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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context about the tool's limitations ('not proof of certification or legal sufficiency') and the types of signals it scans for, going beyond the structured fields.

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

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

The description is three concise sentences: the first states the action and purpose, the second lists specific signal types, and the third caveats limitations. Every sentence adds value with no waste, and the key information is front-loaded.

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

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

For a simple one-parameter tool with a complete schema and output schema, the description provides thorough context: what it scans, what it looks for, and its limitations. No critical information is missing for an agent to select and invoke the tool correctly.

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

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

The schema covers 100% of parameter descriptions, so the description doesn't need to elaborate on the 'url' parameter. The description mentions 'public' pages, which aligns with the schema description and adds minimal extra meaning, making a baseline 3 appropriate.

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

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

The description clearly states the tool scans public security, trust, compliance, or legal pages for common enterprise buying signals, using a specific verb and resource. It distinguishes itself by specifying the compliance-posture-related use case and listing concrete signal types, standing out from sibling tools.

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

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

The description gives clear context on when to use the tool ('before you claim a vendor supports a particular compliance posture') and notes it is a signal scanner, not proof. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to infer appropriate usage.

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

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds that the tool is unauthenticated, reports specific fields (HTTP status, content type, etc.), and warns that success only proves basic reachability. 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?

Three sentences, each earning its place: purpose and usage, what is reported, and limitations/caveats. Front-loaded, no wasted words.

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

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

With one simple parameter, output schema mentioned, and annotations covering safety, the description is complete. It covers main use case, output contents, and limitations. Adequate for an agent to decide when to use.

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

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

Schema coverage is 100% with a detailed description of the url parameter (public http(s) URL, normalization). The tool description adds usage context but no new parameter semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool performs a live, unauthenticated fetch against a public URL/API endpoint. It uses specific verbs and resources, and the context distinguishes it from siblings like inspect_security_headers or verify_claim.

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

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

Explicitly states when to use ('simply whether an endpoint currently responds') and when not to use (authenticated flows, POST side effects, JavaScript execution, deeper business logic). Provides clear guidance on appropriate use cases.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: results are cached for 5 minutes, and JavaScript-rendered, logged-in, or heavily obfuscated pricing details can be missed. This goes beyond the annotations without contradicting them.

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

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

The description is compact and front-loaded: purpose first, then usage context, then limitations and caching. Every sentence provides meaningful information with no fluff 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?

Given the tool's simple scope, the description fully covers what the tool does, its limitations, cache behavior, and return content. With annotations and output schema also present, this is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100% for the single url parameter, with a clear description. The tool description adds extra guidance: 'Prefer the specific pricing page, for example https://stripe.com/pricing, rather than a generic homepage.' This enriches the parameter semantics 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 verb and resource: 'Fetch a public pricing page and extract first-pass pricing signals.' This distinguishes it from siblings like compare_pricing_pages, which likely compares multiple pages, and check_endpoint, which is more generic. The purpose is immediately clear.

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

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

The description explicitly says 'Use this when you already have a likely pricing URL and need a quick live scan of visible page text.' It also lists exclusions: 'does not map prices to exact plans, normalize currencies, execute checkout flows, or guarantee that a price applies to a specific region or customer type.' This provides 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?

Adds substantial context beyond annotations: 'returns per-package registry metadata in input order, with field availability varying by registry. Missing or unpublished packages return found=false. Registry responses are cached for 5 minutes.' This enriches the readOnly/idempotent/openWorld hints with specific behavior.

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

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

Four sentences, each packed with distinct value: core action, use cases, return/missing behavior, caching. No fluff or repetition; 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 essential context: what it does, when to use, when not to use, output behavior, and caching. With a rich output schema and strong annotations, the description is fully complete for the tool's complexity.

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

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

Schema description coverage is 100%: both packages and registry are already well-described in the schema. The description only reinforces 'exact package names' without adding meaningfully new parameter-level information, so baseline 3 is appropriate.

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

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

Clearly states the verb and resource: 'Compare two or more exact package names side by side using live npm or PyPI metadata.' It also differentiates from siblings by explicitly saying 'Do not use it to discover unknown alternatives,' which distinguishes it from tools like estimate_market.

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 positive conditions ('Use this when you already know the candidate packages and need evidence for claims...') and negative exclusions ('Do not use it to discover unknown alternatives, estimate market size, or compare packages across different registries'), effectively guiding tool selection without needing to read sibling names.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable context beyond that: the output is heuristic and page-level, does not map every price to every plan, and does not normalize regional billing differences. This discloses important output quality limitations that an agent needs to know before relying on results.

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

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

Two sentences, no fluff. The first sentence states the purpose and use case; the second states limitations. Information density is high and every word earns its place. The description is front-loaded with the primary action.

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

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

Given a single well-documented parameter, strong annotations, and an output schema (not shown but present), the description covers what the tool does, when to use it, and its limitations. There are no obvious gaps in the agent's ability to select and invoke the tool correctly.

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

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

The schema fully documents the 'pages' parameter, its items, and required properties, so baseline is 3. The description adds semantic meaning about what the comparison focuses on (visible prices, free-plan signals, plan-name hints), which goes beyond the schema's structural definitions. It also clarifies that URLs must be public pricing pages.

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

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

The description clearly states the tool's action ('Compare two to five public pricing pages side by side') and its scope. It distinguishes itself from siblings by emphasizing a quick, live comparison of visible prices, free-plan signals, and plan-name hints, which is distinct from broader competitor analysis or single-page checks.

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

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

Explicit usage guidance is provided: 'Use this when you want a quick, live comparison of visible prices, free-plan signals, and plan-name hints across vendors.' The description also implicitly states when not to use it by noting limitations (heuristic, page-level, no normalization), which serves as an exclusion criterion.

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 show readOnlyHint=false, destructiveHint=false. The description adds that it creates a persistent resource, runs automatically, and persists results, which is consistent and adds useful behavioral context beyond the annotations.

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

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

The description is two sentences, front-loaded with purpose, and every part is informative. No fluff 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?

Given the tool creates a monitor with 6 parameters (3 required) and has an output schema, the description covers purpose, execution, storage, and API key requirement. It does not explain the output, but that's expected with an output schema. Minor missing details like error conditions are not critical.

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

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

The input schema has 100% coverage with detailed parameter descriptions. The tool description summarizes target types and schedule but does not add significant new meaning beyond the schema, so baseline 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?

The description clearly states the verb 'Create' and the resource 'persistent monitor', and lists the various target types (URL, pricing page, etc.), distinguishing it from siblings like list_monitors, delete_monitor, check_endpoint, etc. It is specific and actionable.

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 scheduling (automatic via cron or on-demand via run_monitor_now), storage in SQLite, and the requirement for a team API key. It provides context for when to use the tool but does not explicitly contrast with sibling tools like check_endpoint for one-time checks.

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

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

Adds beyond annotations: specifies permanent deletion of results and auth requirement. Annotations give destructiveHint but description enriches with specifics.

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, zero fluff. Key information front-loaded.

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

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

With output schema present, description covers purpose, irreversibility, auth. Sufficient for a destructive 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% and monitor_id description matches tool context. Description adds no extra parameter 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 uses specific verb 'delete' and resource 'monitor and all its stored results', clearly distinguishing it from siblings like create_monitor, list_monitors.

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?

States 'cannot be undone' implies cautious use, and 'Requires a team API key' gives prerequisite. No explicit when-not or alternatives, 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 declare read-only, idempotent, open-world, and non-destructive. The description adds valuable behavioral context: responses are cached for 5 minutes, and results reflect search relevance not market share, which complements the annotations without contradiction.

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

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

Four concise sentences deliver purpose, usage, exclusions, and caching behavior with no filler. The most important information is front-loaded in the first sentence.

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 two-parameter read-only search tool with an output schema and rich annotations, the description covers key behaviors, limitations, and cache behavior. It is fully adequate for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% with descriptive text for both query and registry. The description itself adds only an example phrase ('edge orm') and reiterates the npm/PyPI choice, providing no additional semantic value beyond the schema, so baseline 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 the tool searches npm or PyPI to estimate package category crowdedness before making market claims. This is a specific verb-resource-purpose combination that distinguishes it from sibling tools focused on compliance, endpoints, pricing, and monitors.

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 it ('when you have a category or search phrase') and what not to use it for ('Do not use it to compare exact known package names or to infer adoption from downloads'). However, it does not name a specific alternative tool, slightly reducing the guidance completeness.

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

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

Annotations already declare readOnlyHint, destructiveHint, and idempotentHint, indicating a safe, read-only, idempotent operation. The description adds an important behavioral detail: it requires a team API key. No contradictions with annotations.

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

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

The description is concise with two sentences that front-load the purpose and immediately follow with content and a requirement. Every sentence adds value with no wasted words.

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

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

Given that an output schema exists (not shown but indicated), the description does not need to explain return values. It mentions the content of the report but could explicitly reference the 'period' parameter to clarify the time window. Overall, it is mostly complete.

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

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

Schema description coverage is 100%, so the schema already documents both parameters fully. The description does not add any additional semantics or context about the parameters, remaining at baseline.

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

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

The description clearly states the tool generates a summary report of monitor activity for a time window, listing specific content such as monitors run, changes, failures, risk levels, and follow-up actions. The verb 'generate' and the resource 'change report' are specific and distinct from sibling tools.

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

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

The description provides a clear use case (summary report for a time window) and mentions a prerequisite (requires team API key). However, it does not explicitly state when not to use this tool or suggest alternatives among siblings, so it is slightly less than perfect.

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, idempotentHint=true, so the safety profile is clear. The description adds that it requires a team API key and lists the data fields returned, providing context beyond annotations without contradiction.

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

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

The description consists of two concise, front-loaded sentences with no wasted words. It efficiently delivers the core purpose and a key requirement (team API key).

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 existence of an output schema (not shown but present), the description need not detail return values. It covers the tool's purpose, data included, and a critical prerequisite (team API key). With 100% schema coverage for parameters and a clear context of sibling tools, it is sufficiently complete for a read-only retrieval 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 description coverage is 100% for both parameters (monitor_id, limit). The description does not add additional meaning beyond the schema; it only implicitly mentions temporal ordering but no extra parameter details. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Retrieve' and the resource 'most recent run results for a monitor', listing specific data included (change details, confidence score, evidence URLs, error info). It differentiates from siblings like list_monitors (which lists monitors) and run_monitor_now (which triggers a run).

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 does not explicitly state when to use this tool versus alternatives. It implies usage when needing recent run results, but lacks exclusions or comparisons to sibling tools like generate_change_report or verify_claim.

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

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

Annotations already indicate safe, idempotent read. Description adds behavioral details: fetches URL, inspects specific headers, normalizes bare domains to HTTPS, and warns about limitations. 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?

Multiple sentences but each serves a purpose: action, usage context, list of headers, and limitation. Front-loaded with the core action. No unnecessary words; efficient and clear.

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

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

Given the simplicity (1 param, rich annotations, output schema exists), the description covers purpose, usage, limitations, and behavioral nuances completely. No gaps.

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

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

The schema already fully describes the 'url' parameter with high coverage (100%). Description does not add new semantics beyond usage context; it reinforces 'public URL' but not necessary. Baseline 3 is appropriate.

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

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

The description explicitly states the tool fetches a public URL and inspects security headers, listing specific headers. It distinguishes itself from siblings by focusing on quick due diligence for browser security, not replacing full reviews.

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

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

It provides clear when-to-use: before claiming a strong security baseline, for quick due diligence on public apps/docs. It also states limitations: does not replace real security review, authenticated testing, or vulnerability scanning. No explicit sibling alternatives, but context is sufficient.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying that a team API key is required and that the output includes last run status and schedule. This provides context beyond the annotations, though it does not detail any potential limits or pagination.

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

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

The description is concise with two sentences. The first sentence front-loads the key functionality, and the second adds a necessary requirement. There is no redundant or wasted text.

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

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

For a simple listing tool with one optional parameter, comprehensive annotations, and an existing output schema, the description covers all necessary aspects: purpose, returned data, and authentication requirement. No gaps are apparent.

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 single parameter 'active_only' is fully described in the input schema with a default value and explanation. The description does not add any extra semantics beyond what is already in the schema, so it meets the baseline for 100% schema coverage.

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

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

The description clearly states the verb ('List'), the resource ('monitors'), the scope ('owned by this API key'), and what is included ('with last run status and schedule'). This distinguishes it from sibling tools like 'get_monitor_result' which focuses on a single monitor's result.

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 a prerequisite ('Requires a team API key') but does not explicitly guide when to use this tool versus alternatives like 'list_resources' or when not to use it. The context from sibling tools helps differentiate, but the description itself lacks comparative guidance.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds value by stating 'Zero-cost schema discovery' and 'No quota consumption, no API key required', which are behavioral traits beyond annotations. No contradictions.

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

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

Three sentences, each serving a clear purpose: stating the action, highlighting value, and providing usage guidance. 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 zero parameters, rich annotations, and presence of output schema, the description adequately covers what the tool does. It mentions output (list of tools and access tiers), and the output schema can provide further detail.

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 zero parameters (100% coverage). Description doesn't need to add parameter details. Baseline for no parameters is 4, and no additional info is required.

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 'List all available Ground Truth tools and their access tiers', using a specific verb and resource. This distinguishes it from sibling tools like list_monitors (which lists monitors) and compare_competitors (which compares competitors).

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 'Call this to explore what verification tools are available before making a tool call', providing clear context for when to use. Also mentions zero cost and no quota consumption. Could be improved by noting when not to use (e.g., for data retrieval), but overall clear.

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

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

Annotations provide no readOnly or destructive hints, but the description adds valuable behavioral details: it records results, returns a change indicator, counts against monthly quota, and requires a team API key. 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 three sentences with no wasted words. The first sentence clearly states the main action. It could be slightly more concise, but it is well-structured and front-loaded.

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

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

Given the low parameter count and presence of an output schema, the description covers key aspects: usage context, quota impact, auth requirements, and result behavior. It is sufficiently complete for the tool's complexity.

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

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

With 100% schema description coverage, the input schema already documents the single required parameter 'monitor_id'. The description does not add further semantic detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool runs a monitor's verification check immediately outside its normal schedule. It uses specific verbs and resources, and effectively distinguishes itself from sibling tools like list_monitors or create_monitor.

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 indicates this tool is for immediate checks outside normal schedule and mentions quota impact, but does not explicitly state when not to use it or provide alternatives like get_monitor_result for past results.

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, so the safety profile is covered. The description adds substantial behavioral context beyond annotations: tests run in order with no branching or early exit, the verdict mapping is fully mechanical (all passing => SUPPORTED, zero passing => REFUTED, otherwise PARTIALLY SUPPORTED), and the tool is not an open-ended research agent. This goes well 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?

The description is compact and well-organized: first sentence defines the action, second covers typical use cases, third imposes constraints, fourth explains the verdict algorithm, fifth names alternatives. Every sentence earns its place, and there is no redundant or filler content. It is appropriately sized for a tool of this complexity.

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

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

Given the tool's moderate-to-high complexity (multiple test types, ordered execution, verdict logic), the description covers all key aspects: what it does, when to use it, behavioral constraints, verdict determination, and sibling alternatives. An output schema exists for return values, and the description already explains the verdict mapping, so nothing critical is missing.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds some context by listing example check types (endpoint reachability, npm search counts, substring presence) and emphasizing that tests must be explicitly defined, but this largely restates information already in the schema. The hypothesis parameter is self-explanatory. No additional syntax or format details beyond the schema are provided, so a 3 is appropriate.

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

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

The description clearly states the tool 'Run a small verification plan made of concrete live checks and summarize whether a hypothesis is supported' with a specific verb and resource. It also distinguishes itself from siblings by explicitly naming alternatives (verify_claim, estimate_market, compare_competitors) and framing itself as a coordination tool, not an open-ended research agent.

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 when-to-use guidance ('Use this when one conclusion depends on multiple simple checks...') and when-not-to-use guidance with named alternatives ('Use verify_claim when you already have evidence URLs...'). It also restricts usage by stating 'every test must be explicitly defined in advance' and 'no branching or early exit', which clarifies the boundary versus more flexible research tools.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds the algorithm: case-insensitive keyword match, half-threshold for support, cache duration. No contradiction with annotations.

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

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

Front-loaded with primary action, each sentence adds value, no unnecessary words. Length is appropriate for the tool's complexity.

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

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

Covers all necessary aspects: purpose, when to use, algorithm, caching. Given the presence of an output schema for return values, the description is complete.

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

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

Despite 100% schema coverage, the description adds meaningful context: explains the matching is case-insensitive substring and suggests choosing phrases verbatim, and clarifies the half-keyword threshold for support.

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 specifies the verb 'check' and resource 'factual claim supported by evidence URLs', and distinguishes from siblings by explicitly stating it is not for open-ended search, semantic reasoning, or contradiction extraction.

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 ('evidence-backed claim checks on known pages') and when not to use ('not for open-ended search...'), with clear context about keyword matching and caching.

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