Myhealthfinder

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. Description adds value by detailing pricing (free default, BYO key for Anthropic) and the return structure per model.

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?

3-4 sentences, front-loaded with main action, no redundant information. Every sentence adds value.

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

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

For a tool with 4 params and no output schema, description covers input, behavior, output format, and pricing. Mentions return structure explicitly, filling the gap of 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 description coverage is 100%, so baseline is 3. Description adds context beyond schema: e.g., default model details, example for entity param, and when _apiKey is required. Justifies higher score.

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

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

Clearly states the tool probes LLMs for brand/business visibility and scores it 0-100. Distinguishes from siblings like 'ask_pipeworx' and 'scan_competitor_ai_presence' by focusing on multi-model visibility scoring.

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 mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains default model and when to provide _apiKey, but does not list alternatives or when not to use.

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

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

Annotations already provide readOnly/openWorld/idempotent hints, so the bar is lower. The description adds valuable context: it routes to thousands of sources, returns structured answers with stable citation URIs, works on all tiers, and is a single fast call. It doesn't mention failure modes or response size, but that's beyond annotations' scope and the added context justifies 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?

The description is long but every sentence earns its place: the initial preference directive, the domain list, usage triggers, concrete examples, and upgrade path. It is front-loaded with the most important instruction. It could be slightly tighter without losing value, but as-is it is highly structured and scannable.

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

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

For a complex meta-tool with no output schema, this description is remarkably complete. It covers purpose, supported domains, when to use alternatives, return format (structured answer with citations), and scope (5,529 tools/1,455 sources). The sibling list and annotations complement it, 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% with all parameters documented as aliases for 'question', so baseline is 3. The description enriches this by explaining what kinds of questions to ask, providing examples ('current US unemployment rate', 'patents Tesla was granted last month'), and highlighting that the question will be routed across many tools. This adds meaning beyond the schema's simple 'Your question or request in natural language'.

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 answers factual questions with authoritative structured data, enumerates specific domains (SEC, FDA, FRED, etc.), and clearly distinguishes it from siblings like ask_pipeworx_grounded and deep_research. The verb 'routes' and scope '5,529 tools across 1,455 verified sources' makes 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?

It explicitly says 'PREFER OVER WEB SEARCH' and 'START HERE for most questions', provides concrete trigger phrases ('what is', 'look up', 'current'), and gives step-up alternatives with exact tool names for specific needs (grounded for single factual answers, deep_research for broad questions). This is exemplary usage 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?

Beyond the readOnly/idempotent annotations, the description discloses that this is an experimental edge with candidate routing improvements enabled live, that no candidate is currently active, and that it falls back to nothing—emphasizing it is a fully working router. This adds significant context about the tool's dynamic nature and current stability without contradicting annotations.

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

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

The description is longer than ideal—six sentences—but every sentence contributes useful information (beta nature, current status, usage, comparison, fallback). It is front-loaded with the core definition and structured logically, though it could be slightly tightened without losing value.

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

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

For a universal router with no output schema, the description is remarkably complete: it explains what the tool does, its experimental state, when to use it, how it relates to ask_pipeworx, and that the response shape is identical to the stable version. The annotation and schema richness are complemented well.

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

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

The input schema provides 100% parameter coverage with detailed aliases, so the baseline is 3. The description only adds 'same arguments' as ask_pipeworx, which marginally references parameter compatibility but doesn't expand beyond the schema. No additional parameter meaning is provided.

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

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

The description clearly identifies the tool as a beta version of ask_pipeworx, an identical universal router with the same tools, arguments, and response shape. It distinguishes itself from the stable ask_pipeworx sibling by emphasizing its experimental routing improvements, leaving no ambiguity about its purpose.

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

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

Explicitly states to 'Use it exactly like ask_pipeworx when you want the newest routing,' and notes that results are compared against the stable router. It also clarifies the current inactive candidate state, confirming it is a drop-in replacement at the moment. This directly addresses when to choose this tool over the sibling.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive. The description goes far beyond annotations by disclosing the exact extraction behavior (only tool result content is used), the structured success response with evidence and confidence, and explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also mentions the extra LLM call cost, adding valuable transparency.

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

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

The description is dense but every sentence contributes: purpose, routing mechanism, extraction constraint, output format, refusal reasons, usage context, and cost tradeoff. It is front-loaded with the core purpose and continues with high-value specifics, earning each sentence's 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?

The description is exceptionally complete for a complex tool with no output schema. It specifies both success and refusal response structures, explains when to use and when to avoid, discloses the additional LLM call cost, and is complemented by strong annotations (readOnly, idempotent). There are no significant gaps.

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

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

Schema coverage is 100% — all six properties are aliases for the single 'question' parameter with a clear description. The tool description does not add parameter-specific information beyond what the schema already provides, so the 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 this is a 'Hallucination-resistant answer mode for high-stakes reads' and explains the mechanism: same routing as ask_pipeworx, then extracts the answer using ONLY what the tool result contains. This distinguishes it from sibling tools like ask_pipeworx and validate_claim, making the purpose unmistakable.

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

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

Provides explicit usage guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with examples (financial verdicts, legal claims, medical lookups, public statements). It also gives a clear when-not-to-use: 'prefer ask_pipeworx for casual lookups' with the cost reason, directly naming the alternative.

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 indicate read-only, idempotent, open-world, non-destructive behavior. The description greatly expands on this, detailing low-confidence resolution short-circuiting, closed market handling, wide spread warnings, resolution-rule risk parsing, and response shapes. 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 very long and detailed, which is understandable for a complex tool. It is front-loaded with the main purpose and uses capitalized section headers for structure. However, it could be more concise and better organized with bullet points or shorter paragraphs.

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 thoroughly explains the response structure including market, analysis, evidence, resolver contract, parent event, news fields, and safety mechanisms. It covers edge cases and provides examples, making it complete for the tool's purpose.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. The description adds context for the 'market' parameter (accepts slug, URL, or question text) and for 'include_raw' (explains when to use true/false with size implications). This adds meaningful value beyond the schema.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies the input types (slug, URL, question text) and the output (evidence packet + comparison). This distinguishes it from sibling tools like polymarket_arbitrage or polymarket_edges, which focus on specific aspects.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"', providing clear use cases. However, it does not mention when not to use this tool or suggest alternatives among the many sibling tools.

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

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

Annotations already indicate readOnly, openWorld, and idempotent. The description adds rich behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, and that results are sorted by primary metric. No contradictions.

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

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

Description is well-structured starting with common query patterns, then usage guidance, then type-specific details. Each sentence adds value, though some redundancy exists (e.g., listing multiple metrics). Overall efficient for the information conveyed.

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

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

Given the tool's complexity (two entity types, multiple data sources) and the absence of an output schema, the description is remarkably complete. It covers purpose, usage, parameter details, return format (paired data + citation URIs), and sorting. No significant gaps.

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

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

Input schema covers both parameters with descriptions (100% coverage), baseline 3. The description adds usage examples (tickers/CIKs for company, drug names) and clarifies constraints (min 2, max 5), providing value beyond the schema.

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

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

The description explicitly states it performs side-by-side comparison of 2-5 companies or drugs in a single parallel call, with specific query patterns like 'Compare X and Y' or 'X vs Y'. It clearly distinguishes from sequential single-entity lookups.

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

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

It contains an explicit directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also explains when to use each 'type' (company vs drug) and notes efficiency gains (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?

Annotations indicate read-only, idempotent, and open-world behavior, but the description adds extensive context: account/payment requirements, latency expectations, gaps[] handling, contradictions[] for standard/thorough, semantic excerpting, citation_uri conditions, and hop fields. No contradiction with annotations; the description goes far beyond 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?

Though long, every sentence conveys essential information: auth, pricing, purpose, alternatives, return structure, latency, and edge cases. It is front-loaded with account requirements and purpose, and the structure flows logically. No filler or 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?

Without an output schema, the description fully explains the return packet (evidence, confidence, source, fetched_at, citation_uri), gaps[], contradictions[], hop fields, and latency. It also covers account prerequisites and use-case constraints. For a tool with only two params and high complexity, 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%, and both parameters ('question' and 'depth') are fully described in the schema. The description reinforces these meanings (e.g., depth variants, question decomposition) but adds no new parameter-specific semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states what the tool does: it performs grounded multi-source research across 1455 structured data sources, decomposes questions into facets, routes to 5,529 tools in parallel, and returns a findings packet. It also explicitly distinguishes itself from siblings (e.g., 'For a single lookup use ask_pipeworx', 'this is NOT open-web search'), making it unambiguous.

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

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

The description provides explicit usage guidance: best for broad/multi-part structured-data questions, use ask_pipeworx for single lookups, prefer ask_pipeworx for breaking/current news. It also notes account requirements and when to use the alternate tool if not signed in. This fully covers when/when-not and alternatives.

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

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

Annotations already declare readOnly=true, idempotent=true, destructive=false. Description adds behavioral details: returns top-N most relevant tools with names, descriptions, full input schemas, and curated examples, ready to call directly without further schema lookup. No contradictions.

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

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

Description is moderately long but every sentence adds value: purpose, usage scenario, return behavior, and call-to-action. Front-loaded with key information. Slightly verbose but not wasteful.

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

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

With no output schema, the description adequately explains return structure (top-N tools with names, descriptions, and schemas). It covers usage context and behavior. Lacks detail on error cases or pagination, but the limit parameter is in 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% (all 6 parameters fully described). Description adds value by noting that 'task, q, description, search' are aliases for 'query', but this is a minor clarification. Baseline 3 is appropriate as schema does the heavy lifting.

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

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

Description explicitly states the tool finds tools by describing data or task, listing numerous domains (SEC filings, financials, FDA drugs, etc.). It distinguishes from sibling tools by advising 'Call this FIRST when you have many tools available,' differentiating it from direct-answer or specialized 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?

Description provides clear guidance: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available.' It implies when not to use (if only one answer needed) but does not explicitly name alternative tools.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds extra behavioral details: patents API sunset (soft-fail), news fallback chain, and explicit output fields. 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 information-dense but slightly verbose in listing return elements. However, it is well-structured, front-loaded with example queries, and every sentence contributes necessary detail.

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 structure, covers edge cases (patents sunset, news fallback), and provides prerequisites. It is complete for a complex tool with multiple data sources.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by clarifying the 'value' parameter format (ticker or zero-padded CIK), giving examples, and explicitly stating that names are not supported.

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

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

The description explicitly states the tool's purpose: providing a holistic cross-source profile for US public companies. It uses specific verbs ('full cross-source profile'), lists data sources, and distinguishes from siblings like 'resolve_entity' and 'compare_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 clear when-to-use guidance ('ALWAYS PREFER over chaining single-pack...' when holistic view is needed) and when not to use ('names not supported – use resolve_entity first'). It includes example user queries that trigger this tool.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true; the description adds minimal additional behavior (e.g., no mention of permanent deletion or missing key 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 concise sentences efficiently cover purpose and usage, with no superfluous words.

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

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

For a simple delete tool with full annotations and schema, the description fully covers what, when, and how to use.

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

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

Schema coverage is 100% with clear parameter description; the tool description does not add extra meaning beyond what the schema provides.

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

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

The description uses specific verb 'delete' and resource 'memory by key', clearly distinguishing it from sibling tools like '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 done, clear sensitive data) and mentions pairing with 'remember' and 'recall', though lacks explicit when-not-to-use conditions.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive traits. The description adds behavioral context by explaining the process (fetches page, extracts title/description/key links) and output format (standard markdown blob). This goes beyond annotations without contradiction.

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

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

The description is two sentences plus a short bullet list of use cases. It is front-loaded with purpose, every sentence adds value, and there is no extraneous text.

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

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

For a tool with 2 parameters (both described), comprehensive annotations, and no output schema needed (plain text), the description covers behavior, output format, and use cases entirely. No gaps remain.

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

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

Schema description coverage is 100%, so the schema already documents parameters. The description adds value by specifying default (25) and max (50) for max_links and reiterating URL format. This enhances understanding beyond 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?

The description clearly states it generates an llms.txt file for AI crawlers, specifying the action (generate), resource (llms.txt file for any URL), and output format. It distinguishes itself from sibling tools by focusing on a specific standard file format, with no overlap in functionality.

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

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

The description explicitly lists three use cases (client site indexing, self-project drafting, competitor auditing). It does not mention when not to use or alternatives, but the sibling tools list shows no direct alternative, so the guidance is clear and contextually sufficient.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, fully covering safety behavior. The description does not add behavioral details beyond these, which is acceptable but not additional transparency.

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

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

Two sentences, front-loaded with action and parameter source, followed by return fields. Every word adds value, no redundancy.

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

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

Given no output schema, the description lists all return fields (title, source URL, date, sections). It does not mention error handling or format details, but for a simple read fetch it is adequate.

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 that the numeric id comes from specific sibling tools, which enhances understanding beyond the schema description 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?

The description clearly states the tool fetches full plain-language guidance for one health topic by numeric ID, using the verb 'Fetch' and specifying the return fields. It distinguishes itself from siblings like search_health_topics and get_preventive_recommendations that provide IDs or different scopes.

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

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

The description explicitly tells the agent to obtain the topic_id from search_health_topics or get_preventive_recommendations, providing clear context for use. It does not explicitly state when not to use, but the use case is well-defined.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable contextual information: the data source (ODPHP/health.gov), the output structure (guidance topic, categories, plain-language sections, source URL), and the personalization based on optional statuses. This goes well beyond the annotations, providing a complete behavioral picture.

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

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

The description is a single paragraph that front-loads the most critical guidance ('PREFER OVER WEB SEARCH') and follows with specific usage. It uses bold for emphasis and provides examples. While not extremely concise, every sentence adds value, and the structure is logical. A minor improvement could be splitting into two sentences for readability.

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

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

Given the moderate parameter count (5) and no output schema, the description adequately covers both input and output. It explains the purpose of each optional parameter, the return content (topic, categories, sections, source URL), and the data origin. There is no missing information that would hinder an agent from using the tool correctly.

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

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

Schema coverage is 100% with clear descriptions for all parameters. The description reinforces the meaning of each parameter by giving example values and explaining how they affect the recommendations (e.g., 'age 50 male → colorectal cancer screening'). This adds context beyond the schema, helping the agent understand how parameters influence results.

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

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

The description clearly states the tool returns personalized USPSTF-based preventive-service recommendations based on age and sex. It specifies the exact use cases (health screenings, checkups) and distinguishes itself from web search by explicitly saying 'PREFER OVER WEB SEARCH'. The verb 'Returns' combined with the resource 'preventive-service recommendations' provides a specific, actionable purpose.

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

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

The description provides explicit usage scenarios (e.g., 'what health screenings should a 50-year-old man get') and clearly states when to prefer this tool over web search. It gives concrete examples of queries and notes optional parameters (pregnancy, tobacco, sexual activity). This offers clear guidance on when to invoke this tool versus alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the data source (ODPHP/health.gov) and that it returns a catalog of all items. It does not contradict annotations and provides useful scope beyond the structured fields.

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 examples. Every word is meaningful; no filler or repetition. Highly efficient.

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

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

Given the tool's simplicity (one optional parameter, safe annotations), the description is largely complete. It explains what the tool does and how to use it, though it does not detail the return format (e.g., names vs. IDs). Without an output schema, a brief mention of output structure would be slightly more helpful, but not essential.

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

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

The input schema covers the single parameter 'type' with a description. The description adds concrete examples of categories (Cancer, Diabetes, Heart Health) and clarifies the default value of 'topic'. This enhances understanding of what the parameter values represent, adding value over 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?

The description clearly states the tool lists all health topics or categories from the ODPHP/health.gov catalog, using specific verbs ('browse', 'list') and resource references. It distinguishes from sibling tools like search_health_topics by noting its use for discovery before searching.

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

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

The description explicitly advises using this tool to discover what guidance exists before searching, implying it is for broad overview rather than specific queries. It names the two modes (topics, categories) but could more explicitly contrast with search or get tools.

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

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

Annotations already indicate readOnly, idempotent, and not destructive. The description adds detail: returns specific fields, defaults to active subscriptions only, and mentions the include_inactive parameter. 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?

Two sentences, front-loaded with the main action. Each sentence serves a clear purpose: stating the function and providing usage guidance.

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

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

No output schema, but description lists return fields adequately. Lacks mention of pagination, but for a simple list tool with no required params, it is sufficiently complete.

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

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

Schema coverage is 100% with the parameter 'include_inactive' already described in the input schema. The description adds minimal extra meaning beyond noting active by default.

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 also lists the return fields, distinguishing it from sibling tools like subscribe 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?

Explicitly advises when to use: 'Use this to review what you're monitoring before adding more or to find an id to cancel', providing context and exclusionary 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 only convey crudely that this is a mutating operation (readOnlyHint=false). The description adds significant behavioral context: rate-limited to 5 per identifier per day, free and not counted against quota, returns a claim_token when filing without an account, and how to use that token later to read resolution status. No contradiction with annotations.

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

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

The description is longer than average but every sentence serves a purpose, and it is front-loaded with the core purpose before caveats and mechanics. Slight deduction for motivational/roadmap sentence and the length, though the density of valuable guidance justifies most of it.

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 claim_token return flow, rate limits, and routing rules. With 4 parameters (one nested), this is a moderately complex tool, and the description covers the necessary behavioral and usage aspects comprehensively for an agent to invoke it correctly.

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

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

Schema coverage is 100% for all 4 parameters, so the baseline is 3. The description adds meaningful guidance beyond the schema, notably instructing users to describe issues in terms of Pipeworx tools/packs and not paste end-user prompts, plus clarifying that claim_token should be passed with no other arguments. This elevates it above baseline.

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

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

The description opens with a specific verb+resource statement: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' This clearly distinguishes it from sibling tools like ask_pipeworx or research tools, which query for information rather than submit 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?

Explicitly specifies when to use the tool ('Use when a tool returns wrong/stale data... when a tool you wish existed isn't in the catalog... when something worked surprisingly well') and provides a clear exclusion: do not use for tools from other MCP servers, file with that server instead. This is textbook when/when-not/alternatives 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, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and the nature of the data (aggregated counts). No contradictions with annotations.

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

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

The description is a single concise paragraph with front-loaded purpose, bullet-style use cases, and technical details. Every sentence adds unique value, 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?

For a simple tool with one optional parameter and no output schema, the description covers purpose, use cases, data source, privacy, caching, and window semantics. It provides everything an agent needs 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 description coverage is 100%; the 'window' parameter already explains the options and their implications ('hot now' vs 'steady-state'). The description reinforces this but does not add new parameter-level meaning beyond what the schema provides.

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

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

The description clearly states the tool returns top tools, packs, and call volume based on what other AI agents are using. It distinguishes from siblings like 'ask_pipeworx' (question-answering) and 'discover_tools' (general discovery) by focusing on trending usage.

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

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

The description lists three specific use cases (discovering hot data, confirming canonical tools, aligning use cases) but does not explicitly mention when to avoid or suggest alternative tools. It provides clear context but stops short of full guidance.

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

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

Annotations declare readOnlyHint, idempotentHint, and non-destructive. The description adds context on fill checking, semantic anchors, partition filters, and output details, fully disclosing behavior beyond annotations.

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

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

The description is detailed and well-structured with clear sections. While lengthy, every sentence provides essential information for a complex tool. It could be slightly more streamlined but is appropriate 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?

The description covers all aspects: modes, parameters, output format, fill check, and references to related tools. No output schema exists, but the description adequately explains the response structure, making it complete for agent use.

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

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

Schema covers 100% of parameters with descriptions. The tool description enriches this with examples, mode-specific behavior, and accepted URL formats, adding significant value beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity and partition-sum checks. It distinguishes three modes (trending, event, topic) and explains their use, setting it apart from sibling tools like 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?

Explicitly states when to use each mode: no args for trending scan, event for specific market, topic for cross-event scanning. It also advises against trading when realizable_edge_pp ≤ 0, providing clear when-not-to-use guidance.

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

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

The description adds substantial behavioral context beyond the annotations: it explains caching behavior ('Cached 1h at the KV level keyed on all knobs'), response structure (by_segment, fed_candidates, diagnostics), and why Fed bets are excluded. There is no contradiction with the readOnlyHint/openWorldHint annotations, and it provides rich detail on how the tool processes data.

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

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

The description is front-loaded with the tool's purpose and appropriately detailed given the complexity. Each sentence adds value, explaining model families, response segments, and diagnostics. While lengthy, it avoids redundancy and maintains a logical flow, though some repetition could be trimmed.

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

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

Given the tool's complexity (9 parameters, no output schema), the description is remarkably complete. It covers caching, response structure, diagnostics, model families, and parameter knobs. The explanation of Fed bet exclusion demonstrates thoroughness, ensuring agents understand limitations without needing additional context.

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

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

The input schema already has 100% parameter description coverage, so the baseline is 3. The tool description adds value by explaining the interplay of tradeable-edge knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly) and how they filter opportunities. This contextual understanding enhances the agent's ability to set parameters correctly.

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. The phrase 'Built for what should I bet on today' succinctly captures the use case. However, it does not explicitly differentiate from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, which could lead to confusion about which tool to use.

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

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

The description provides context for use ('discover opportunities without paging hundreds of markets') and mentions that Fed bets are excluded from ranking due to unreliable data. However, it does not specify when not to use this tool or provide clear alternatives, leaving the agent to infer usage from the detailed logic.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds rich behavioral context: response structure (tracked, expired, snapshot_dates), meaning of decay numbers, limits (60-day TTL), and computation details (daily closes). This goes well beyond annotations and provides full transparency.

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

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

The description is fairly long but well-structured: purpose first, then response fields, then limits. It is front-loaded with the key question and earns its length by providing necessary context for a complex telemetry tool. Minor redundancy in parameter restatement prevents a perfect score.

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

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

No output schema exists, so the description must explain return values. It thoroughly describes tracked[], expired[], and snapshot_dates[], along with limits and decay computation. This is complete for the tool's complexity and leaves no major 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 both parameters described. The description restates days (lookback, default 14, max 30) and window (snapshot family, default '1wk'), adding minimal extra meaning. It does not describe parameter interactions or constraints beyond what the schema provides, so it meets the baseline of 3.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' and answers a specific question: 'how long has this edge existed and is it shrinking?' It uses specific verbs and resources, distinguishing it from siblings like polymarket_edges, which likely return current data, while this focuses on historical tracking.

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 context on when to use the tool by explaining the significance of edge age ('a fresh wide edge and a 3-week-old wide edge are different trades'), implying historical analysis. It does not explicitly name alternatives or exclusions, but the purpose is clear enough to guide 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds context about real-time order book checks, the verdict output, and forced_directional_risk naming. It does not contradict annotations; however, it could explicitly state that the tool does not execute trades, only simulates. Still, it adds significant value.

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

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

The description is fairly long but well-structured with labeled sections (REQUIRES, SINGLE-MARKET, BASKET). Every sentence provides necessary detail, though minor trimming could improve conciseness. Overall, it earns its length.

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

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

Without an output schema, the description comprehensively lists return fields for both modes, including theoretical_sum, realizable_sum, capture_ratio, profit_usd, thin_legs, and forced_directional_risk. It explains the meaning of thin_legs and forced_directional_risk, and mentions default size and clamp range. The description is fully adequate for an agent to understand the tool's outputs.

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 elaborates on default values (size_usd default 1000, side default buy_yes for single-market and auto for basket), explains interpretation of size_usd as settlement notional for baskets, and clarifies usage of market vs event. This adds meaningful context 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 performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between two distinct modes (single-market and basket). It lists specific return fields like top_of_book, vwap_fill_price, slippage_pp, and verdict, making the purpose unambiguous.

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

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

The description explicitly directs use before acting on polymarket_arbitrage or polymarket_edges signals, and for trades above ~$500. It warns about thin books and partial basket fills, and clarifies required parameters (market or event). This provides strong when-to-use and when-not-to-use guidance.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description goes far beyond by detailing safety fields, compatibility conditions, and meaning of skipped counters, with 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 well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS) and each sentence adds value. However, it is relatively long and could be slightly more concise, though the detail is justified.

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

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

With no output schema, the description fully explains the response structure (leg prices, top spreads, compatibility fields) and covers edge cases like temporal misalignment and shape mismatch. It is comprehensive for a read-only analytical 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 semantic value: explaining the two modes, providing example values for topic, and clarifying overrides. This helps the agent understand parameter relationships and 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts and explicit tickers), making the purpose specific and distinct from sibling tools like polymarket_arbitrage.

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

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

The description explicitly explains when to use (to find spreads) and when not to trust (compatibility_warning cases, temporal misalignment). It warns that most pre-mapped topics return warnings, setting clear expectations.

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, destructiveHint, and idempotentHint. The description adds behavioral details beyond annotations: scoping to identifier and the ability to list all keys when key argument is omitted. 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 concise and well-structured. It is front-loaded with the primary action, and each sentence adds value without redundancy. No wasted words.

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

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

Given the tool's simplicity, no output schema, and high schema coverage, the description covers all necessary context: how to retrieve values, how to list keys, scoping, and relationship to sibling tools. It is complete for the agent's needs.

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 description for the 'key' parameter. The description adds meaning by explaining that omitting the key lists all saved keys, which is not stated in the schema. This provides additional guidance for 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 retrieves previously saved values or lists all keys. It uses specific verbs ('retrieve', 'list') and identifies the resource (saved keys/values). It distinguishes from sibling tools 'remember' and 'forget' by mentioning them explicitly.

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

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

The description explains when to use the tool: to look up context stored earlier, avoiding re-derivation. It mentions scoping and pairs with remember and forget. However, it does not explicitly state when not to use it or list alternative tools for similar tasks.

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

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

Annotations indicate read-only, idempotent, and non-destructive. The description adds that setting mark_read=true updates read status, which is a behavioral detail beyond annotations. No contradictions, though mark_read implies a state change which slightly contradicts readOnlyHint, but is mild.

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 a clear front-loaded purpose, then details. It is informative without unnecessary fluff, though slightly longer than strictly necessary.

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

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

Given no output schema, the description explains return fields and provides usage examples (filtering, mark_read). All 5 optional parameters are covered in schema, and the description adds enough context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, but description adds value by providing examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explaining mark_read behavior. Not all parameters are elaborated (limit, unread_only), but enough context 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 clearly states the tool pulls fired events from the subscription feed and specifies the returned data fields (source, citation_uri, raw event payload). It distinguishes from siblings by being the dedicated alert retrieval tool, with no overlap in purpose.

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

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

The description explains when to use filtering (type, since), the mark_read option for marking events read, and mentions polling is fine. It does not explicitly list when not to use it, but provides sufficient context for typical usage.

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

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

Annotations already declare idempotent, non-destructive, read-only, and open-world. Description adds substantial context: fans out to multiple sources, fallback mechanism, soft-fail for USPTO, and return structure with changes[] and citation URIs. No contradictions with annotations.

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

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

Description is somewhat long but front-loaded with relevant example queries. Every sentence serves a purpose; no filler. A minor improvement could be trimming redundant explanations, but overall it earns its length.

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

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

Given the tool's complexity (multiple data sources, fallback, parameter formats) and absence of an output schema, the description covers key behavioral aspects and return structure. It could mention pagination or error handling but is sufficient for correct invocation.

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

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

Schema covers all three parameters (100% coverage), so baseline is 3. Description adds value by giving concrete examples for 'since' (ISO date, relative shorthand, recommended default '30d'), clarifying 'value' can be ticker or CIK, and explaining 'type' is currently limited to 'company'.

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

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

Description opens with concrete example queries ('What's new with X', 'latest on Y') and explicitly states it is a change feed for a company. It also clearly distinguishes from sibling entity_profile, ensuring the agent knows when to use which 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?

Provides explicit details on when to use (e.g., 'updates on Acme') and when not to ('Use entity_profile instead when you want the static profile'). Also explains fallback logic between GDELT, GNews, and USPTO, giving the agent clear decision boundaries.

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 idempotent and non-destructive hints. Description adds scoping by identifier, persistence differences for authenticated vs. anonymous sessions, and 24-hour retention for anonymous sessions, beyond what annotations 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 sentences, front-loaded with purpose, every clause adds value. No wasted words.

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

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

Given simplicity (2 required params, no output schema), description covers purpose, usage, behavior, and integration with sibling tools. Fully adequate.

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 naming conventions (e.g., 'subject_property') and clarifies that value can be any text, enhancing the schema's enumerated examples.

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 verb+resource ('Save data...reuse later') and illustrates with concrete examples (resolved ticker, target address). It clearly distinguishes from sibling tools like recall and forget.

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

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

Explicitly states when to use ('when you discover something worth carrying forward') and pairs with recall/forget. No explicit when-not-to-use, but context is sufficient.

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

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

The description discloses behavioral traits beyond the annotations: it mentions internal cascading through lookup endpoints, graceful degradation of LEI/FIGI enrichment if sources are unavailable, and explicit reporting of unresolved identifiers. These add context not captured by the read-only and idempotent hints.

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

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

The description is comprehensive but relatively long, starting with example queries and then detailing behavior. Every sentence contributes value, though it could be trimmed slightly for clarity.

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

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

With no output schema, the description fully explains return behavior: for company, it lists all possible identifiers including unresolved; for drug, it specifies RxCUI, ingredient, brand, and citation. It also covers input formats and fallback behavior, making it complete.

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

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

The schema describes type and value, but the description enriches both: for company, it details the cross-source identity spine and accepted input formats; for drug, it lists expected outputs. This goes well beyond the schema's brief descriptions.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers, with specific verbs 'resolve' and resource 'entity'. It distinguishes itself by noting 'Use FIRST whenever you have a name but need an ID' and providing examples of use cases.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID', providing a clear when-to-use instruction. However, it doesn't explicitly state when not to use it or name alternative tools for other scenarios, so it lacks full exclusions.

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

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

Annotations already indicate read-only, idempotent, and non-destructive nature. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks results, and returns score, confidence, and signal density.

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, front-loaded with purpose and use case, with no redundant words. Every sentence adds value.

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

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

Given no output schema, the description adequately lists return fields (score, confidence, signal density). Parameter count and schema coverage are high. Could mention entity limit or error handling, but overall complete.

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

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

Schema coverage is 100% (baseline 3). The description adds context: first entity is the subject, rest competitors; models parameter distinguishes free vs. paid; context parameter disambiguates common names.

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

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

The description clearly states the verb 'Compare' and the resource 'AI visibility across multiple entities'. It distinguishes from sibling tool ai_visibility_check by specifying side-by-side comparison and ranking.

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

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

The description explicitly states the use case for competitive AI-marketing audits with an example question. However, it does not provide explicit exclusions or situations to avoid using this tool.

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

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

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. The description adds important behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. No contradiction with annotations.

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

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

The description is one well-structured paragraph that front-loads the purpose and key details. It efficiently covers usage, limitations, and outputs. Slightly longer than necessary 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?

Despite no output schema, the description explicitly lists return fields (summary block with is_latest, license, published_at, etc.). It also explains error handling (partial failures, timing issues) and ecosystem scope. Annotations cover safety. The description is complete and self-contained.

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

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

The input schema covers 100% of parameters with descriptions (package and version). The description does not add additional semantic meaning beyond the schema for these parameters. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description states a specific verb ('Scan Dependency') and resource ('npm package') and clearly describes the composite check across two services (deps.dev, bundlephobia) with specific outputs. It distinguishes this tool from siblings by its unique scope (NPM ecosystem, combined license/advisory/size/popularity check).

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

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

Explicitly says when to use: when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also states when not to use: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This provides clear context and alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about return format and scope (ODPHP/health.gov topics), aligning with annotations. No contradictions.

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

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

Two sentences, front-loaded with purpose and examples, followed by guidance on next steps. Every sentence adds value with no redundancy.

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

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

Given a single required parameter and no output schema, the description adequately specifies input behavior, return fields, and how to proceed for detailed guidance. No gaps.

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

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

Schema coverage is 100% with a brief description. The tool description adds context by explaining full-text search and giving examples, slightly enriching the parameter meaning 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 full-text search over ODPHP/health.gov health topics by keyword, provides examples, and specifies the return fields (id, title, categories, source URL). It also distinguishes from the sibling tool get_health_topic.

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 context for when to use (keyword search) and suggests using get_health_topic for full guidance. Implicitly contrasts with list_health_topics which would list all topics. Could be more explicit about alternatives, but adequate.

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

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

Discloses embedding model (BGE-base-en), window size (500-char overlapping), cosine similarity, character cap (200K chars with truncation flagging), and output details (character offsets for verification). Annotations confirm idempotent, read-only, open-world, and non-destructive; description adds valuable implementation details without contradiction.

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

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

Two sentences plus a technical note, all front-loaded with key information. No redundant or vague language. Every sentence serves a distinct purpose: overview, usage guidance, and behavioral details.

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

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

Despite no output schema, the description specifies return type (top-N passages with offsets and scores), caps (200K chars), and integration pattern (pair with ask_pipeworx_grounded). For a search tool, this is fully sufficient.

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

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

Schema description coverage is 100%, but the description adds context beyond the schema: explains 'text' as already-fetched content, gives query examples ('supply-chain risk', 'fiscal year 2024 revenue'), and clarifies the purpose of 'limit'. Slight additional value warrants a 4.

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 performs semantic search inside a fetched record, specifying verb ('search') and resource ('inside a fetched record'). Distinguishes from siblings by noting it works with already-fetched text and pairs 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 advises when to use ('when the record is too big to cram into the prompt') and provides an alternative approach (piping with ask_pipeworx_grounded). Also explains the benefit: saves context and returns only relevant passages.

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

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

Adds significant behavioral context beyond annotations: requires OAuth account, SMS cap, webhook signing and auto-disable. No contradiction with annotations. Annotations are sparse, so description carries burden well.

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

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

Front-loaded with purpose and requirements. Dense but well-structured, covering types, params, and delivery. Could be slightly more concise, but no fluff.

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

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

Given complexity (3 params, nested objects, no output schema), description covers prerequisites, each type with examples, delivery details, and limitations. Complements sibling tools list_subscriptions and unsubscribe.

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

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

Schema coverage is 100%, but description adds extensive examples and explanations for each parameter, especially for type-specific params and delivery channels. Significantly more informative than 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 action (create), resource (subscription), and return (subscription id). Distinguishes from siblings like list_subscriptions and unsubscribe. Lists supported types.

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 context on when to use (requires Pipeworx OAuth account) and when not (anonymous/BYO cannot persist). Mentions delivery channels and limitations (SMS cap, webhook auto-disable). Could explicitly contrast with recent_alerts, but implies usage alternatives.

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

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

Annotations already declare safe read-only, idempotent, non-destructive behavior. Description adds that results are drawn from a live catalog and include argument shapes. No contradictions, but behavioral context beyond annotations is moderately useful.

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

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

Well-structured with front-loaded purpose, alternative phrasings, and concise usage instructions. 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?

Given no output schema, the description adequately explains the return format (category-bucketed examples with tool+argument shapes). Covers parameter usage and when to use, making it complete for an onboarding tool.

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

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

Schema coverage is 100% with a clear description. Description adds real-world context: 'pass topic to focus' and 'omit for cross-category spread', slightly enhancing the semantic meaning beyond the schema.

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

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

The description clearly states the tool returns category-bucketed example questions for onboarding, distinguishing it from sibling tools like ask_pipeworx and compare_entities. It specifies the exact verbs: 'call', 'returns', 'use FIRST'.

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

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

Explicitly says when to use (first when you don't know what Pipeworx can do), how to call (no args or with topic), and what to expect (example questions with tool shapes). Provides clear context and exclusions.

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

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

Annotations indicate idempotent and non-destructive. Description adds ownership enforcement and deactivation behavior, linking to historical availability via recent_alerts.

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, no fluff, key information front-loaded. Every sentence is meaningful.

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?

Simple tool with one parameter, no output schema, annotations provided. Description covers purpose, usage constraints, and side effects 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 description for 'id' is complete ('Subscription id (uuid) returned by subscribe'), so description adds no extra meaning. Baseline 3 per rules.

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 verb 'cancel' and resource 'subscription by id'. Clearly distinguishes from siblings like 'subscribe' and 'list_subscriptions'.

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

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

Explicitly states ownership enforcement (only cancel own subscriptions) and behavioral effect (deactivation, not deletion). Lacks explicit when-not-to-use alternatives but provides sufficient 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 readOnly, openWorld, idempotent, and non-destructive behavior. The description adds critical behavioral semantics beyond that: it explains that 'could_not_verify' means the check did not happen and must not be treated as evidence, and 'unsupported' means no source was found. This error-context disclosure is valuable for an agent interpreting results correctly and goes well beyond the annotations.

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

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

The description is detailed but every sentence carries useful information: examples, usage, path routing, return values, failure semantics, and efficiency benefit. It is well-structured and front-loaded with the core purpose. No fluff or redundancy; the length is justified by 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?

Given that there is no output schema, the description fully compensates by listing all verdict types (confirmed, refuted, etc.), the presence of citations and reasoning, and explaining nuanced failure modes (could_not_verify vs unsupported). It covers what an agent needs to know to use the tool correctly and interpret results, 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?

Schema description coverage is 100%, with both 'claim' and 'tolerance_pct' adequately described in the input schema. The tool description itself does not add extra parameter-level details or examples, so it meets the baseline but does not exceed it. The mention of 'exact percent-delta math' for financial claims is behavioral context rather than parameter semantics.

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 verifies natural-language factual claims against authoritative sources, with specific query examples like 'Is it true that...' and 'fact check'. It distinguishes itself from siblings by focusing exclusively on claim verification and even notes it replaces 4-6 sequential calls, making its purpose unambiguous and separate from broader research tools like deep_research.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides internal routing guidance (SEC EDGAR fast path for company-financial claims, grounded pipeline for all others), giving clear contextual direction. However, it does not name sibling tools as alternatives or explicitly state when NOT to use this tool, so it misses the top score.

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