Anapioficeandfire

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

The description adds value beyond annotations by detailing that probing multiple LLMs yields a score (0-100), confidence, signals, and raw response per model, plus a combined view. It also notes cost implications for Anthropic calls. Annotations already indicate read-only, idempotent, non-destructive behavior.

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

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

The description is concise with four sentences. It front-loads the main action and then adds usage context. Every sentence contributes value without redundant information.

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

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

Despite no output schema, the description explains the return structure: per-model object with score, confidence, signals, raw_response plus combined view. It covers use cases, default behavior, and optional model selection. Could be slightly more detailed on interpreting confidence or signals, but overall complete for a probing tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra context: default model is Workers AI Llama-3.3-70b (free), Anthropic requires _apiKey with direct payment, and context helps disambiguate entities. This enriches parameter understanding beyond the schema.

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

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

The description clearly states the tool's action: probe LLMs for knowledge about a specific entity and score visibility (0-100) per model. It specifies the verb, resource, and output, and distinguishes itself from siblings like scan_competitor_ai_presence by emphasizing per-model scores and combined view.

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

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

The description provides explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default model and optional Anthropic key. However, it does not explicitly state when not to use this tool or differentiate from siblings that might perform similar tasks.

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

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

Annotations already indicate safe, read-only, idempotent behavior. Description adds context about the tool's scope (4,552 tools, 1160 sources), output format (structured answer with pipeworx:// URIs), and that it handles various question types. 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 appropriately detailed, front-loaded with key guidance, and uses clear structure with examples. Every sentence serves a purpose, and it avoids unnecessary verbosity.

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

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

Despite having no output schema, the description explains the return format (structured answer with citations). It covers all aspects: purpose, usage, parameters, and behavioral details. Complete and actionable.

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

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

Schema coverage is 100% with all parameters being aliases for 'question'. Description further explains that the question is in natural language and lists aliases ('q', 'text', etc.). Provides multiple examples that add meaning beyond schema.

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

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

The description clearly states the tool's purpose: routing questions to a large set of tools and sources to return structured answers with citations. It distinguishes from siblings by providing explicit guidance on when to use alternatives.

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

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

Provides explicit when-to-use guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and 'Step up only when needed' for specific tools like ask_pipeworx_grounded and deep_research. Includes example queries.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context beyond annotations: it explains the extra LLM call cost, the refusal modes (with specific reasons listed), and the return format including evidence. No contradiction with annotations.

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

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

The description is fairly long but well-structured: it starts with a clear purpose, explains the mechanism, then gives usage guidance and return format. Some redundancy exists (e.g., listing aliases in both description and schema is unnecessary). Each part earns its place, but the length could be trimmed slightly without losing meaning.

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

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

Given the tool's high-stakes nature and many sibling tools, the description is comprehensive: it covers when to use, how it works, refusal reasons, return format, cost trade-off, and comparison to the main sibling. The absence of an output schema is compensated by the detailed return format description. It sufficiently equips an agent to select and invoke correctly.

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

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

The schema has 6 parameters, all aliases for 'question', with 100% schema coverage. The description adds 'Your question in natural language' and lists the aliases, which is helpful but adds only marginal value over the schema's own descriptions. The description does not provide additional detail like expected format or length limits.

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 identifies the tool as a 'hallucination-resistant answer mode' for high-stakes reads. It specifies the exact behavior: picks the right tool, fetches data, extracts answer only from the tool result. It explicitly distinguishes itself from the sibling 'ask_pipeworx' by noting the extra extraction step and the same routing logic.

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

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

The description gives explicit guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims, medical lookups, public statements). It also states when not to use it: 'prefer ask_pipeworx for casual lookups' and mentions the cost trade-off of an extra LLM call.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds extensive behavioral details beyond annotations: resolver contract with confidence levels, parent_event extractor, news fallback behaviors, safety short-circuits for low-confidence or closed markets, illiquid spread handling, and resolution-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?

The description is very long and detailed, covering many aspects. While front-loaded with core purpose, the length may be overwhelming. Every sentence adds value but conciseness is sacrificed for completeness. Could be restructured to be more concise.

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

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

Given the tool's complexity (fan-out to multiple source APIs, resolver logic, parent events, etc.) and absence of output schema, the description is extremely complete. It covers behavior, edge cases, safety measures, and response interpretation in detail, leaving little 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?

Schema coverage is 100% (baseline 3). The description adds significant meaning: explains how to input market (slug, URL, or question text) with examples, describes the depth parameter with quick vs thorough options, and details the include_raw parameter including its effect on response size. This enriches parameter understanding well beyond schema.

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

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

The description explicitly states the tool 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the resource (Polymarket bet) and the action (pull data and return evidence). It distinguishes from siblings by listing specific use cases like 'should I bet on X' and providing detailed fan-out examples that are unique to this tool.

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

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

The description provides explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also includes extensive examples of fan-out for different categories. While it does not explicitly state when not to use or provide direct alternatives, the guidance is clear and context-rich.

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 (readOnly, idempotent, not destructive) already cover safety. Description adds minimal behavioral context beyond the basic fetch operation. 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?

Extremely concise (two words), no wasted text. Could be slightly more descriptive but remains efficient for a simple tool.

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

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

Given single parameter and output schema, description provides bare minimum. Lacks context on behavior for missing ids, error handling, or differentiation from many sibling tools.

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

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

Schema description coverage is 0% (no param descriptions). Description says 'by id' but does not clarify the type or source of the id, failing to compensate for missing schema details.

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

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

Description clearly states 'Single book by id', indicating a fetch operation on a resource by identifier. Differentiates from sibling 'books' (plural) which likely lists multiple.

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

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

No explicit when-to-use or alternatives. While naming implies single book retrieval, no guidance on when to use this versus 'books' list tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds return fields (ISBN, authors, etc.), which is useful 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?

Single sentence, efficiently structured, front-loaded with action, no redundancy.

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

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

With output schema present, description covers return fields. Parameters are well-explained, but no mention of sorting or default behavior.

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 0%, but description explains each parameter's role (name filter, date range, pagination) adding meaning beyond the schema's property names.

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

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

Description clearly states 'Search and list A Song of Ice and Fire books' with filterable parameters, distinguishing it from sibling 'book' (singular) which likely returns a single book.

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?

Does not explicitly state when to use this tool vs alternatives like 'book' or 'character'. Context is implied but no when-not or alternative naming.

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

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, which describe the behavioral profile. The description adds no new behavioral information (e.g., whether the character is cached, rate limits, or authentication needs). It merely restates the tool's purpose.

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

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

The description is very short (one sentence), which is concise, but it fails to front-load important usage context. It omits parameter descriptions or examples, making it less helpful than it could be without being 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?

Given the tool has an output schema, the description need not explain return values. However, it lacks essential context about the input parameter (e.g., that 'id' is required and numeric). For a simple retrieval tool, this is minimally adequate but incomplete.

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 0% schema description coverage, the description must compensate by explaining the 'id' parameter. It only says 'by id' without clarifying the id type, format, or meaning. This adds no value beyond the schema property name.

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 'Single character by id.' clearly indicates the tool retrieves a single character resource by its identifier. It distinguishes from the sibling tool 'characters' (plural), which likely returns multiple characters. However, the verb 'get' or 'retrieve' is implied but not explicit.

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

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

The description provides no guidance on when to use this tool versus its siblings like 'characters' or 'book'. It lacks any context about prerequisites, limitations, or alternative tools, leaving the agent to infer usage solely from the name.

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 reveals that the tool returns specific fields and allows filtering, but does not add further behavioral context like rate limits or pagination behavior beyond the schema presence.

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

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

The description is a single, well-structured sentence that front-loads the purpose and enumerates filters and returns with no extraneous content. Every phrase earns its place.

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

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

Given the tool's 8 optional parameters and existing output schema, the description covers the main filtering and return aspects. It could be more complete by noting pagination parameters and that all parameters are optional, but overall it provides a solid overview.

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 0%, so the description wholly compensates by listing most filter parameters (name, culture, birth/death descriptor, gender, alive status). Two parameters (page and pageSize) are not explained, but the description adds significant value for the other six.

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

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

The description clearly states the verb 'Search and list' with the resource 'ASOIAF characters' and enumerates specific filters and return fields. It effectively distinguishes from the sibling 'character' tool (singular) by implying a list/search function rather than a single resource retrieval.

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 searching and listing characters but does not explicitly state when to use this tool over alternatives like 'character' or 'books'. No guidance on prerequisites or when not to use 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?

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years correctly, and sorts results by primary metric. 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 example phrases, every sentence adds value. Slightly long but packed with essential information.

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

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

No output schema, but description explains return type (paired data + pipeworx:// citation URIs) and sorting. Adequate for an entity comparison tool.

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

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

Schema coverage is 100%. Description adds meaning by explaining what each enum value does and providing concrete examples for 'values' (tickers/CIKs for companies, drug names).

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

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

Description uses specific verbs like "Compare" and resources "companies or drugs". It also distinguishes from siblings like "entity_profile" by explicitly stating to prefer this tool over sequential lookups when comparing entities.

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

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

Explicitly tells the agent when to use (comparing 2-5 entities) and when to use alternatives (single lookups). Includes example queries and notes that it replaces 8-15 sequential lookups.

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

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

Discloses key behaviors beyond annotations: account and authentication needed, paid plan for 'thorough' depth, returns findings packet with gaps[] for unanswered facets, expected 15-60s response time. No contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false).

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

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

Relatively long but well-structured with critical information front-loaded (account requirement). Every sentence adds value—covers prerequisites, behavior, use cases, and limitations. Minor redundancy but not excessive for the complexity.

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

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

Covers all necessary aspects: input (question, depth), prerequisites (account, free vs paid), process (parallel decomposition), output (findings packet with evidence, confidence, gaps), timing, and alternatives. No output schema exists, so description adequately fills gaps.

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

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

Schema describes both parameters (question and depth enum) with coverage 100%. Description adds meaning: depth values map to number of facets (quick=3, standard=5, thorough=8), and explains that broad/multi-part questions are intended for decomposition, adding nuance beyond the schema.

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

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

The description clearly states it performs 'grounded multi-source research across Pipeworx's 1160 STRUCTURED data sources' by decomposing questions into focused facets routed to 4,552 tools in parallel. It distinguishes from siblings like ask_pipeworx (single lookup) and makes the resource and action unambiguous.

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

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

Explicitly states when to use ('broad/multi-part questions over structured data') and when not to ('BREAKING or colloquial CURRENT-NEWS' topics, single lookups). Provides an alternative (ask_pipeworx) and notes account requirements including a paid plan for certain depth levels.

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=true, idempotentHint=true. The description adds beyond these: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This discloses the richness of the output and the no-friction design, which annotations do not cover.

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

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

The description is two sentences plus examples. It front-loads the core action and then explains output and usage context. Every sentence adds essential information, with no redundant or verbose phrasing.

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

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

Despite no output schema, the description fully explains the return value: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples).' It also covers the input parameters (query, limit) and their aliases. For a discovery tool with simple parameters and a well-described output, this is complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the primary parameter's natural language usage with concrete examples ('analyze housing market trends', 'look up FDA drug approvals') and mentions that aliases (task, q, search, description) are accepted. This goes beyond 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: 'Find tools by describing the data or task.' It provides a list of example domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools by stating 'Call this FIRST when you have many tools available and want to see the option set.' This makes the unique role clear.

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

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

The description explicitly states when to use the tool: 'Use when you need to browse, search, look up, or discover what tools exist' and provides a clear directive: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives both positive and negative guidance.

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

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

Annotations indicate readOnly, openWorld, idempotent, not destructive. The description adds behavioral context beyond annotations: explains it fans out across multiple sources, mentions USPTO sunset (soft-fail), returns specific fields like 'recent_filings (up to 5 with...URIs),' and includes fallback mechanisms (GDELT→GNews). 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 densely packed with useful information in a single paragraph. It front-loads the core purpose and usage guidance. While somewhat long, each sentence adds value. Could benefit from minor restructuring, but overall efficient.

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

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

Despite lacking an output schema, the description thoroughly explains the return structure: lists all data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF) and specific fields (cik, company_name, recent_filings with URIs, fundamentals, patents with soft-fail note, news with fallback, LEI). This fully 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% and schema descriptions already explain the parameters. The description adds examples ('AAPL', '0000320193') and reinforces that names are not supported, but does not provide new semantic information 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 explicitly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call.' It lists specific verbs like 'tell me about,' 'research,' 'brief me on,' demonstrating clear purpose and distinguishes from sibling tools like 'resolve_entity' which handles name resolution.

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

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

Provides explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also states when not to use: 'Names not supported — use resolve_entity first if you only have a name.' This clearly differentiates usage from alternatives.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data, which aligns with annotations and adds value beyond them.

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

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

Two sentences, front-loaded with purpose, no 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?

For a simple tool with one required param and no output schema, the description fully covers purpose, usage, and behavioral 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%, and the description does not add significant meaning beyond what the schema already provides for the single parameter 'key'.

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 'Delete a previously stored memory by key' – a specific verb and resource. It distinguishes from siblings remember and recall.

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

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

Explicitly states when to use: stale context, task complete, or clearing sensitive data. Also pairs with remember and recall, providing clear guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, so the agent knows it's safe. The description adds that it fetches the page and emits standard markdown, providing useful context beyond annotations but not deep behavioral details like rate limits or error handling.

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

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

The description is concise with three sentences: first defines purpose, second explains process, third lists use cases. No fluff, well-structured, and front-loaded with the core 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?

Given 2 parameters, high schema coverage, no output schema, and no nested objects, the description adequately explains output (text blob ready for placement) and use cases. It is complete enough for an agent to understand the tool's function and output.

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

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

Schema description coverage is 100% (both parameters documented). The description does not add meaningful extra meaning beyond what the schema provides; it simply restates the parameters in context. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for any URL, specifying the actions (fetch, extract, emit) and the output format. It distinguishes itself from sibling tools like ai_visibility_check by focusing on creating the llms.txt file specifically.

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

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

The description provides explicit use cases (client site indexing, drafting own project, auditing competitors) but lacks explicit when-not-to-use or alternative tool suggestions. The context is clear but not fully exhaustive.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, covering the safety profile. The description adds no behavioral context beyond the retrieval mechanism. No mention of error behavior, permissions, or data freshness.

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

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

The description is extremely concise (5 words) but lacks necessary detail. This under-specification reduces clarity, making it more a tautology than a helpful summary. A one-line description of a tool with a parameter should at least explain what the parameter means.

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 simplicity (one required param, output schema exists), the description could still mention that it returns the full house object or specify what happens if ID is invalid. The omission of such details makes it insufficient for an agent to use confidently.

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 0%, so the description should compensate. It only says 'by id,' which adds minimal meaning to the 'id' parameter beyond the schema stating it's a required number. No format, range, or source information is given.

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 'Single house by id.' clearly indicates the resource (a single house) and the retrieval method (by identifier). It implicitly distinguishes from the sibling tool 'houses' (plural) which likely returns a list.

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

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

The description provides no guidance on when to use this tool versus alternatives. While the sibling 'houses' exists, there is no explicit instruction or comparison. The agent must infer usage from the singular/plural naming.

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 only that it is a search/list operation, which is consistent but does not provide additional behavioral insights 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 extremely concise (2 words) but lacks structure. While not verbose, it is underspecified for a tool with many parameters. It is merely a minimal label.

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

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

Given the complexity of 9 optional parameters and an output schema, the description is insufficient. It does not explain the domain (houses from a fictional universe), filtering capabilities, or pagination behavior, leaving users to infer from examples.

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 0% and the description adds no parameter information. For a tool with 9 parameters, the lack of meaning for fields like 'words', 'region', etc., severely hinders correct usage.

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

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

The description states a clear verb-resource combination: 'Search/list houses.' It distinguishes from sibling tools like 'house' (singular) which likely returns a specific house, but it could be more specific about the domain context (e.g., from Game of Thrones).

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

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

No guidance is provided on when to use this tool versus alternatives like 'house', 'characters', or search tools. There is no when-to-use or when-not-to-use advice.

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, etc. Description adds context by listing returned fields and mentioning default filtering for active subscriptions, exceeding what annotations alone convey.

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

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

Two concise sentences with front-loaded purpose. Every sentence adds value: purpose + return fields, then usage guidance. No redundancy or filler.

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

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

Given low complexity, schema coverage, and annotations, the description is largely complete. It lists return fields and usage context. Could explicitly state it's scoped to the caller, but 'caller's' already implies that.

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

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

Schema coverage is 100% with a clear description for the single parameter. The description adds minimal extra value beyond stating the default behavior (active only), which is already implied.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies when to use vs. subscribe/unsubscribe, though no explicit alternatives are named.

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

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

Annotations provide no behavioral insights beyond readOnlyHint, openWorldHint, etc. The description adds important behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against quota, team reads digests daily. This is useful context beyond annotations.

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

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

The description is concise yet comprehensive. It is a single paragraph with front-loaded purpose, no redundant information, and every sentence serves a clear function.

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

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

Given the tool's simplicity (no output schema, no complex parameters), the description covers all necessary aspects: purpose, when to use, how to format, limitations, and processing. No gaps.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds some extra guidance for the 'message' field (be specific, describe in terms of tools/packs) and clarifies the 'type' enum's purpose, but the schema already provides good descriptions. Minimal added semantic 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?

Clearly states the tool's purpose: sending feedback to the Pipeworx team. Uses specific verbs ('tell') and resources ('Pipeworx team'). Distinguishes from sibling tools like ask_pipeworx which are for asking questions, not providing feedback.

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

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

Provides explicit guidance on when to use the tool for bugs, features, data gaps, praise, and other. Includes instructions on format (describe in terms of tools/packs, don't paste user prompt) and mentions rate limits. No alternatives mentioned but not needed.

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

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

Description adds behavioral context beyond annotations: self-aggregating, no PII, caching behavior. Annotations already provide readonly, idempotent, non-destructive hints; 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?

Well-structured with bullet points. Every sentence adds value without redundancy. Concise yet informative.

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

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

For a simple tool with one optional parameter and no output schema, the description covers purpose, usage, behavior, and parameter semantics completely.

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

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

Schema coverage is 100%. Description adds nuance: shorter windows for hot trends, longer for steady-state demand, which adds value beyond the enum values.

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

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

Description clearly states it returns top tools, packs, and call volume over a window, with specific use cases. Verb 'returns' is precise. Distinguishes itself from siblings like 'discover_tools' by focusing on current popularity.

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

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

Explicitly lists three use cases for when to use the tool. Does not specify when not to use or mention alternatives, but the context is clear enough for selection.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds rich detail on internal mechanics: monotonicity checks, partition-sum checks, Jaccard similarity threshold, placeholder filtering, and fill check against live CLOB depth. 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 dense and informative, with each sentence adding value. It is front-loaded with the purpose and modes. However, it is somewhat verbose and could be more structured (e.g., bullet points) for easier consumption, though still effective.

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

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

Given no output schema, the description fully details the output format (opportunities array with gap_pp, suggested_trade, etc., and partition_check in event mode). It covers edge cases like placeholder filtering and fill check, and references a sibling tool for additional functionality. Complete for a 2-param optional tool.

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

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

Schema coverage is 100%, but the description adds significant value: it explains the purpose and behavior of each parameter, that event accepts both slugs and URLs, and that topic expects a seed question. This goes far beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the resource (Polymarket) and the method, distinguishing it from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

The description provides explicit guidance on when to use each mode: no args for trending scan, 'event' for a specific market, and 'topic' for cross-event scanning. It also advises against trading when the fill check shows no realizable edge, and directs to polymarket_fill_risk for custom sizing.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds extensive context: caching (1h at KV level), output structure including diagnostics, and how knobs affect results. No contradictions; fully transparent.

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

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

The description is overly verbose, with detailed explanations of model families and mechanics that could be shortened. It front-loads purpose but then delves into excessive technical details. A more structured format with bullet points would improve readability without losing information.

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

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

The tool has 9 parameters and no output schema, yet the description covers all aspects: purpose, usage, parameter semantics, behavioral details (caching), output structure (by_segment, diagnostics), and edge cases. It is fully complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, but the description adds significant value beyond schema descriptions. It explains how parameters like min_liquidity and max_spread_pp act as tradeable-edge filters, and provides context for defaults and interactions. Some parameters, like window, have clear enums where description adds little, but overall enhances understanding.

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

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

The description clearly states the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It specifies the use case 'what should I bet on today' and distinguishes from siblings like `polymarket_arbitrage` by focusing on edge detection across multiple models.

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

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

The description provides explicit guidance on when to use the tool: for discovering opportunities without paging hundreds of markets. It details tradeable-edge knobs and caching behavior. However, it does not explicitly list alternatives or when not to use, though sibling names imply other tools for specific tasks.

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

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

Annotations already declare non-destructive, read-only, idempotent. Description adds rich context: history bounded by 60-day TTL, snapshots on cache-miss, gaps mean no scan that day, decay from daily closes, and response structure details.

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?

Dense but effective. Could be slightly more structured (e.g., bullet points for response fields). However, front-loaded with key question and no wasted sentences.

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

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

No output schema, but description fully explains return values (tracked, expired, snapshot_dates) with fields, caveats, and limits. Very complete for 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 covers both params with descriptions. Description adds value: clarifies default/max for days, default for window, and explains how parameters affect returned data (snapshot family).

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, answering 'how long has this edge existed and is it shrinking?'. It distinctly differentiates from sibling 'polymarket_edges' which likely returns raw snapshots.

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

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

Explicitly tells when to use (understanding edge age/decay) and implies when not (use polymarket_edges for current edges). Includes limits like 60-day TTL and snapshot gaps.

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

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

Annotations already indicate safe read-only, idempotent behavior. The description adds valuable behavioral context: it walks the order-book ladder, returns a verdict (clean/degraded/cannot_fill), and warns about partial basket fills converting arbs into unhedged directional positions, which goes beyond the annotations.

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

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

The description is dense but well-structured: it leads with the main purpose, then breaks into single-market and basket sections, followed by usage guidance. While long, every sentence serves a purpose. Minor redundancy could be trimmed, but overall it's efficient for the 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?

Despite lacking an output schema, the description fully details the return fields for both modes (e.g., top_of_book, vwap_fill_price, capture_ratio, thin_legs, max_clean_notional_usd). It covers inputs, behaviors, and risks comprehensively, making it 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?

Input schema has 100% description coverage. The description enriches parameter meaning by explaining the two modes ('market' vs 'event'), default side behavior in basket mode, and how 'size_usd' is interpreted differently in single-market vs basket mode. This adds value beyond the schema's field 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 explicitly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It clearly distinguishes single-market and basket modes, with specific return fields, making the purpose unmistakable and distinct from siblings like polymarket_arbitrage and polymarket_edges.

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

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

The description provides explicit usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains the rationale—theoretical overround on thin books is not capturable and partial basket fills introduce directional risk—giving clear when-to-use and when-not-to-use context.

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

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

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds significant behavioral context: safety fields (compatibility_warning, temporal_alignment, skipped counters), explanation of when spreads are meaningful vs not, and that many pre-mapped topics return warnings. 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 lengthy but well-structured into concept, modes, response, safety fields, and warnings. It front-loads the core purpose and is logically organized. Minor verbosity 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?

Without an output schema, the description thoroughly explains the response structure (leg prices, top spreads, safety fields) and important constraints like temporal alignment and compatibility warnings. It covers all relevant aspects for an agent to understand and use the tool effectively.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the two modes (topic vs explicit), listing the topic options, and stating that explicit parameters override the topic side. This goes beyond the schema's simple 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 identifies the tool as computing cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself from siblings like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

The description explains two modes (topic shortcuts vs explicit tickers) and warns that many pre-mapped topics are not currently tradeable, guiding the agent to use explicit modes when needed. It does not explicitly contrast with sibling tools but provides context for appropriate 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 readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds scoping detail ('Scoped to your identifier') and pairing guidance, providing value beyond the structured 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 focused sentences, each adding value. Core action is front-loaded. No wasted words.

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

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

For a simple read-only retrieval tool with one optional parameter and no output schema, the description covers purpose, usage, scoping, and pairing. No obvious gaps.

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

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

Schema coverage is 100% with a description for the single parameter. Description adds meaning by explaining the optionality ('omit to list all keys'), which is not fully captured in the schema alone.

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

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

Clearly states the verb ('Retrieve' / 'list') and the resource ('value previously saved' / 'all saved keys'). Explicitly distinguishes from siblings 'remember' and 'forget'.

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

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

Provides concrete examples of when to use (look up user's target ticker, address, research notes). Implicitly indicates not to use for saving or deleting, but does not explicitly state exclusions or alternatives beyond sibling names.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds value by detailing mark_read behavior (flagging events read so next call shows newer ones) and noting the same feed is available via HTTP. No contradictions.

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

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

Four sentences, front-loaded with main purpose, no redundancy. Each sentence adds necessary context (return fields, filters, side effect, alternative access).

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

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

No output schema, but description explains return fields (source, citation_uri, raw payload). Also mentions alternative HTTP endpoint. Lacks details on pagination or error handling, but sufficient for typical polling use case.

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 contextual value for 'mark_read' by explaining its impact on subsequent calls. Other parameters (type, since) are mentioned but not deeply expanded, which is acceptable given 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?

Description clearly states 'Pull fired events from your subscription feed' and details what is returned (source, citation_uri, raw payload). It specifies filtering options and a side effect (mark_read), distinguishing it from other tools like 'recent_changes'.

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

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

No explicit when-to-use or when-not-to-use compared to sibling tools. Mentions an HTTP endpoint as an alternative for scripts/dashboards, but does not guide agent on tool selection 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 indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing fan-out to multiple sources, fallback mechanism, and soft-failure for USPTO, providing context beyond annotations.

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

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

The description is informative but somewhat verbose, with multiple clauses detailing sources and fallback. It front-loads with examples but could be more concise without losing essential details.

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

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

Given the tool's complexity (multiple data sources, fallback, soft-fail), the description covers return structure, parameter formats, and alternative tool usage. No output schema, but description compensates 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?

With 100% schema description coverage, baseline is 3. The description enhances parameter understanding by providing examples for 'since' (ISO and relative), explains 'value' accepts ticker or CIK, and notes 'type' 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?

The description uses specific examples like "What's new with X" and defines the tool as a change feed for a company over a window, clearly distinguishing it from the sibling tool entity_profile for static profiles.

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

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

The description explicitly guides to use entity_profile for static profiles, implies usage for dynamic updates, and explains fallback behavior. However, it could more clearly state when not to use this tool versus other siblings like recent_alerts.

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 idempotentHint=true, destructiveHint=false. The description adds context: memory is key-value scoped by identifier, authenticated users get persistent memory, anonymous sessions last 24 hours. This goes beyond annotations but doesn't fully specify all side effects, hence a 4.

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

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

Three sentences with no fluff. The most critical information (purpose, when to use, scoping) is front-loaded. Every sentence adds value.

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

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

Given the simplicity of the tool (2 params, no output schema), the description covers purpose, usage guidelines, storage semantics, and pairing with siblings. No gaps remain for effective use.

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

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

Input schema covers 100% of parameters with descriptions. The description adds usage examples (e.g., 'subject_property', 'target_ticker') and explains that value can store 'findings, addresses, preferences, notes', which enhances understanding beyond the schema.

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

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

The description clearly states 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes itself from sibling tools like 'recall' and 'forget' by explicitly mentioning them, making the purpose unambiguous.

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

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

The description provides explicit guidance on when to use this tool: 'Use when you discover something worth carrying forward' and contrasts with siblings via 'Pair with recall to retrieve later, forget to delete.' It also explains scoping by identifier and persistence duration.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds critical context: cascading multiple lookup endpoints, auto-disambiguation, citation URI returns, and specific format outputs. This goes beyond annotation basics.

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 example queries and a clear usage directive, then systematically covers supported types. Every sentence adds value; no redundancy. Efficient for the complexity of the tool.

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

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

Despite no output schema, the description fully explains return values for both entity types, including citation URIs. It covers behavior (cascading lookups), edge cases (auto-disambiguation), and integration with other tools. Complete for a lookup tool.

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

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

Schema covers all 2 parameters with descriptions. The description enhances both: for 'type', provides example outputs per enum value; for 'value', gives concrete examples (AAPL, ozempic) and notes auto-disambiguation. Adds meaning beyond schema.

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

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

The description clearly states it resolves user-spoken names to official identifiers, lists specific query patterns, and details the two supported entity types (company, drug) with precise outputs. It distinguishes itself from sibling tools by positioning as the primary lookup tool for IDs.

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 'Use FIRST whenever you have a name but need an ID,' establishing clear priority. Notes that it replaces 2-3 manual lookups, implying efficiency. No explicit when-not-to-use, but the context is strong.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds value by explaining the internal process (calls ai_visibility_check for each entity, ranks results) and the return format (score, confidence, signal density). No contradictions.

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

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

Three short sentences: first states purpose and process, second provides usage example, third specifies return content. No fluff, front-loaded, 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 no output schema, the description adequately explains what is returned (ranked list with score, confidence, signal density per entity). It also covers all parameters implicitly through the process description. Sufficient for an agent to understand the tool's full behavior.

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

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

Schema coverage is 100% with descriptions. The description adds extra meaning: first entity is treated as 'subject' for narrative, and notes that omitting models defaults to just workers-ai. These nuances aid correct parameter usage.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, uses specific actions (probes with ai_visibility_check, ranks, surfaces most/least recognized), and distinguishes from sibling tool ai_visibility_check which is for single entities.

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

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

The description provides a concrete use case (competitive AI-marketing audits) and implicitly contrasts with single-entity check via mention of ai_visibility_check. However, it does not explicitly state when not to use or list alternatives.

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

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

Annotations already set readOnly, idempotent, non-destructive. The description adds crucial behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. It also outlines the return structure (summary block, advisories, links, alternatives).

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

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

The description is relatively long but front-loaded with the core purpose. Every sentence adds unique information. Could be slightly trimmed but remains efficient and well-structured.

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

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

Given the composite nature (two external APIs), lack of output schema, and potential for partial failures, the description is thorough. It covers inputs, outputs, failure handling, and performance nuance. An agent can fully understand what to expect.

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

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

Schema coverage is 100% with both parameters documented. The description adds minor value: clarifies that package accepts scoped packages and version defaults to latest. This is helpful but not substantial 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 opens with a clear purpose: a composite check for adding an npm package. It names the exact resources (deps.dev and bundlephobia) and specifies the scope (NPM only), distinguishing it from any sibling tools.

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

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

Explicitly states when to use: when an agent asks about safety, popularity, size, or cost of adding a package. Also provides exclusion guidance: for non-NPM ecosystems, use deps.dev:version directly. No ambiguity.

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

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

Discloses embedding model (BGE-base-en), chunking (500-char overlapping windows), character limits (200K chars with truncation flag), and return fields (offsets, similarity scores). Annotations already declare idempotent and read-only hints, and description adds contextual behavior.

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

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

Two sentences front-loaded with purpose and usage, followed by concise technical details. 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?

Explains mechanics (embeddings, chunking, truncation, offsets) and pairs with ask_pipeworx_grounded. Despite missing output schema, description sufficiently covers return format. Annotations provide additional safety context.

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

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

Schema covers all 3 parameters with descriptions. Description adds no further parameter-specific meaning beyond schema; baseline 3 due to high schema coverage.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' with specific verb and resource, and distinguishes from siblings by noting it is for when the record is too large, pairing with ask_pipeworx_grounded.

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

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

Explicitly states when to use ('Use when the record is too big to cram into the prompt') and mentions a complementary tool, but does not explicitly state 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?

The description discloses key behavioral traits beyond annotations: authentication requirement, persistence of subscriptions, delivery channel limitations (10/day SMS cap, webhook auto-disable after 10 failures), and that the feed is always on. No contradiction with annotations (idempotentHint is consistent).

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

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

The description is comprehensive but slightly lengthy. It is front-loaded with the core purpose and return value. Every sentence provides necessary detail, though a more structured format (e.g., bullet points) could improve readability without losing information.

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

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

Given the complex nested parameter and no output schema, the description covers all aspects: authentication, parameter details, return value, delivery options, and constraints like webhook signing secret and auto-disable. It is complete enough for an agent to use the tool correctly.

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

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

The description adds significant meaning beyond the input schema: each type is explained with concrete examples (e.g., sec_8k items codes, polymarket_edge topics), params structure is detailed, and delivery channels include webhook signing secret behavior. Schema coverage is 100%, but description enriches usage.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It specifies the verb (create), resource (subscription), and distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides detailed context: requires Pipeworx OAuth account, lists supported types with examples, and explains delivery options with constraints (SMS cap, webhook auto-disable). It does not explicitly state when not to use, but the sibling tools cover other operations, so guidance is clear.

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

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

Annotations already declare readOnlyHint etc. Description adds that results come from live tool catalog and include tool+argument shapes, beyond annotation coverage.

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 common user queries, structured with categories. Slightly long but every sentence adds value.

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

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

For an onboarding tool without output schema, description sufficiently explains purpose, usage, parameter, and return format (category-bucketed examples with tool shapes).

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 topic 100%, description adds value by explaining omission vs topic focus effect (full spread vs focused).

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

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

Description clearly states it returns category-bucketed example questions with tool+argument shapes. Distinguishes from siblings like ask_pipeworx (answers vs suggests questions).

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions optional topic parameter for focus.

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

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

The description adds behavioral context beyond annotations: it explains that the row is deactivated (not deleted) and historical events remain available via 'recent_alerts'. This complements the annotations (destructiveHint=false) and provides useful transparency. No contradiction with annotations.

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

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

The description is concise with two sentences, front-loading key information (action, ownership, effect). Every sentence adds value without redundancy.

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

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

For a simple one-parameter tool with no output schema, the description covers the essential aspects: action, ownership constraint, and behavioral impact. It references the related sibling 'recent_alerts' for context, making it fairly 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% and the schema describes the 'id' parameter as 'Subscription id (uuid) returned by subscribe.' The description mentions 'by id' but adds no additional semantics beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the action 'Cancel a subscription by id' and the resource (subscription via id). However, it does not explicitly differentiate from sibling tools like 'subscribe' or 'list_subscriptions', which are closely related.

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

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

The description includes a key usage condition: 'Ownership is enforced — you can only cancel your own subscriptions.' However, it lacks guidance on when not to use this tool or mention of alternatives like 'recent_alerts' for viewing historical data.

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 that it returns a verdict, structured form, actual value with citation, and percent delta. It also specifies the data source (SEC EDGAR + XBRL) and current limitations (v1). No contradiction with annotations.

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

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

The description is dense and includes many examples and details, which is helpful but somewhat verbose. It could be streamlined without losing clarity. The use of bullet points or shorter sentences might improve readability.

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

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

Given a single parameter and no output schema, the description covers the tool's purpose, usage, scope, limitations (v1, company-financial), and return output. It provides enough context for an agent to decide when to invoke this tool.

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

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

There is only one parameter 'claim' with a schema description that already explains it. The description provides additional examples but does not add new semantic information beyond the schema. Since schema coverage is 100%, baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: fact-checking natural-language claims against authoritative sources. It provides specific examples of query types and explicitly mentions the scope (company-financial claims via SEC EDGAR + XBRL), distinguishing it from general search 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 advises 'Use whenever the agent needs to check whether something a user said is factually correct.' It also notes the tool replaces multiple sequential calls, implying efficiency. However, it does not explicitly mention when not to use it or directly compare with sibling tools like deep_research or resolve_entity.

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