Maptiler

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context: it returns per-model details (score, confidence, signals, raw_response) and a combined view, and notes cost implications (Anthropic billed separately). 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?

Four sentences, front-loaded with action, no redundant information. Every sentence adds value: purpose, default, augmentation, output, and use cases.

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

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

Despite no output schema, the description fully explains the return structure. It covers all parameters, use cases, and cost implications. Annotations cover safety, so 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 baseline is 3. The description summarizes parameters but does not add significant meaning beyond the schema. For example, it says 'Default model is Workers AI...' which is already in the 'models' parameter description.

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

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

The description clearly states the verb (probe), resource (LLMs knowledge about entities), and output (score 0-100 per model). It distinguishes from sibling tools like 'scan_competitor_ai_presence' by being a general visibility check, not competitor-specific.

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 the default model (free) and optional Anthropic probe (BYO key), and lists use cases (AI-marketing audits, pre-launch checks, competitive monitoring). It does not explicitly say when not to use or name alternatives, but the context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds that the tool routes to 4,774 tools, returns structured answers with pipeworx:// citation URIs, and works on every tier. It doesn't mention error handling or scope limits, but the overall behavioral context is well-addressed.

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

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

The description is verbose but front-loaded with the key recommendation. Every sentence serves a purpose: explaining scope, comparing to alternatives, giving examples. Could be slightly trimmed, but the structure is logical and agent-friendly.

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

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

Given the tool's complexity (6 params, no output schema), the description fully explains its role, when to use it versus siblings, what it returns (structured answer with citations), and provides ample examples. The agent has enough context to decide 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%—every parameter (including aliases) has a description. The description provides many examples of questions but doesn't add new parameter-level meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description explicitly states it routes questions to authoritative structured data tools, provides a concrete list of domains (SEC filings, FDA data, etc.), and gives many example queries. It clearly distinguishes from sibling tools like ask_pipeworx_grounded and 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 provides explicit guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and exactly when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant, deep_research for broad questions). It even tells the agent to use this for specific query phrases like 'what is', 'look up', etc.

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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) already indicate safe read-only behavior. The description adds critical behavioral insights: extracts answer only from tool results, returns explicit refusal reasons (e.g., not_in_source, no_tool_match) when data doesn't directly answer. This fully discloses the tool's 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 a single paragraph but packs essential information upfront: purpose, routing, return format, usage guidance, and cost. It avoids unnecessary words, though the return format details could be slightly more structured. Still, it is efficient and well-organized.

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

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

Despite lacking an output schema, the description fully explains the return structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and key refusal reasons. It covers complexity with 6 parameter aliases, provides usage context, and contrasts with the sibling tool. The description is sufficient for correct invocation and decision-making.

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

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

Schema description coverage is 100%, with all parameters documented as aliases for the question. The description does not add new parameter semantics beyond what the schema provides; it merely reiterates the concept of sending a natural language question. Per guidelines, baseline is 3 when coverage is high, and no added value is present.

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 this as a 'hallucination-resistant answer mode' for high-stakes reads, distinguishing it from ask_pipeworx by noting it extracts answers using only tool results. It specifies the return format with evidence and explicit refusals, making the purpose highly specific and differentiated from siblings.

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: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' Also advises when not to use: 'prefer ask_pipeworx for casual lookups' and notes the extra cost. This provides clear contextual 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?

Adds extensive behavioral context beyond annotations, including fan-out logic, response shapes, error states, and risk factors, 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?

While structured, the description is excessively long (~500 words) and could be more concise without losing essential information.

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

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

Covers all aspects: input variants, fan-out logic, response structure, trust indicators, error states, and risk factors, making it complete without an output schema.

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

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

Despite 100% schema coverage, the description enriches parameter understanding with context on usage, examples, and effects like depth and include_raw.

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 relevant data, using specific verbs and resources. It distinguishes itself from sibling tools by focusing on Polymarket bets and data aggregation.

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

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

Provides explicit use cases like 'should I bet on X' and warns about inspecting confidence, but lacks direct comparison to alternatives.

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

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), behavioral traits (off-calendar fiscal year handling, sorting by primary metric), and output format (paired data + citation URIs). Surpasses annotations which only indicate read-only, idempotent, and open-world 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?

Packs many details into a single paragraph. Uses example phrases and bullet-like structure. Slightly verbose but every sentence adds value. Could be tightened slightly without losing information.

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

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

Covers purpose, parameters, behavior, and output despite lacking an output schema. Explains return format (paired data + URIs) and data provenance. Complete for a tool with two simple parameters.

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

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

Adds significant meaning beyond schema: explains 'type' enum maps to data sources, 'values' format with examples (tickers, drug names), and constraints (2-5 items). Schema coverage is 100% but description enriches with real-world context.

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

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

The description uses strong action verbs like 'compare' and 'side-by-side comparison' with specific resource types (companies or drugs). It clearly distinguishes from sibling tools like entity_profile by emphasizing comparative functionality and parallel calls.

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

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

Provides explicit use cases (head-to-head, ranking, comparisons) and advises to prefer over sequential lookups. Lacks explicit when-not-to-use but context implies it's for multi-entity scenarios only.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds context that input is WGS84 and output is converted coordinates, no contradiction. Adds value beyond annotations.

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

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

Single sentence with clear action and examples. Slightly repetitive with synonyms, but not overly long.

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 and no param descriptions. Description does not clarify output format, error handling, or coordinate array structure. Incomplete for an accurate 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 has zero parameter descriptions. Description mentions target CRS with examples but does not specify coordinate format (e.g., order of lat/lng) or what the array structure represents. Insufficient for agent to correctly use without additional knowledge.

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

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

Description clearly states the tool converts WGS84 lat/lng to other CRS, with multiple synonyms and examples. It differentiates from siblings like geocode and elevation tools.

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

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

Explicitly tells when to use: converting standard lat/lng to projected systems. Provides example target CRS. Could mention when not to use, but not necessary given clear purpose.

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

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

Discloses account requirements, parallel decomposition, output format (findings packet with evidence, confidence, source, gaps), and limitations (returns empty gaps for uncataloged topics). Annotations already indicate readOnly, idempotent, and non-destructive; description adds rich behavioral context.

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

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

Front-loaded with critical caveats (account required, alternatives). Well-structured with clear sections: prerequisites, core function, mechanics, output, usage guidance. No wasted sentences despite 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?

For a complex tool with no output schema, the description fully explains the output format, behavior, and limitations. It covers all aspects needed for an agent to correctly invoke and interpret results.

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

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

Schema covers both parameters with descriptions (100% coverage). Description adds value by explaining depth values in natural language (quick=3, standard=5, thorough=8) and clarifying that questions are decomposed into facets.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research across Pipeworx's 1242 STRUCTURED data sources in ONE call.' It explicitly distinguishes itself from open-web search and sibling tools like ask_pipeworx.

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

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

Provides explicit guidance on when to use (broad/multi-part questions over structured data) and when not to use (breaking news, single lookup). Names alternative tools: ask_pipeworx for single lookups and for current news.

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. Description adds value by detailing the output format (names, descriptions, schemas with curated examples, ready to call), making agent expectations clear without 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?

Every sentence earns its place: purpose, domain list, output format, strategic advice. No fluff, logically ordered, and efficiently packed with information.

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

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

Without output schema, description adequately covers return structure and examples. Mentions domain coverage and aliases. Minor gaps (error handling, pagination) but acceptable for a straightforward discovery 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%, baseline 3. Description adds meaning by explaining aliases, providing example queries, and clarifying the 'limit' parameter's default/max, going 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 the verb (find/discover) and resource (tools) and distinguishes from siblings by emphasizing it returns tool definitions ready to call, unlike domain-specific tools that perform tasks.

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

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

Explicitly states when to use ('when you need to browse, search, look up, or discover what tools exist') and advises calling it first. Lacks explicit when-not-to-use, but the positive guidance is strong and differentiated.

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 read-only and non-destructive behavior. The description adds value by specifying the data source (MapTiler SRTM/Maxar) and unit (meters), which are beyond annotation hints and help the agent understand the tool's origin and output.

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

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

The description is a single sentence with example natural language queries, directly conveying the tool's purpose without unnecessary words. It is front-loaded with the core action and context.

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 two-parameter tool with comprehensive annotations, the description adequately covers what the tool does and its data source. However, it omits the coordinate system (e.g., WGS84) and does not describe return values, though no output schema is required.

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

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

The input schema has 0% description coverage, and the description does not explain the lat/lon parameters beyond being coordinates for a single point. It lacks details like expected format (e.g., decimal degrees) or bounds, forcing reliance on parameter names 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 provides elevation in meters above sea level at a single point, using alternative phrasings like 'altitude at [point]'. It distinguishes from the sibling tool 'elevation_polyline' by specifying singular point rather than polyline.

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 example queries indicating usage for single-point elevation requests but does not explicitly state when not to use this tool or mention alternatives like 'elevation_polyline' for multiple points. Usage context is implied but not thoroughly guided.

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, non-destructive hints. The description adds limited additional behavior info (e.g., 'samples'), but no details on resolution, limits, or return format.

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

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

The description is very concise, front-loaded with practical examples, and uses minimal words to convey the tool's purpose and usage.

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

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

For a simple tool with one parameter, good annotations, and no output schema, the description sufficiently covers the purpose and usage. Minor gaps in parameter details are acceptable given the tool's simplicity.

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

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

Schema coverage is 0%, so the description must compensate. It explains that 'coordinates' is a sequence of lat/lng pairs, adding meaning beyond the schema's array of numbers. However, the order (lat,lng vs lng,lat) is ambiguous.

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 returns elevation samples along a polyline, with specific examples like 'elevation gain' and 'topographic profile'. It distinguishes from a point elevation tool by emphasizing the polyline nature.

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

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

The description provides clear usage scenarios: hiking, cycling, trail planning. It does not explicitly mention alternatives or when not to use, but the sibling list includes a point 'elevation' tool, which is implied.

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

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

Annotations correctly indicate read-only, open-world, idempotent, non-destructive. The description adds behavioral details: fans across multiple sources (SEC, XBRL, USPTO, GDELT, GLEIF), soft-fails for USPTO sunset, and returns specific data. No contradiction.

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

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

The description is packed with useful information in a coherent structure: example queries first, then explanation of functionality, then return fields, then parameter details. Slightly long but every sentence adds value; could be tightened slightly.

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

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

Given the complexity (multi-source, no output schema), the description covers return fields, sources, limitations (USPTO sunset, name unsupported), and preferred usage. It lacks error handling or pagination details, but these are less critical for a one-shot profile tool.

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

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

Schema coverage is 100% for both parameters. The description adds meaning: explains that type is limited to 'company' currently, and value expects ticker or zero-padded CIK, but not names. This clarifies usage 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 uses specific verbs ('profile', 'research', 'brief') and clearly states the tool returns a 'full cross-source profile of a US public company in ONE parallel call'. It lists exactly what is returned (CIK, filings, fundamentals, patents, news, LEI) and distinguishes from alternatives like chaining single-pack lookups or resolve_entity for names.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view'. Also warns 'names not supported (use resolve_entity first if you only have a name)', giving clear when-to-use and when-not-to-use guidance.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description only adds 'Delete' which aligns but doesn't extend beyond annotations.

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

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

Two sentences, no waste. Front-loaded with purpose, then context. Every sentence adds value.

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

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

For a simple tool with one parameter and no output schema, the description plus annotations provide complete guidance.

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 descriptive parameter info. Description doesn't add beyond schema's 'Memory key to delete.' Baseline score applies.

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

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

Description clearly states 'Delete a previously stored memory by key.' Verb and resource are specific. Distinguishes from siblings by mentioning 'remember' and 'recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also directs to sibling tools for alternative operations.

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 a non-destructive, read-only, idempotent operation. The description adds detail: fetches the page, extracts data, and emits markdown. It does not introduce contradictions and provides behavioral context beyond annotations.

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

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

The description is front-loaded with the main action and is concise, using only a few sentences without any extraneous 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?

Given the low complexity (2 parameters, no output schema, no nested objects), the description fully covers the tool's behavior and output. It clearly explains the result (a text blob for llms.txt) and provides sufficient context for 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 description coverage is 100%, so the schema already documents both parameters. The description adds an example for 'url' and restates the default/max for 'max_links', but does not provide significant new 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's specific verb and resource: 'Generate a production-ready llms.txt file for any URL'. It explains the process (fetches, extracts, emits) and the output format, distinguishing it from siblings by its unique function.

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

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

The description explicitly lists use cases (getting a client's site indexed, drafting for own project, auditing a competitor), providing clear context for when to use the tool. However, it does not mention when not to use it or any alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering safety. The description adds value by specifying the data source (OSM-curated), that results are ranked, and the relationship to geocode_by_id. It does not contradict annotations, but could mention limitations like OSM coverage variability or rate limits, though not required given strong annotations.

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

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

The description is a single paragraph that is front-loaded with alternative invocation phrasings. It is concise (4 sentences) and avoids redundancy, but could be slightly more structured (e.g., listing parameter types). Still, it is efficient and uses natural language well.

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 9 parameters and no output schema, the description covers the core functionality (forward geocoding, returning ranked results with coordinates and address components) but fails to explain the optional parameters or output format details. Annotations (readOnly, etc.) reduce the burden, but the parameter gap makes the tool less usable without external knowledge. Adequate but not 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 0%, meaning the description does not explain most of the 9 parameters. Only the required 'query' parameter is implied through example invocations. Optional parameters like bbox, limit, types, country, language, proximity, fuzzyMatch, and autocomplete are not described, leaving a significant gap for the agent to understand how to use them. The description compensates minimally.

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 forward geocoding using MapTiler (OSM data), returning ranked candidates with coordinates and address components. It also provides alternative phrasings like 'Geocode [address]' and distinguishes itself as an alternative to Mapbox/Google geocoders, implying differentiation from sibling tools like geocode_reverse (reverse geocoding) and geocode_by_id (re-lookup).

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

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

The description provides explicit guidance on when to use this tool (when OSM data is desired) and mentions alternatives (Mapbox/Google geocoders). It also hints at a workflow by noting that feature IDs can be used with geocode_by_id. However, it does not explicitly state when not to use it or provide prerequisites, but the context is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that it re-fetches data, which is consistent and marginally helpful but doesn't disclose additional behavioral traits beyond what annotations provide.

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

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

Description is just two sentences, front-loaded with the core purpose and usage. 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 and the presence of annotations, the description covers the main purpose but misses explaining the language parameter. For a 0% schema coverage, more completeness is expected.

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

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

Schema description coverage is 0%, so the description should explain both parameters. It mentions the 'id' parameter implicitly but completely omits any explanation of the 'language' parameter, leaving its purpose unclear.

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

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

Description clearly states the tool re-fetches a MapTiler geocoding feature by its stable ID, linking it to geocode/geocode_reverse results. This is a specific verb+resource that distinguishes it from siblings like geocode and geocode_reverse.

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 explains when to use (to refresh or re-localize a previously discovered place) and where the ID comes from (geocode/geocode_reverse). It doesn't explicitly state when not to use, but the context is clear enough.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc., fully covering the safety profile. The description adds minimal behavioral context beyond stating the data source (MapTiler). It does not mention rate limits, error handling, or response specifics. With annotations providing the essential safety traits, the description's contribution is limited.

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 (one sentence with example phrasings). However, it repeats the same idea in three different ways, which is slightly redundant. Still, it is front-loaded and efficient.

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

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

Given the tool has 5 parameters and no output schema, the description is too minimal. It does not explain how parameters like 'limit', 'types', or 'language' affect the output, nor does it describe the return format. Annotations help with safety but not with parameter usage or output expectations.

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

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

The input schema has 5 parameters with 0% description coverage. The description does not explain any parameter semantics (e.g., what 'limit', 'types', 'language' do). Since the schema lacks descriptions, the description should compensate but fails to do so, leaving the agent without guidance on parameter usage.

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

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

The description clearly states the tool's purpose: converting coordinates to address/locality/region via reverse geocoding using MapTiler. It includes example queries that help the agent understand the use case. This distinguishes it from sibling tools like 'geocode' (forward) and 'coordinates_convert'.

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 example queries ('What address is at [lat,lng]') that indicate when to use the tool. However, it does not explicitly state when not to use it or mention alternatives among siblings. The context of sibling tools suggests differentiation from 'geocode', but no explicit guidance is given.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds value by listing return fields (id, type, params, created_at, last_fired_at, fire_count) and default filter on active subscriptions. Does not contradict annotations.

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

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

Two sentences: first states purpose and return fields, second gives usage guidance. Front-loaded, concise, no waste.

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

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

Simple tool with one optional param and rich annotations. Description covers purpose, return data, and usage context. No output schema needed; return fields are described.

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

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

Schema coverage is 100%; the description does not add meaning beyond the schema's description of include_inactive. Baseline score of 3 applies.

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

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

Clearly states 'List the caller's active subscriptions' with specific verb, resource, and scope. Distinguishes 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 says when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Provides clear context for 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 provide default safety traits; the description adds critical behavioral context: rate-limited to 5 per day, free, doesn't count against quota, team reads digests daily. This goes 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?

Four well-structured sentences, front-loaded with purpose, no fluff. Every sentence earns its place.

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

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

Covers all relevant aspects: usage, rate limits, quota impact, feedback processing. For a feedback tool with no output schema, this is complete and sufficient.

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

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

Schema coverage is 100%, and the description adds meaning: explains each enum type, clarifies that message should be specific, and describes optional context fields. Adds value over raw schema.

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

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

The description clearly states the tool's purpose: sending feedback about bugs, missing features, data gaps, or praise. It distinguishes from sibling tools, which are query/analysis tools, making the feedback channel 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?

Provides explicit guidance on when to use (e.g., wrong data, missing tool, praise) and what to avoid (don't paste end-user prompts). Does not explicitly state when not to use, but context is clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII). No contradiction with annotations.

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

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

Two paragraphs: first states purpose, second lists use cases and metadata. No wasted words, front-loaded with key information.

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

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

For a simple read-only tool with one parameter and no output schema, the description covers purpose, use cases, data source, caching, and privacy. Sufficient for agent selection.

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

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

Schema coverage is 100% with a single parameter 'window'. Description adds meaning by explaining trade-offs: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.'

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 returns top tools, top packs, and total call volume over a recent window. It distinguishes itself from siblings like 'ask_pipeworx' by focusing on trends rather than answering questions.

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

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

Three explicit use cases are listed (discovering hot data sources, confirming popular tool, seeing alignment). No mention of when not to use, but the context is clear enough.

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

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds significant behavioral context: it explains the fill check (pricing against live CLOB depth, avoiding trades when realizable_edge_pp <= 0), the semantic anchor for cross-event pairs (Jaccard similarity >= 0.30), and the partition filter (dropping placeholder slugs >20%). No contradiction with annotations.

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

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

The description is well-structured with clear sections for each mode, filters, and fill check. It is front-loaded with the main purpose. While it is lengthy, every sentence contributes value, and the complexity of the tool justifies the length. A minor reduction in wordiness could improve conciseness.

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

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

Given the tool's complexity (multiple modes, partition checks, fill checks, filters), the description is comprehensive. It explains the output structure (opportunities[] with gap_pp, suggested_trade, etc.), the partition check logic, the fill check for book depth, and references a sibling tool for custom sizing. No output schema exists, but the description adequately describes return values.

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%, providing good initial parameter descriptions. The tool description adds substantial additional meaning: it elaborates on the 'event' parameter with slug/URL examples and mode behavior, and explains that 'topic' triggers cross-event scanning with seed questions. This goes well beyond the schema's basic descriptions.

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

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

The description clearly states that the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It specifies three calling modes (no args, event, topic) with distinct purposes, and the verb 'find' combined with specific resources (arbitrage opportunities, Polymarket) makes it highly distinguishable from sibling tools like polymarket_fill_risk or polymarket_edges.

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

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

The description provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. It also gives concrete examples for the event and topic parameters, and directs agents to polymarket_fill_risk for custom sizing, effectively covering when not to use this tool.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds extensive behavioral details: caching (1h KV), model families with math, slippage assumptions, and diagnostic output. 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 thorough but overly long and dense with technical details. While well-structured with sections, it could be more concise. It is front-loaded with purpose but delves into deep model descriptions.

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 9 parameters, no output schema, and high complexity, the description is exceptionally complete. It details response structure (by_segment, fed_candidates, diagnostics), caching behavior, and parameter effects. It fully equips 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?

All 9 parameters are documented in the schema (100% coverage). The description adds significant value by explaining parameter usage, trade-offs (e.g., slippage_pp adjustments, min_partition_leg_kelly clarification), and providing 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 clearly states the tool scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price, using specific verbs like 'scan' and 'return'. It distinguishes from siblings like polymarket_arbitrage by focusing on mispricings from Pipeworx models.

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

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

The description is tailored for 'what should I bet on today' and explains segments and knobs. It hints at usage contexts but lacks explicit when-not-to-use or alternatives. However, it provides rich guidance on parameter adjustment.

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 read-only, idempotent, non-destructive. The description adds valuable behavioral context: uses daily snapshots, returns time-series, handles expired opportunities, and explains snapshot gaps due to cache-misses. 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, front-loading the purpose, then args, then response sections (tracked, expired, snapshot_dates), then limits. 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?

Despite no output schema, the description thoroughly explains the response structure (tracked[], expired[], snapshot_dates[]) and covers limits like TTL and snapshot enablement. This makes the tool fully interpretable.

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

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

Schema covers both parameters with descriptions. The tool description adds defaults (days:14, window:'1wk'), clamp info (days 2-30), and explains how each parameter affects the response. This goes beyond schema.

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

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

The description clearly uses a specific verb (track/provide) and resource (edge telemetry), stating it answers 'how long has this edge existed and is it shrinking?' It distinguishes from sibling polymarket_edges by focusing on persistence and decay rather than raw edges.

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

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

The description provides context on when to use (e.g., differentiating a fresh wide edge from an old one) and includes limits like history depth and TTL. It does not explicitly name alternative tools but implies this is for decay analysis, which 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?

Discloses behavioral traits beyond annotations: explains it checks live order book depth, returns verdict (clean|degraded|cannot_fill), and warns about partial basket fills converting arb into unhedged directional position. No contradiction with annotations (readOnlyHint=true).

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 well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS). Front-loaded with purpose. Slightly verbose but justified by the complexity of two modes and multiple return fields.

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

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

Despite no output schema, the description thoroughly enumerates return fields for both modes (e.g., top_of_book, vwap_fill_price, profit_usd, thin_legs, forced_directional_risk). It also references sibling tools and explains parameter interplay, making it complete for an agent.

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

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

With 100% schema description coverage, baseline is 3. The description adds context: explains mode mutual exclusion (event vs market), side default logic for baskets, and clamp range for size_usd, thus providing moderate added meaning.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It details two modes (single-market and basket) and lists their specific outputs, distinguishing itself from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly tells when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why (thin books, partial basket fills causing directional risk), providing clear alternatives.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: explains modes, response structure, safety fields (compatibility_warning, temporal_alignment, skipped_cross_type/subtype counters), and conditions when spreads are invalid. 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 comprehensive but somewhat lengthy. It is well-structured with sections (TWO MODES, RESPONSE, SAFETY FIELDS), though could be tightened without losing information. Every sentence adds value, but the density may impede quick parsing.

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

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

No output schema, so the description must explain return values, which it does: leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, and counters. It covers safety conditions and alignment checks, making the tool's behavior predictable. Given the complexity of 3 parameters with 100% schema coverage, the description is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. The description elaborates on the topic parameter by listing all 10 macro shortcuts and explains that explicit tickers override topic-mapped ones, adding 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 computes cross-venue spread between Kalshi and Polymarket, specifying it compares the same resolving question. It differentiates from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison rather than within-venue 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 explains two modes (topic shortcuts and explicit tickers) and provides explicit guidance on when spreads are meaningful via safety fields like compatibility_warning and temporal_alignment. It does not explicitly state when not to use the tool, but the safety fields serve that purpose.

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

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

Annotations already indicate readOnly and idempotent. Description adds scoping to identifier and pairing with remember/forget, which is useful but not critical beyond annotations.

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

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

Three sentences pack purpose, use cases, scoping, and complementary tools. No redundancy, front-loaded with core action.

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

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

Single optional param, no output schema, annotations cover safety. Description fully explains operation, scoping, and complement with remember/forget. 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 covers parameter fully (100% coverage). Description adds behavior of omitting key to list all, plus usage examples, which adds value beyond schema.

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

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

Clearly states it retrieves saved values via remember or lists all keys, with concrete examples like ticker, address, notes. Differentiates from siblings by mentioning 'remember' and 'forget'.

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

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

Explicitly says when to use: to look up context stored earlier. Describes listing behavior when key omitted. Could add when not to use, but sufficient for typical scenarios.

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 explains that mark_read:true flags returned events as read, modifying the system's state. However, annotations include readOnlyHint: true, which implies no state changes. This contradiction (description claims mutation, annotation claims read-only) severely undermines 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 concise, with four sentences covering purpose, return fields, filtering/mark_read functionality, and an alternative access method. Each sentence adds value, and the structure is front-loaded with the main purpose.

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

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

The description covers the return fields, filtering, and marking read behavior. Without an output schema, the mention of return fields is helpful. It does not discuss pagination or error cases, but for a read-idempotent tool with good annotations, it is largely 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 good parameter descriptions. The description adds extra context on how to use type and since together, and explains the effect of mark_read, providing semantics 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 the tool pulls fired events from the subscription feed, with specific verb 'pull' and resource 'fired events'. It distinguishes from sibling tools like list_subscriptions, which list subscriptions themselves.

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 mentions that polling works fine and provides an alternative API endpoint, but does not give explicit guidance on when to use this tool versus other sibling tools like list_subscriptions or subscribe. Usage context is implied but not fully elaborated.

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

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

Beyond annotations (readOnly, idempotent, etc.), description details fan-out behavior, source-specific logic (GDELT vs GNews, USPTO soft-fail), return structure (changes[] grouped by source, total_changes count, citation URIs), and relative date shorthand.

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

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

Dense but well-organized: starts with usage examples, then technical details, then alternatives. Every sentence adds value, though slightly heavy on 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?

Given no output schema, description explains return structure fully. Covers edge cases (USPTO soft-fail, GDELT→GNews fallback) and provides comparison with entity_profile.

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

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

Schema coverage is 100%. Description adds practical examples for since (e.g., '7d', '30d', '3m', '1y', ISO date), suggests typical value ('30d'), explains value accepts ticker or CIK, and clarifies type only supports 'company'.

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

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

The description clearly defines the tool as returning a change feed for a company over a time window, sourcing from SEC EDGAR, GDELT/GNews, and USPTO. It explicitly distinguishes from sibling tool entity_profile for static profiles.

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

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

Provides explicit example queries like 'What's new with X' and recommends using entity_profile for static profiles. Explains GDELT preferred with GNews fallback for rate limits or errors.

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 true and non-destructive. Description adds context: key-value pairs scoped by identifier, 24-hour retention for anonymous, 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?

Four sentences, front-loaded with main purpose, then examples, scope, and pairing. 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?

Provides comprehensive context: purpose, usage, behavior (scoping, persistence), and related tools. Fully meets needs for a simple 2-param tool.

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

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

Schema covers both parameters (100%). Description adds examples and usage context, exceeding 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?

Clearly states the tool saves data for reuse across conversations/sessions, with specific examples (ticker, address, preference). Distinguishes from siblings recall and forget.

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

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

Explicitly says when to use (when discovering something worth carrying forward) and pairs with recall/forget. Also notes persistence differences (authenticated vs anonymous).

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 valuable behavioral context: each call cascades through several endpoints and replaces 2-3 manual lookups. No contradiction with annotations.

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

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

The description is well-structured with bullet points and front-loaded purpose. It is slightly verbose but every sentence adds value. Could be tightened without losing 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?

Despite no output schema, the description fully explains the return values for each type (e.g., ticker + CIK + company name + citation URI). It also covers internal cascading behavior, making it complete for a lookup tool.

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

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

Input schema has 100% coverage, so baseline is 3. The description adds examples (e.g., 'AAPL', 'ozempic') and clarifies that type enum includes 'company' and 'drug', enhancing understanding beyond schema.

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

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

The description clearly states that the tool resolves user-spoken names to canonical identifiers, with specific examples (ticker, CIK, RxCUI). It distinguishes itself from siblings like compare_entities or entity_profile by focusing on identifier lookup.

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.' It provides supported types and internal methods. However, it does not specify when not to use it or mention alternatives among siblings.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, ensuring safe behavior. The description adds context beyond annotations: it reveals the internal probing mechanism (uses ai_visibility_check per entity) and the output structure (ranked list with score, confidence, signal density). 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 two sentences long with zero wasted words. The main action ('Compare AI visibility across multiple entities side-by-side') is front-loaded, and every sentence adds essential information.

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

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

Despite having no output schema, the description sufficiently explains the return format (ranked list with score, confidence, signal density). It covers the tool's purpose, input expectations, and use case. Given the tool's complexity (multi-entity comparison, ranking), the description is complete enough 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 coverage is 100%, so baseline is 3. The description adds meaningful context: it specifies that the first entity in the 'entities' array is treated as the subject and rest as competitors, and explains the 'context' parameter purpose (disambiguates common names). This adds 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 uses a specific verb ('Compare AI visibility across multiple entities side-by-side'), identifies the resource (AI visibility of entities), and distinguishes from sibling tools like ai_visibility_check (single probe) and compare_entities (general comparison). It also details the process: probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized.

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 clearly states the use case: 'useful for competitive AI-marketing audits' and provides an example question. However, it does not explicitly mention when not to use the tool or list alternatives, though the sibling tool list implicitly offers choices.

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?

Describes partial failure handling (bundlephobia first measurement delay 5-30s, sources_failed lists timeout) and output structure (summary block, advisories, links, versions). Annotations already indicate read-only/idempotent; description adds behavioral details.

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

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

Description is detailed but each sentence serves a purpose: purpose, usage, output, limitations. Could be slightly more concise, but 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?

Covers purpose, usage, output shape, error handling, and ecosystem scope. Without an output schema, the description adequately describes the return structure. All necessary context for an agent to invoke correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds extra details: scoped packages accepted for 'package', and default behavior for 'version' (latest when omitted). Provides additional context beyond schema.

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

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

The description clearly states it's a composite check for npm packages, using specific verbs like 'should I add' and mentions both deps.dev and bundlephobia. It differentiates from siblings by specifying the npm ecosystem scope.

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

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

Explicitly states when to use: if an agent asks about safety/popularity/size of an npm package, or 'what does adding lodash cost me'. Also specifies limitations: NPM only in v1, other ecosystems via deps.dev.

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, destructiveHint=false. Description adds valuable behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. Adds context beyond annotations.

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

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

Four sentences, front-loaded purpose, every sentence adds value: purpose, use case, pairing, technical details, limitations. 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 simple parameters, full schema coverage, and detailed annotations, the description covers output format (passages with offsets and scores), internal mechanics, and limitations. Slightly incomplete without explicit output schema but sufficient for agent decision.

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

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

Schema coverage is 100% so baseline 3. Description does not add new parameter semantics beyond what schema already provides (examples already in 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 'Semantic search INSIDE a fetched record' and differentiates from siblings by mentioning pairing with ask_pipeworx_grounded and saving context for large records.

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

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

Provides clear when-to-use guidance: 'Use when the record is too big to cram into the prompt.' Also suggests pairing with ask_pipeworx_grounded. Lacks explicit when-not-to-use but the positive guidance is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, indicating safe behavior. The description adds that it returns a URL (not the image) and notes 'No interactive JS required,' but does not disclose potential issues like URL expiration or error handling.

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

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

The description is concise and front-loaded, using a single sentence with effective lists. The slash-separated alternative phrases are slightly unconventional but still clear. 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 9 parameters, no output schema, and no parameter descriptions, the description adequately covers the tool's purpose and required inputs but omits details on optional parameters and output format (e.g., URL structure). Leaves moderate 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?

With 0% schema description coverage, the description must compensate but only lists required parameters (style, coords, zoom, width/height) without detailing valid values (e.g., style names, zoom range) or optional parameters (marker, retina, attribution). Minimal guidance beyond parameter 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 tool returns a static map image URL, specifying use cases (embedding in Slack, docs, etc.) and input requirements (style, coords, zoom, width/height). However, it does not explicitly differentiate from sibling tools like tiles_json, though the 'static' vs 'interactive' contrast is implied.

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 when to use the tool (embedding static maps) but does not explicitly state when not to use it or mention alternatives. The sibling list includes related tools, but no direct comparison is made.

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 annotations: requires OAuth, subscription types, delivery channels, rate limits (sms 10/day), and webhook auto-disable after 10 failures. No contradiction with annotations.

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

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

The description is front-loaded with key info, well-structured with sections for types and delivery, and 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 3 parameters with 100% schema coverage, no output schema, the description explains return value (subscription id) and covers all behavioral aspects, making it complete.

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

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

Schema coverage is 100%. The description adds rich context for each parameter, including type-specific filter structures and delivery channel details, going well beyond the schema.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream, distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides context on when to use (proactive monitoring), prerequisites (Pipeworx OAuth account), and examples of supported types. It lacks explicit 'when not to use' but is sufficient.

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

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

Annotations already provide readOnly, idempotent, openWorld hints. The description adds specifics about the return format (category-bucketed examples, drawn from live catalog) and the effect of the topic parameter, 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 detailed but each sentence adds value, from trigger phrases to return structure. While long, it is well-organized and front-loaded with usage scenarios. Minor reduction possible.

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 sufficiently explains the return format (category-bucketed example questions with tool+argument shape) and parameter behavior, covering all needed context 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% (one parameter described). The description adds concrete examples of valid topic values and explains the default behavior (cross-category spread when omitted), going beyond the schema's brief description.

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

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

The description clearly states the tool returns category-bucketed example questions with exact tool+argument shape, serving as an onboarding entry point. It distinguishes itself from siblings by targeting users who don't yet know what to ask.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides when-not guidance by referencing alternatives like ask_pipeworx, entity_profile, etc. Also explains invocation with no args or topic.

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, destructiveHint=false, so the description's listing of returned content adds moderate value but does not disclose error behavior or limitations beyond what annotations provide.

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

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

Two sentences with no redundancy: purpose stated, distinction made, and guidance given in a compact structure.

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

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

The tool is simple (one parameter, no output schema), and the description provides enough context for a specialist user, though missing details on error handling.

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

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

With 0% schema description coverage, the description only implies the parameter is a tileset identifier via context, without specifying valid formats or 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 clearly states 'TileJSON spec metadata for a MapTiler tileset (zoom range, bounds, layer schema)' and explicitly distinguishes from sibling tools like geocode and static_map_url.

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 'most users want geocode / static_map_url instead', indicating when not to use this specialist tool and providing clear alternatives.

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

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

Adds value beyond annotations: clarifies non-destructive nature (deactivate vs delete) and ownership check. Aligns with idempotentHint and destructiveHint annotations.

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

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

Extremely concise: three sentences each serving a distinct purpose (action, ownership, deactivation). No filler or repetition.

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?

Fully adequate for a simple single-parameter tool with no output schema. Covers purpose, ownership, and post-call state behavior.

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

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

Schema covers 100% of parameter description. The description reinforces 'by id' but does not add new semantic detail beyond what the schema already provides.

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

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

Clearly states the action 'Cancel' and the resource 'subscription by id', immediately distinguishing it from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

Specifies ownership enforcement ('you can only cancel your own subscriptions') and behavioral nuance ('deactivated, not deleted'), but lacks explicit when-not or alternative tool guidance.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds that it replaces 4-6 sequential calls, returns a structured verdict with citation, and notes limitations to company-financial claims. 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?

Description is informative and well-structured with example queries, usage guidance, version info, and output details. It is slightly verbose but every sentence adds value.

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

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

Given the tool's simplicity (1 parameter, no nested objects, no output schema), the description fully covers purpose, usage, output format, and domain constraints. No gaps remain.

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

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

Schema coverage is 100% for the single 'claim' parameter, so baseline is 3. Description provides additional examples and clarifies natural-language format, adding minimal extra meaning beyond schema.

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

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

The description clearly states the tool verifies factual claims, using natural-language input against authoritative sources. It is distinct from sibling tools, none of which directly perform claim verification.

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

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

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies support for company-financial claims. Does not explicitly state when not to use or list alternatives, but the context is clear.

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