Seo Competitors

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate safety and non-destructiveness. The description adds significant value by detailing per-model behavior (default free model, Anthropic requires API key, direct payment) and output structure ({score, confidence, signals, raw_response} + combined view). No contradictions.

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

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

The description is a single, front-loaded paragraph of four sentences. It efficiently conveys purpose, usage, and important nuances without extraneous 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?

Despite 4 parameters and no output schema, the description thoroughly explains the output format and usage context. It covers prerequisites (API key), default behavior, and typical use cases. The agent has enough information 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 description coverage is 100%. The description adds context beyond the schema, such as clarifying the default model for 'models', the requirement of '_apiKey' for Anthropic, and providing concrete examples for 'entity' and 'context'. This helps the agent beyond the bare 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: probe LLMs for knowledge about an entity and score visibility (0-100). It specifies the verb 'probe', the resource 'LLMs', and the output format. It distinguishes from siblings like 'scan_competitor_ai_presence' by being more general.

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

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

The description provides clear usage context: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the optional Anthropic model and the need for an API key. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description goes beyond by specifying that the tool routes to one of 4,396 tools, fills arguments, returns structured answers with stable citation URIs, and works on every tier. This adds significant behavioral context 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?

The description is front-loaded with the most critical guidance ('PREFER OVER WEB SEARCH'), uses bold for emphasis, and is well-structured with clear sections. While slightly verbose, every sentence serves a purpose: distinguishing from web search, listing domains, providing examples, and differentiating from siblings.

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 fully explains the return value: a structured answer with stable citation URIs. It covers input expectations, tool behavior, and fallback logic. The level of detail is comprehensive for a tool of this complexity, leaving no critical 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?

The input schema has 6 parameters, all aliases for a single field 'question', with 100% description coverage. The description provides natural language examples and clarifies that any alias works. However, it does not add substantial semantic nuance beyond the schema; it serves as reinforcement.

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: answering factual questions across diverse domains by routing to a large set of tools. It explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research. The verb 'ask' and resource 'Pipeworx' are well defined, and examples solidify the scope.

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

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

The description provides explicit guidance on when to use this tool ('PREFER OVER WEB SEARCH' for factual questions, triggers like 'what is', 'look up'), when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistance, deep_research for broad questions), and excludes breaking news (handled internally). This is a model of usage clarity.

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

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

Beyond annotations (readOnlyHint, etc.), the description adds rich behavioral details: it extracts answers only from tool results, returns specific fields including refusal reasons, and mentions the extra LLM call cost. This fully discloses behavior without contradicting any 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 and front-loaded with the core purpose. It is detailed but efficient, with no redundant sentences. Minor conciseness improvement possible, but overall 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 no output schema, the description fully explains return values on success and failure, including all fields and refusal reasons. The parameter is clearly explained, and the tool's role among 29 siblings is well-defined. 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?

The input schema has 100% coverage with each parameter described as 'Alias for question.' The description adds value by noting that the question is in natural language and that aliases (query, q, prompt, text, input) are accepted. This clarifies usage beyond the schema.

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

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

The description clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It explicitly contrasts with sibling 'ask_pipeworx' by explaining the same routing but with grounded extraction. This distinguishes it effectively from other tools.

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

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

The description provides explicit guidance on when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also advises preferring 'ask_pipeworx for casual lookups' due to the extra cost, giving clear 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 indicate readOnlyHint, idempotentHint, openWorldHint, and non-destructive. Description goes far beyond: details safety mechanisms (low-confidence short-circuits, closed market status), response shapes, resolver contract, parent event extractor, news fallbacks, wide-spread liquidity, cancellation rule risk. No contradiction with annotations.

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

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

Front-loaded with purpose and use cases. Dense but each sentence adds value. Could be slightly more concise, but given the complexity, it's 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?

No output schema, so description fully explains return values: result.market fields, result.analysis with model probability and edge, result.evidence keyed by source, resolver contract, parent_event extraction, news fields with fallback info, statuses for low confidence and closed markets, wide-spread tradeability, cancellation rule. Extremely 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 covers 100% of parameters with descriptions. The description adds meaning by explaining depth enum values, market input types (slug, URL, question text), and include_raw default with rationale. Adds context beyond the schema.

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

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

Clearly states it researches Polymarket bets by pulling Pipeworx data. Describes input types, core functions (resolve, classify, fan out, return evidence packet), and use cases. Distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on comprehensive data gathering.

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 for "should I bet on X"…' providing clear context. Does not explicitly state when not to use or mention alternatives, but the context is clear. Sibling tools are listed, implying differentiation.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds extensive behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), handling off-calendar fiscal years, sorting by primary metric, and return of citation URIs.

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 efficiently packs trigger phrases, usage guidance, per-type details, and output behavior. It is front-loaded with examples. Could be more structured with bullet points, but remains concise and scannable.

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 two parameters, full schema coverage, no output schema, and moderate complexity, the description is mostly complete. It covers input, data sources, sorting, and return format (paired data + URIs). Minor omission: drug output metrics are listed but not fully enumerated as with companies.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant meaning: explains the difference between company and drug types, specifies input formats (tickers/CIKs vs drug names), and clarifies the data fields returned per type.

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

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

The description uses specific verbs like 'compare', 'rank', and 'head to head', and explicitly targets 2-5 companies or drugs. It distinguishes from sibling tools by stating it replaces 8-15 sequential lookups and should be preferred over 'sequential single-pack lookups'.

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

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

The description provides clear examples of when to use (e.g., 'which is bigger', 'rank these companies') and explicitly states to prefer this tool over sequential lookups. It does not include explicit when-not-to-use scenarios, but the constraints (2-5 entities, type=company/drug) are implied.

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

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

The description discloses key behaviors beyond annotations: decomposes questions, parallel execution, returns findings packet with evidence, confidence, source, fetched_at, citations, and gaps for unanswered facets. It mentions never inventing answers and expected 15-60s response time. Non-subject topics yield mostly empty gaps.

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

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

The description is dense but every sentence earns its place, starting with critical account requirement and alternatives. It is well-structured with logical flow: account info, tool purpose, usage guidance, output description, and performance expectations.

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, no output schema, and extensive sibling tools, the description is remarkably complete. It covers input parameters, output format, internal mechanism, usage scenarios, limitations, and prerequisites. The agent can confidently select and invoke the tool.

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

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

The description adds significant meaning beyond the input schema: it explains the depth levels ('quick'=3, 'standard'=5, 'thorough'=8) and that 'thorough' requires a paid plan. For 'question', it clarifies that broad/multi-part queries are fine and decomposition is intended.

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

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

The description clearly states the tool performs grounded multi-source research across 1102 structured data sources, decomposing questions into facets and routing to 4,396 tools in parallel. It distinguishes itself from siblings like ask_pipeworx, which is for single lookups or breaking news.

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 provides usage guidance: requires a free account (paid for 'thorough' depth), alternative ask_pipeworx for signed-out users, best for broad/multi-part questions, and prefer ask_pipeworx for breaking news. It clearly states when not to use this tool.

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

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

Annotations already provide readOnlyHint and idempotentHint, but the description adds behavioral context about the output: returns top-N relevant tools with full input schemas and curated examples, ready to call directly. No contradictions with annotations.

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

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

The description is front-loaded with the main purpose, then provides usage context, output details, and a usage directive. It is slightly long but efficient, with no redundant sentences.

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

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

Given the complexity (6 params, no output schema), the description covers the tool's purpose, input, and output sufficiently. It explains that results include full schemas and are ready to call, which compensates for the missing output schema.

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

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

Schema coverage is 100% with descriptions for all parameters. The description additionally explains that multiple parameters (q, task, search, description) are aliases for query, adding value beyond the schema's individual 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 'Find tools by describing the data or task' and lists specific domains (SEC filings, financials, etc.), distinguishing it as a discovery metatool. It explicitly says to call this first to see the option set, differentiating from sibling tools that perform specific tasks.

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 usage context: 'Use when you need to browse, search, look up, or discover what tools exist for' and instructs 'Call this FIRST when you have many tools available'. It lacks explicit exclusions or alternatives, but the 'FIRST' directive implies it is a starting point, not a replacement for specific tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral context: fans out across multiple sources, specific data returned, USPTO soft-fail (sunset May 2025), and that names are not supported. 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 medium-length but front-loaded with examples and key guidance. Every sentence adds value: usage examples, preferred use, sources, data returned, limitations. Could be slightly more concise but remains efficient.

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

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

Given no output schema, description adequately covers return values (cik, company_name, recent_filings with URIs, fundamentals sorted, patents, news, LEI). Also covers failure modes (USPTO soft-fail, name not supported). Provides sufficient context for an agent to understand tool capabilities and 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?

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the 'company' enum's single value, giving examples of ticker ('AAPL') and zero-padded CIK ('0000320193'), and clarifying that names are not supported, requiring resolve_entity first.

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

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

Description explicitly states it provides a 'full cross-source profile of a US public company' and uses clear action verbs like 'tell me about', 'research', 'brief me'. It lists the specific data sources and returned fields, distinguishing it from sibling tools like resolve_entity.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views, and advises using resolve_entity first if only a name is available. Provides concrete examples of when to use and when not.

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 destructive and not read-only; description adds rationale for deletion (clear sensitive data) and aligns with destructive nature. Does not address idempotency or error cases, but annotations cover basic behavioral traits.

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 brief sentences with no wasted words. The purpose and usage are front-loaded, making it easy to scan.

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

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

For a simple one-parameter tool with annotations, the description covers purpose, when to use, and behavioral context. 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 clear parameter description. Description adds no additional parameter details beyond what the schema provides, meeting the baseline expectation.

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 (delete), the resource (memory), and the method (by key). It also distinguishes from sibling tools by mentioning 'remember' and 'recall'.

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

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

Explicit guidance on when to use: when context is stale, task done, or to clear sensitive data. Implicitly suggests not using for active context, but lacks explicit when-not-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 already declare readOnly, idempotent, non-destructive. Description adds behavior: fetches page, extracts title/description/links, emits standard markdown. No contradictions.

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

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

Three sentences plus use-case list, no redundancy. Front-loaded with key action and outcome.

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 two parameters and no output schema, description covers input, process, output format, and use cases. Annotations already handle safety profile.

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

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

Schema covers both parameters 100%. Description adds default (25) and max (50) for max_links, and clarifies url is a full URL, 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?

Description clearly states the tool generates an llms.txt file for any URL, with specific verb and resource. It distinguishes from siblings by focusing on a unique task not covered by other 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?

Provides explicit use cases for getting a site indexed, drafting for own project, or auditing competitors. Lacks explicit when-not-to-use guidance, but uniqueness reduces need.

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

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

Annotations already declare readOnlyHint and idempotentHint. The description adds context that it returns only the caller's subscriptions and lists specific fields, which is valuable beyond the annotations, though not exhaustive.

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

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

The description is two sentences with no fluff: first sentence states purpose and output, second gives usage context. Efficient and front-loaded.

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

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

Given the simple nature of the tool and lack of output schema, the description adequately covers purpose, returned fields, and usage context. Minor omission: no mention of pagination or error handling, but not critical.

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 'include_inactive'. The tool description does not add further meaning to the parameter, so baseline score holds.

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 lists the caller's active subscriptions, specifying the verb and resource. It also details the return fields, and given siblings like 'subscribe' and 'unsubscribe', it effectively distinguishes its role.

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

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

The description explicitly advises using this tool to review monitoring before adding more subscriptions or to find an ID for cancellation, providing clear when-to-use guidance relative to sibling tools.

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

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

The description adds behavioral details beyond annotations: it states the tool is free and doesn't count against tool-call quota, and has a rate limit of 5 per identifier per day. Annotations only indicate non-read-only, non-destructive, etc. The description does not contradict any annotation.

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 (5 sentences) and front-loads the core purpose. Every sentence adds value: usage guidelines, rate limits, quota info, and content advice. 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 has 3 parameters (including a nested object) and no output schema, the description covers all important aspects: when to use, what to include/exclude in the message, rate limits, and quota impact. No gaps remain.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds value by explaining when to use each feedback type (bug, feature, etc.) and advising against pasting end-user prompts. However, it does not provide additional syntax or format details beyond what the schema offers.

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: sending feedback (bugs, features, praise) to the Pipeworx team. The purpose is distinct from all sibling tools, which are research/information tools. The description uses specific verbs and resources, making it 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 specifies when to use the tool (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompts). It also provides context on rate limits (5 per identifier per day) and quota (free, doesn't count). This is comprehensive guidance.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable context: no PII, source (CF analytics-engine), and caching behavior (5min-1h depending on window). No contradictions.

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

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

The description is concise with two focused paragraphs: first states what the tool does, second lists use cases. Every sentence adds value without redundancy or fluff.

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

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

Given no output schema, the description specifies return data (top tools, packs, call volume) but lacks details on format or structure. However, for a simple aggregation tool, this is nearly complete. Annotations fully cover safety traits.

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

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

Schema coverage is 100% with one parameter (window) having an enum and description. The description adds meaningful guidance on interpretation ('shorter windows surface what's hot right now; longer windows show steady-state demand'), going beyond the schema.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over configurable windows. It uses specific verbs and distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on aggregate trending data.

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

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

The description lists three explicit use cases (discovering hot data sources, confirming canonical tools, seeing alignment) but does not specify when not to use or mention alternatives. However, the use cases provide clear context 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds significant behavioral context: it explains the required parameters, the semantic anchor for cross-event pairs (Jaccard similarity), partition filter, fill check against CLOB depth, and response structure. This goes well 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 detailed and well-structured (requirement first, then mode details, then additional checks). It is about 250 words, which is appropriate for the complexity, but some redundancy could be trimmed. Still, it efficiently conveys all necessary 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 and lack of output schema, the description is remarkably complete. It explains the response fields (opportunities, partition_check, fill_check), the fill check logic, and references a sibling tool for custom sizing. An AI agent has enough information 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 description coverage is 100%, so baseline is 3. The description adds meaning by explaining what each parameter does in practice (e.g., event accepts slugs or full URLs, topic is a seed question) and provides examples. It adds value but does not delve into deep internals.

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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and specifies the resources used (event slugs, topic seeds). This differentiates it from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

The description explicitly recommends using event for a specific market and topic for cross-event scanning, noting that cross-event mode catches patterns single-event misses. It also states that calling with no arguments fails. However, it does not explicitly compare to sibling tools or provide 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?

The description adds rich behavioral context beyond annotations: cache duration (1h at KV level keyed on knobs), that Fed bets are excluded from ranking with an explanation, diagnostic counters for empty segments, and detailed model logic (e.g., lognormal barrier, favorite-longshot bias correction with per-sport alpha). 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 main purpose and uses ALL CAPS headings for scannability, but it is very long (nearly 400 words) and dense with technical details. While each part serves a purpose, the length may overwhelm agents, warranting a moderate score.

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

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

Given no output schema, the description fully compensates by detailing the response structure (by_segment with three segments, fed_candidates, _diagnostics), model families, filtering knobs, and caching. It addresses potential pitfalls (e.g., unreliable Fed signals) and explains why segments might be empty, making the tool complete for agent decision-making.

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

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

Schema descriptions cover all 9 parameters thoroughly (100% coverage). The overall tool description summarizes knobs but does not introduce new parameter semantics beyond what is already in the schema, meeting the baseline without adding extra value.

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

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

The description opens with a specific verb-resource pairing: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It elaborates the three model families (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT), clearly differentiating from sibling tools like polymarket_arbitrage or polymarket_edge_tracker by focusing on disagreement signals and ranking.

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 states it's built for 'what should I bet on today' and describes the output segments and tradeable-edge knobs, implying use cases for bet discovery. However, it does not explicitly compare to sibling tools (e.g., polymarket_arbitrage) or specify when not to use it, leaving differentiation to 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, which the description does not contradict. The description adds detail on how decay is computed (from daily closes of edge_pp_net, not intraday), which enriches transparency 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 long but front-loaded with the core purpose. Each sentence adds value, including explanation of response fields and limits. Could be slightly more concise, but the detail is justified by 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?

With no output schema, the description thoroughly explains the response structure (tracked, expired, snapshot_dates) and their fields. It also covers limitations (60-day TTL, snapshot gaps), making it fully self-contained.

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

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

Input schema covers both parameters with descriptions, achieving 100% coverage. The description adds context on default values and range for days (max 30) and explains 'window' as a snapshot family, going beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It answers a specific question about edge age and trend, distinguishing it from sibling tool 'polymarket_edges' which likely provides current edges only.

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 tells when to use this tool (to assess edge age and decay) and provides context on limits (60-day TTL, snapshot gaps). It does not explicitly mention when not to use it or alternatives, but the context is adequate.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds detailed behavioral context: returns verdict (clean|degraded|cannot_fill), lists per-leg fill details, thin_legs, forced_directional_risk, and explains the risk of partial basket fills. This significantly enhances transparency 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 well-structured with sections for REQUIRES, SINGLE-MARKET, BASKET, and usage guidance. It is dense but not wordy; every sentence adds value. Slightly long due to thoroughness, but appropriate for the tool's complexity.

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

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

Given no output schema, the description extensively documents return values for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, theoretical_sum vs realizable_sum, capture_ratio, profit_usd, thin_legs, etc.). It covers edge cases like thin books and partial fills, making it fully contextually complete.

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

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

Schema coverage is 100%, but the description adds further meaning: explains that `market` or `event` are required (mutually exclusive), clarifies `size_usd` interpretation for buys vs sells and basket mode, and details `side` defaults and basket auto-behavior. This adds significant value beyond the schema.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes single-market and basket modes, and names specific outputs like top_of_book, vwap_fill_price, verdict, etc., making it easy to understand what the tool does.

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 tells when to use the tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns against partial basket fills converting an arb into an unhedged direction, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds significant behavioral context: it explains compatibility_warning cases (non-equivalent bet shapes, semantically unrelated events), temporal_alignment, and skipped_cross counters. It also notes that spreads are meaningless if temporal alignment is false. 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 relatively long but well-structured with sections for modes, response, and safety fields. It uses bold headers for readability. While verbose, every sentence provides necessary information; none are wasted. A bit more conciseness would improve it, but it is still effectively 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 has no output schema, the description is crucial and covers all important aspects: return fields (prices, spread, compatibility_warning, temporal_alignment, skipped counters), parameter semantics, and behavioral caveats. It is complete enough for an AI agent to invoke correctly without additional context.

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

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

Schema coverage is 100%, so baseline is 3. The description adds substantial value beyond the schema: it explains the two modes, lists the topic shortcuts, describes how explicit parameters override topic mappings, and details the response structure (prices, spread, safety fields). This greatly aids an AI agent in constructing correct parameters.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket. It uses specific verbs ('calculates', 'compares') and distinguishes itself from sibling tools like polymarket_arbitrage by specifying it operates across venues. The description also details two modes and response 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?

The description explains when to use the tool: for comparing the same question across two venues. It explicitly mentions two modes (topic shortcuts vs explicit pairings) and warns that most pre-mapped topics return compatibility warnings, so the tool may not yield tradeable spreads. However, it does not explicitly state when not to use it versus alternatives like polymarket_arbitrage, 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 already set readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: scoping to identifier, dual behavior with/without key, and pairing with remember/forget. No contradictions; adds information beyond annotations.

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

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

The description is concise, front-loading the core behavior in the first sentence, and uses additional sentences to add value without redundancy. Every sentence earns its place.

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

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

Given the single parameter, no output schema, and rich annotations, the description fully covers all necessary aspects: dual functionality, scoping, and usage scenarios. It is complete for an agent to understand how to 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?

With 100% schema coverage, baseline is 3. The description adds meaning by explaining the effect of omitting the key (list all) and providing example use cases (ticker, address, notes), exceeding the schema's description which only states 'Memory key to retrieve (omit to list all keys)'.

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

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

The description clearly specifies the verb 'retrieve' or 'list', the resource ('values saved via remember'), and distinguishes between retrieving a specific key and listing all keys. It also explicitly mentions pairing with sibling tools remember and forget, differentiating its role.

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 states when to use the tool ('to look up context the agent stored earlier... without re-deriving it from scratch') and implies alternatives by mentioning omit key for listing. It provides context for typical use cases but does not explicitly state when not to use it or enumerate alternative tools beyond recall/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 already indicate safe read-only and idempotent behavior. The description adds behavioral context: the mark_read flag mutates read state, and it describes return fields. This goes beyond annotations by clarifying the side effect of mark_read and the alternative endpoint.

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 (5 sentences) with no filler. Front-loaded with the core action and return, then adds filters and usage tips. Every sentence serves a purpose.

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

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

Given no output schema, the description adequately describes return fields (source, citation_uri, raw payload) and the mark_read effect. Mentions the alternative URL for scripts. Could be more explicit about output format (list vs single), but overall complete for a simple feed reader.

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

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

Schema covers 100% of parameters with descriptions. The description restates some parameter functions (type, since, mark_read) and adds examples (e.g., 'sec_8k'), but adds minimal new semantic meaning beyond 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 primary action ('Pull fired events from your subscription feed') and specifies the return content (source, citation_uri, raw payload). It distinguishes itself from sibling tools like `subscribe` and `list_subscriptions` by focusing on reading alert events.

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 guidance on filtering by type and since, explains the mark_read parameter's effect, and mentions polling suitability. Also gives an alternative HTTP endpoint for scripting. However, it does not explicitly state when not 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?

Discloses parallel fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO PatentsView API sunset issues, and return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs). No contradictions with annotations (readOnlyHint, idempotentHint, etc.) which already indicate safety; description adds operational detail.

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 packs a lot of information without fluff, but could be more structured (e.g., bullet points for sources or return format). Still efficient and front-loaded with examples.

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

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

Covers all essential aspects: data sources, fallbacks, parameter formats, return structure, and sibling differentiation. Without an output schema, description adequately explains the output. No gaps remain given tool complexity.

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

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

Schema coverage is 100%, but description adds substantial value: explains `since` format (ISO date or relative shorthand like '7d', '30d' etc.), suggests '30d' for monitoring, gives examples for `value` (ticker or CIK), and notes `type` currently only supports 'company'.

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

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

Description explicitly defines tool as 'change feed for a company in the last N days/weeks/months' with examples like 'what's new with X'. It distinguishes from sibling 'entity_profile' which provides static profile 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?

Provides concrete query examples and clear guidance on when to use this tool vs entity_profile. Explains fallback behavior between GDELT and GNews, and the soft-failure for USPTO. Offers parameter usage tips for `since`.

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 beyond annotations: scoping by identifier, persistence differences (authenticated vs anonymous, 24h). Annotations confirm idempotent, non-destructive, not read-only. No contradiction.

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

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

Front-loaded with main purpose, well-structured with usage guidance and lifecycle info. 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 simple 2-param tool with no output schema, description fully covers purpose, usage, behavior, storage details, and lifecycle. Agent has sufficient info 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%, baseline 3. Description doesn't add new parameter semantics beyond the schema's descriptions. Examples in schema key description provide context.

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

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

Description clearly states 'Save data the agent will need to reuse later' with specific verb and resource. Explicitly distinguishes from siblings 'recall' and 'forget'.

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

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

Provides explicit context: 'Use when you discover something worth carrying forward...' and mentions pairing with recall/forget. Could add exclusion scenarios but overall clear.

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

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

The description fully discloses the tool's internal cascade through several lookup endpoints, which the annotations (readOnlyHint, openWorldHint, idempotentHint) do not cover. It also details the output for each type, including specific fields like ticker, CIK, RxCUI, and citation URIs, providing rich 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 concise but packed with useful information, starting with example user queries to quickly convey purpose. Every sentence serves a purpose, and the structure is logical: overall purpose, usage hint, then detailed per-type 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?

Given that there is no output schema, the description thoroughly explains what is returned for each entity type, including specific identifiers and citation URIs. It also hints at internal complexity ('cascades through several lookup endpoints'), making the tool's behavior predictable for an AI agent.

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

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

Despite 100% schema coverage, the description adds significant value by explaining the semantics of the 'type' and 'value' parameters with concrete examples and expected input formats (e.g., ticker like AAPL, drug names like ozempic). This goes beyond the bare schema descriptions.

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

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

The description clearly states the tool's purpose: resolving a user-spoken name to a canonical/official identifier needed by other tools. It provides concrete examples ('What's the ticker for...') and distinguishes itself from sibling tools by being the first step when an ID is needed, while siblings like compare_entities and entity_profile serve different functions.

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

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

The description explicitly advises 'Use FIRST whenever you have a name but need an ID,' which is a clear usage guideline. It also mentions that it replaces 2-3 manual lookups, implying efficiency. However, it does not explicitly state when not to use it, though that is largely implied by its purpose.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds that it probes each entity with ai_visibility_check and returns ranked list with score, confidence, signal density, which is consistent and adds useful detail beyond annotations.

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

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

Three concise sentences: first states core purpose, second explains process and output, third gives practical use case. No filler, 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?

Tool has 4 parameters fully described in schema. Output schema missing but description explains output format adequately. For a comparison tool with good annotations, this is complete enough for correct usage.

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

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

Schema description coverage is 100%, but description adds extra semantics: entities first entry is subject, rest competitors; models default is workers-ai, anthropic requires _apiKey; context disambiguates. Adds meaningful context beyond schema.

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

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

Description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check internally, and outputs ranked scores. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

Explicit use case given: competitive AI-marketing audits. Implies when to use vs alternatives (e.g., comparing multiple entities). No explicit when-not-to-use, but 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?

The description adds behavioral details beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and sources_failed will list timeouts. Annotations already indicate readOnly, openWorld, idempotent, and non-destructive, and the description complements without contradiction.

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

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

The description is a single paragraph with valuable information, but it is somewhat lengthy and could be more front-loaded. It effectively conveys all necessary details without excessive verbosity, earning a solid 4.

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 data sources, partial failures, ecosystem limits) and lack of output schema, the description provides a comprehensive list of returned fields, failure modes, and scope. It adequately prepares the agent for 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 description coverage is 100%, so the baseline is 3. The description does not add significant new semantics beyond the schema's descriptions of 'package' and 'version'; it confirms version defaults to latest but that is already in the schema. Minimal added value.

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

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

The description clearly states the tool performs a composite check for adding an npm package, combining deps.dev and bundlephobia data. It distinguishes from siblings by being specific to npm ecosystem and package evaluation, while other tools like 'validate_claim' or 'deep_research' serve different purposes.

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

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

The description explicitly tells when to use: when an agent asks about safety, popularity, size, or cost of an npm package. It also states when not to use: for other ecosystems (PyPI, Maven, etc.), directing to use deps.dev:version directly. This provides clear usage context and alternatives.

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

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description reveals key behavioral traits: input cap at 200K chars with truncation/flagging, use of BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, and output format (passages with character offsets and similarity scores). No contradictions with annotations.

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

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

The description is dense but efficient, using 5 sentences to convey purpose, usage, benefits, technical details, and constraints. Every sentence adds value; no redundancy or fluff. The structure is logical: declaration, usage, pairing, technical implementation, and limitations.

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 adequately describes return values (passages, character offsets, similarity scores). It covers input constraints, technical approach, and integration patterns. Given the tool's complexity (semantic search), the description is complete and actionable for an AI agent.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds significant context: it explains how the search works (embeddings, windowing), provides example queries, and clarifies the text capacity. This goes beyond the bare schema, aiding correct invocation.

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

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

The description clearly states the tool's purpose: semantic search inside a fetched record. It uses specific verbs ('search inside', 'pass', 'get back') and clearly identifies the resource (text from a fetched record). It distinguishes itself from siblings by mentioning a complementary tool (ask_pipeworx_grounded) and explaining how it differs from using the full document.

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 this tool: 'Use when the record is too big to cram into the prompt'. It explains the benefit (saves context, returns only relevant passages) and provides a concrete alternative/workflow: pair with ask_pipeworx_grounded after fetching with the gateway. This gives clear, actionable guidance.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by detailing the output fields (keywords, volume, rank, URL) and the dependency on a DataForSEO API key. 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 concise sentence with an illustrative example. Every part serves a purpose, no fluff, and the core information is front-loaded.

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

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

Given no output schema, the description adequately explains the return value (keywords with volume, rank, URL). Parameters are fully documented in the schema. Slight miss: no mention of pagination or limit behavior beyond the schema description.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds an example call but does not provide additional meaning beyond what the schema already describes for each parameter.

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

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

The description clearly states the tool's purpose: to return keywords a domain ranks for in Google organic, with specific data points (volume, rank, URL). It also labels it as 'Competitor SEO intelligence,' which 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 implies usage for SEO competitive analysis but does not explicitly state when to use this tool versus alternatives like 'scan_competitor_ai_presence' or other ranking tools. No when-not or alternative guidance is provided.

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

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

The description goes far beyond annotations: it details that subscriptions are persistent, delivery channels (feed always on, email, SMS with 10/day cap, webhook with HMAC signing and auto-disable on 10 failures), and that a subscription ID is returned. Annotations only hint at idempotency and non-destructiveness; the description fills in all behavioral nuances.

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

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

The description is dense but every sentence adds value. It front-loads the purpose and then enumerates types and delivery options. A more structured layout (e.g., bullet points) could improve readability, but it remains concise without redundancy.

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

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

Given the tool's complexity (multiple subscription types, delivery options, account restrictions, return value), the description covers all critical aspects: types, params, delivery behaviors, limits, and the returned subscription ID. No output schema exists, but the description adequately explains 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?

With 100% schema coverage, the description adds substantial meaning: it explains each type's params with concrete examples (e.g., sec_8k items codes, delivery requirements like verified phone). It clarifies optional fields and constraints (e.g., webhook signing secret returned once).

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 'Create a proactive monitoring subscription to a live-data event stream', specifying the verb (create), resource (subscription), and context. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation and proactive monitoring.

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

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

The description provides strong usage context, including the requirement for a Pipeworx OAuth account and a note that anonymous/BYO cannot persist subscriptions. It lacks explicit when-not-to-use or alternatives, but the examples and types cover typical use cases well.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that calling with no arguments gives full spread, and output is drawn from a live catalog of thousands of tools, providing useful context beyond the annotations.

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

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

The description is moderately long but each sentence adds value. It front-loads the purpose and then provides details. Could be slightly more concise, but 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 role as an onboarding entry point, the description covers when to use, how to use (with/without topic), what output contains, and references to meta-tools. No output schema but explains return format adequately.

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?

Only one parameter (topic) with 100% schema coverage. Description adds: 'Omit for a cross-category spread' and lists example values like finance, pharma, betting, adding meaningful guidance beyond the schema's description.

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

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

The description clearly states this is the onboarding entry point for an agent to learn what Pipeworx can do. It specifies the tool returns category-bucketed example questions with tool+argument shapes. This distinguishes it from siblings like ask_pipeworx or 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 instructs to use this tool FIRST when the agent doesn't know what Pipeworx can do, or to learn how to call meta-tools. Also explains optional topic parameter to focus on a specific area.

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

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description explains that the row is deactivated, not deleted, preserving historical events. This adds valuable context 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 efficient sentences covering purpose, constraints, and behavioral detail with no wasted words. Front-loaded with the primary action.

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

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

For a simple mutation with one parameter and no output schema, the description is complete: it clarifies purpose, ownership, side effects, and relation to sibling tools, leaving no ambiguity for an AI agent.

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

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

The schema fully describes the single 'id' parameter with type and source. The description adds no new parameter-specific meaning beyond reinforcing the schema, which is adequate (baseline for 100% coverage).

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

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

The description explicitly states 'Cancel a subscription by id' with a specific verb and resource. It also distinguishes from sibling tools by mentioning ownership enforcement and historical availability via recent_alerts.

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 provides clear ownership constraints ('you can only cancel your own subscriptions') and references an alternative ('historical events stay available via recent_alerts'). While not exhaustive, it effectively guides when and why to use the tool.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it returns a verdict with specific categories, extracted structured form, actual value with citation, and percent delta. It also notes it replaces 4-6 sequential calls. No contradiction with annotations.

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

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

The description is front-loaded with usage examples and efficiently uses every sentence to add value. It could be slightly more concise, but overall well-structured and not verbose.

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

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

For a tool with 1 parameter and no output schema, the description is remarkably complete. It covers input format, supported claim types, return values, and efficiency rationale. No gaps remain.

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

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

Schema coverage is 100% with a single parameter described. The description goes beyond by providing example claims and specifying the domain and data source (SEC EDGAR + XBRL), adding meaning to what the 'claim' parameter should contain.

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: natural-language claim verification against authoritative sources. It provides specific verb phrases ('validate claim', 'fact check', 'verify the claim that...') and limits scope to company-financial claims. It distinguishes from siblings by noting it replaces multiple sequential calls.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also specifies the supported domain (company-financial claims for public US companies). While it doesn't list when not to use or alternative tools, the guidance is clear and actionable.

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