parameters and usage tips. No redundant or vague statements.

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

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

Given the tool has 5 parameters, no output schema, and annotations present, the description covers what is returned (source, citation_uri, raw event), filtering options, and state management via mark_read. It also mentions an alternative access method. This is sufficient for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing an example for type ('sec_8k'), explaining mark_read's effect on subsequent calls, and clarifying the 'since' parameter usage with ISO timestamps. This enhances understanding beyond schema descriptions.

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

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

The description clearly states the tool pulls fired events from the subscription feed and returns recent alerts. It specifies key fields (source, citation_uri, raw event payload) and distinguishes itself from sibling tools like list_subscriptions by focusing on alert retrieval with filtering and state 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 clear usage guidance: it mentions polling works fine and points to an alternative API endpoint (GET registry.pipeworx.io/alerts.json) for scripts/dashboards. While it doesn't explicitly state when not to use or compare with siblings, the context is sufficient for appropriate usage.

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

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

Beyond annotations (readOnly, idempotent, openWorld), the description details the multi-source fanned-out approach (SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail), the return structure (changes[] grouped by source + total_changes + citation URIs), and specific behavior like rate-limiting handling.

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

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

The description is fairly long but well-structured, starting with example queries in plain language, then technical details. Every sentence adds value, but some rephrasing could improve brevity without losing information.

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

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

Given the tool's complexity (multiple sources, fallback, soft-fail) and no output schema, the description thoroughly explains behavior, parameter formats, and return structure. Users have enough information to use the tool correctly and understand its limitations.

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

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

The schema covers 100% of parameters with descriptions. The description adds practical context for 'since' (e.g., 'Use 30d or 1m for typical monitoring') and clarifies that 'value' accepts ticker or zero-padded CIK. It enhances understanding without being strictly necessary.

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

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

The description clearly states it provides a change feed for a company over a specified time window, using specific verbs ('What's new with X', 'latest on Y'). It explicitly distinguishes from the sibling tool entity_profile by directing users to use that tool for static profiles regardless of window.

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 includes explicit when-to-use guidance with example queries, and explicitly states when not to use it ('Use entity_profile instead when you want the static profile'). It also explains the fallback behavior between GDELT and GNews.

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

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

Annotations provide idempotentHint=true and destructiveHint=false, but the description adds valuable context: scoped by identifier, persistence rules (permanent for authenticated, 24h for anonymous), and pairing with recall/forget. No contradictions.

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

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

The description is concise: 4 sentences covering purpose, usage, behavior, and pairing. Front-loaded with the core action ('Save data...') and no unnecessary words. 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 2 parameters, no output schema, and annotations covering idempotence and non-destructiveness, the description adequately covers persistence, scoping, and lifetime. It could mention that the tool is a write operation (implied) but it's clear enough for selection.

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

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

Schema has 100% coverage with descriptions for key and value, but the description enhances meaning by providing naming conventions and examples for values, e.g., 'findings, addresses, preferences'. This goes beyond the schema's generic descriptions.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later' with concrete examples (e.g., 'resolved ticker, target address'). It distinguishes from siblings like 'recall' and 'forget' by mentioning pairing, and the name 'remember' aligns perfectly.

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 ('when you discover something worth carrying forward') and implies pairing with recall/forget. However, it does not explicitly contrast with other tools like 'search_within' or 'entity_profile' that might store similar data, missing clear exclusion 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?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context, such as cascading through multiple lookup endpoints internally and replacing manual lookups, which aids agent understanding.

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

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

The description is moderately detailed but efficiently structured, starting with concrete query examples to establish purpose, then providing usage instruction, supported types, and return format. Slightly verbose but each sentence adds value.

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

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

For a tool with no output schema, the description fully explains return values for each type (company ticker/CIK/name/citation; drug RxCUI/ingredient/brand/citation). It covers auto-disambiguation and internal cascading, making it self-contained for agent use.

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

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

Schema coverage is 100%, and the description significantly enriches parameter meaning: for 'type' it lists supported entity types with details, and for 'value' it provides concrete examples and formats (e.g., ticker, CIK, names for companies; brand/generic for drugs).

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 names to official identifiers, with specific examples like 'What's the ticker for...' and 'find the CIK for...'. It explicitly identifies the resource (names to IDs) and distinguishes from siblings by suggesting 'Use FIRST whenever you have a name but need an ID.'

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

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

The description provides clear guidance on when to use the tool ('Use FIRST whenever you have a name but need an ID') and gives typical query patterns. However, it does not explicitly mention alternatives or when not to use, though context implies it.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral details: probes each entity with 'ai_visibility_check' and returns a ranked list with 'score, confidence, signal density per entity', and treats the first entity as the 'subject' for narrative. No contradiction with annotations.

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

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

Two sentences with high information density, front-loaded with the core action, and ends with a concrete example. No wasted words.

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

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

Given the tool's complexity (multiple models, ranking, 4 parameters) and no output schema, the description adequately explains the return format and key behavior. However, it omits details about the '_apiKey' parameter usage beyond what the schema states.

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 minor context (first entity treated as subject), but largely repeats schema information. No significant additional meaning beyond schema descriptions.

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

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

The description uses a specific verb ('Compare AI visibility') and resource ('multiple entities side-by-side'), clearly differentiating from sibling tools like 'ai_visibility_check' (single entity) and 'compare_entities' (generic).

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

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

The description provides a clear use case ('competitive AI-marketing audits') and an example question, but does not explicitly state when not to use or directly mention alternatives beyond noting it probes 'ai_visibility_check'.

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

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. The description adds partial failure behavior for bundlephobia timing out and graceful degradation. It could mention auth or rate limits but is still strong.

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

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

Single paragraph with front-loaded purpose, use cases, return fields, ecosystem scope, and failure behavior. Efficient and well-structured, though could be slightly more concise.

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

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

Given no output schema, the description comprehensively covers purpose, use cases, return fields, limitations, ecosystem, and failure handling. 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?

Schema coverage is 100% with descriptions. The description adds that scoped packages are accepted and defaults for version, which goes beyond the schema's descriptions.

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

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

The description clearly states the tool is a composite check for adding an npm package, covering license, advisories, version history, and bundle size. It distinguishes from siblings by specifying NPM ecosystem and listing specific use cases.

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

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

Explicitly says 'Use whenever an agent asks...' and provides limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This gives clear when-to-use and when-not-to-use guidance.

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

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

Discloses implementation details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, and a 200K char cap with truncation and flagging. Fully consistent with annotations (readOnly, idempotent, non-destructive).

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 purpose and covers key points in about 4 sentences. It is efficient but includes a bit of extra detail (e.g., embedding model) that could be considered supplementary.

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 specifies return fields (top-N passages with offsets and similarity scores) and behavior on overflow. It covers input limits, pairing advice, and internal mechanics, making it comprehensive for a 3-param tool.

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

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

Schema coverage is 100% with good parameter descriptions. The tool description adds context (e.g., examples of text and query) but doesn't significantly extend the schema's meaning. 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 'Semantic search INSIDE a fetched record' with a specific verb-resource pair. It distinguishes itself from sibling tool ask_pipeworx_grounded by explaining their complementary roles.

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

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

Explicitly states 'Use when the record is too big to cram into the prompt' and contrasts with ask_pipeworx_grounded for grounding over relevant passages. Provides clear context on when to use.

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

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

Annotations provide readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: returns subscription ID, requires OAuth, details delivery channel limits (10/day SMS cap, phone verification, webhook auto-disable after 10 failures, signing secret one-time reveal). This exceeds the information from annotations alone.

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

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

The description is a single paragraph that packs essential details efficiently. While front-loaded with core purpose, the density of examples and constraints might be slightly overwhelming. A structured format (e.g., bullet points) could improve readability, but it remains concise without waste.

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

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

Considering the complexity (nested objects, no output schema), the description covers prerequisites, type-specific parameters, delivery channels with limitations, and even error behavior (webhook auto-disable). It provides sufficient context 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% and includes field descriptions, but the description enriches with concrete examples for each type (e.g., items codes, topic, series_id) and delivery options (email, SMS, webhook with constraints). However, not all enum values (patent_grant, clinical_trial) are exemplified, slightly reducing completeness.

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

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

The description clearly states the verb 'Create' and resource 'subscription' for proactive monitoring. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation. The specific types and delivery channels are mentioned, making 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 includes prerequisites (Pipeworx OAuth account) and explains when to use this tool for setting up proactive monitoring. It mentions the always-on feed as an alternative delivery method and implies that list_subscriptions or recent_alerts can be used for retrieval, but does not explicitly contrast with all sibling tools. Overall, the guidance is clear but could be more explicit about choosing this over alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds context that it returns example questions with exact tool arguments from a live catalog, and that it can be used to learn meta-tools. This provides additional behavior beyond the annotations.

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

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

The description is somewhat lengthy with many trigger phrases in the first sentence, but it is well-structured and front-loaded with the key purpose. Every sentence provides useful information, though some trimming could improve conciseness.

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

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

Given the tool's low complexity (1 optional param, no output schema) and the rich annotations, the description fully covers its purpose, usage, and output shape. It explains the category structure and how to use it for onboarding, leaving no gaps.

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

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

Schema description coverage is 100% for the single optional parameter. The description reinforces the optional nature and lists examples, but does not add significant new meaning beyond what the schema already 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 that the tool returns category-bucketed example questions with exact tool calls, and it serves as an onboarding entry point. It distinguishes itself from siblings like ask_pipeworx and discover_tools by focusing on suggestions rather than direct queries or tool listings.

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

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

The description explicitly says to use this tool first when the agent doesn't know what Pipeworx can do, and it explains how to optionally pass a topic to focus. It doesn't explicitly state when not to use it, but the guidance is clear and useful.

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-only behavior. The description adds specific response format, units, sorting order, and the effect of the kind parameter, providing significant 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?

The description is a single, focused paragraph that front-loads the primary purpose. Every sentence adds information without redundancy. The return format is presented in a compact, readable way.

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

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

Given the tool's complexity (6 parameters, no output schema), the description covers essential behavior: return format, sorting, default window, and kind variation. It lacks mention of error handling or empty results, but is otherwise sufficient.

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

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

Schema coverage is 100%; the description adds value by explaining the kind parameter's role in switching between rates and standing charges, and provides an example for tariffCode. It also gives date format examples. This supplements the schema well, though not exhaustively.

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 a time-series of electricity unit rates for a specific tariff, with a concrete example for Agile. It distinguishes from standing charges via the kind parameter, making the purpose unambiguous.

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

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

The description explicitly guides when to use default vs. period bounds and how to switch to standing charges. However, it does not contrast with sibling tools like product_details, missing explicit when-not-to-use guidance.

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

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

Discloses that the row is deactivated, not deleted, and historical events remain available. This adds context beyond annotations (destructiveHint=false, readOnlyHint=false). The ownership enforcement is also noted.

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

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

Two concise sentences with the key action upfront. Every sentence adds value: first states the action, second clarifies ownership and behavior. No wasted words.

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

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

For a simple tool with one parameter, no output schema, and clear annotations, the description is fully complete. It covers the action, constraint (ownership), and behavioral nuance (deactivation vs deletion).

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

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

Schema coverage is 100% with a description for 'id' as 'Subscription id (uuid) returned by subscribe.' The description adds context by mentioning 'by id' and the source of the id. This adds some value beyond the schema.

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

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

The description clearly states 'Cancel a subscription by id.' It uses a specific verb (cancel) and identifies the resource (subscription by id). This distinguishes it from 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?

Explicitly states ownership enforcement: 'you can only cancel your own subscriptions.' This provides a clear when-to-use constraint. It does not explicitly mention alternatives, but the context is clear enough.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, establishing safety. The description adds value by disclosing the tool's scope (only US public companies, financial claims), the return format (verdict, structured form, actual value with citation, percent delta), and the fact that it consolidates multiple steps into one call. No contradictions with annotations.

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

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

The description is front-loaded with natural-language triggers and gets straight to the point. Every sentence serves a purpose: stating the function, providing usage guidance, clarifying scope, and detailing outputs. It is slightly longer than necessary but remains efficient and 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?

Given the tool's simplicity (single parameter, no output schema), the description is remarkably complete. It covers purpose, when to use, limitations (US public companies, financial claims), and return structure. Without an output schema, the description rightfully explains the verdict types and citation detail, ensuring the agent knows what to expect.

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 for the single required parameter 'claim' is 100%, so baseline is 3. However, the description adds valuable context beyond the schema by providing natural-language examples ("Apple's FY2024 revenue was $400 billion") and explaining that the input is a natural-language factual claim. This enriches the agent's understanding of valid input formats.

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

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

The description explicitly states the tool's purpose: verifying natural-language claims against authoritative sources. It uses specific verbs like 'fact check' and 'verify', and clearly defines the resource (company-financial claims). It distinguishes itself from siblings by noting it replaces multiple sequential calls, making its specialization clear.

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 directive: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also scopes the tool to company-financial claims (revenue, net income, cash position for public US companies), which implicitly guides the agent on when not to use it. It does not explicitly name alternative tools, but the context signals and sibling list help.

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