Webhook Studio

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

No annotations are present, so the description must disclose behaviors itself. It explains the need to set secret and hmac_enabled:true in the same call and that signature scheme auto-detects from header shape, adding meaningful behavior beyond the schema. It doesn't detail permissions or effects on existing settings, but the core mutation behavior is sufficiently described.

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

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

Two concise sentences front-load the primary action and provide essential integration context without repetition. No wasted words.

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

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

For a configuration tool with no annotations and no output schema, the description covers the main purpose, the critical coupling of secret with hmac_enabled, and auto-detection behavior. It omits details about on_failure/tolerance_seconds, but those are already explained in the schema, so the description is suitably complete for its 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?

The schema already documents all 6 parameters with descriptions (100% coverage), so the baseline is 3. The description enriches the meaning of hmac_enabled and header_name by revealing they work together and auto-detection behavior, pushing it to a 4.

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

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

The description clearly states the tool's function with a specific verb ('Turn on') and resource ('HMAC signature verification for a bucket'), and the phrase 'finish a verified webhook integration' distinguishes it from sibling bucket management tools like create_bucket or delete_bucket.

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 context that this is the final step in a verified webhook integration, which implies it should be used after bucket creation and webhook setup. However, it does not explicitly list alternative tools or exclusions, so it earns a 4 rather than 5.

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?

With no annotations provided, the description carries the full burden. It discloses what the tool returns (id, public ingest url, inbox_url, signing secret) and highlights the idempotent behavior of external_ref: 'idempotent re-provisioning and re-finding.' This provides useful behavioral context beyond the schema, though it does not cover every edge case (e.g., default values or environment 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 three sentences long, front-loaded with the primary action and return values, then a focused tip about external_ref. No wasteful or redundant text; every sentence earns its place.

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

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

For a creation tool with no output schema and no annotations, the description is quite complete: it explains the purpose, key return values, and the idempotency mechanism. It does not describe the format of the returned values or any potential limits, but given the tool's simplicity and the schema's coverage, this is sufficient.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds extra meaning for external_ref by giving a concrete example ('acme-api:stripe') and explaining how it enables re-finding with list_buckets, which goes beyond the schema's description. Name and environment are not elaborated, but the schema already covers them adequately.

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 function: 'Provision a new webhook endpoint (a "bucket") that captures incoming HTTP requests.' It uses a specific verb (provision) and resource (webhook endpoint/bucket), and distinguishes itself from siblings like delete_bucket and list_buckets by explicitly being the creation tool.

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

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

The description implies when to use this tool (to create a new webhook endpoint) and provides a concrete usage tip: pass external_ref to re-find the bucket later with list_buckets. It does not explicitly list alternatives or exclusions, but the creation use case is clear and the reference to list_buckets guides follow-up actions.

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?

With no annotations provided, the description carries the full burden. It clearly discloses that deletion is permanent and cascades to captured events, which is critical safety information for an agent. However, it does not mention response behavior or error conditions, but for a simple delete tool this is sufficient.

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 that front-loads the action and includes the key scope detail. There is no redundant or unnecessary 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 tool with one parameter and no output schema, the description adequately covers the purpose, the destructive scope, and the parameter via the schema. It could mention side effects on related resources, but the description already covers the most relevant one (captured events).

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 one parameter, bucket_id, with a description of 'The endpoint id.' Since the schema covers 100% of the parameters and the tool description adds no extra meaning beyond that, 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 uses the specific verb 'delete' and identifies the resource as 'webhook endpoint and its captured events,' clearly distinguishing it from sibling tools like get_bucket and list_buckets. The word 'permanently' adds important scope and irreversibility.

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

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

The description implies usage for permanently removing a webhook endpoint, but it does not explicitly state when to use this tool versus alternatives or include exclusions. There are no prerequisites mentioned, so it falls at the 'implied usage' level.

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?

With no annotations, the description carries the full burden. It discloses a key behavioral trait—'produced without reading a single payload'—and explains the output includes breaking-change flags. This goes beyond the schema, though it omits details like error handling or permissions.

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 short, but the second sentence ends with a garbled fragment ('from defaults to the version before to.') that appears to be an incomplete thought. This structural issue makes it less polished and slightly confusing.

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

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

Given the tool has no output schema, the description does a good job of explaining the high-level return content (field-level changes with breaking flags). However, the confusing trailing fragment and lack of error/edge-case context prevent it from being fully complete.

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

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

The schema descriptions cover 100% of parameters, so the baseline is 3. The description's only parameter-related addition ('from defaults to the version before to') is a confusing fragment that repeats the schema's default for 'from' without adding clarity.

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

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

The description clearly states the tool compares two schema versions and identifies added, removed, or type-changed fields with breaking-change flags. This specific verb+resource distinguishes it from siblings like get_schema or list_schemas.

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 context by framing the tool as the answer to 'why did my handler start failing', implying it should be used when investigating schema-related failures. However, it does not explicitly mention alternatives or when not to use it, so it falls short of full guidance.

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

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

With no annotations, the description carries the full burden. It discloses that results are ranked by consistency, derived from real traffic, and account-specific. This goes beyond a generic 'list' statement. It doesn't mention return format, but the core behavioral traits are well covered.

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 dense sentences front-load the primary action and then enrich with examples and data source. Each sentence earns its place, though the second sentence could be slightly trimmed without losing value.

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

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

For a low-complexity read-only tool with high schema coverage, the description is complete enough. The absence of an output schema is mitigated by the clear 'list' semantics and explanatory context about ranking and source. No critical 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?

The input schema already provides full coverage (100%) with clear descriptions for both schema_id and version. The description adds contextual meaning but does not explain parameter-specific syntax or defaults beyond what the schema already says, 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 action ('List the correlation-key candidates'), the resource ('a learned schema'), and provides concrete examples (Stripe payment_intent, GitHub PR) to illustrate the identifier-like fields. This makes it distinct from sibling tools that manage buckets or events.

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

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

The description explicitly explains when to use this tool: 'to group events by when reconstructing a lifecycle.' It also notes the data is 'derived from real traffic,' implying it reflects actual account data. While no explicit alternative is named, the use case is clear enough to guide tool selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the side effect of creating both a destination and a routing rule in one call, and implies persistence with 'standing forwarding.' It does not disclose potential idempotency, validation, or error behaviors, but the key side effect is clearly communicated, exceeding the baseline.

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 the core purpose, followed by concise usage guidance. No redundant or filler content. Every sentence contributes meaning, and the structure is easy to scan.

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

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

Given the tool's complexity (6 parameters, nested condition_tree, multiple resources created) and lack of output schema, the description covers the core semantics and the most important usage scenarios. It does not explain return values or prerequisites (e.g., bucket must exist), which would be helpful, but the essential context for invoking the tool is present.

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

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

The input schema already covers all 6 parameters with descriptions (100% coverage). The description adds value by explaining the relationship between forward_all and condition_tree as mutually exclusive alternatives, and by referencing the condition_tree grammar as the same as the routing engine. This goes beyond the schema's static parameter descriptions.

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

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

The description explicitly states the action: 'Set up standing forwarding from a bucket to a URL.' This is a specific verb with a clear resource and destination. It distinguishes from sibling tools because none other mention forwarding, and it adds detail that it creates the destination and routing rule in one call.

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 guidance on parameter choice: use forward_all:true for all events, or condition_tree for matching events. It also references the routing engine's AND/OR grammar, which gives context. No explicit sibling alternatives are named, but the tool's unique purpose within the sibling set makes this less critical. It lacks exclusions or when-not-to-use scenarios, so not a 5.

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?

With no annotations, the description carries the full burden. It does disclose important behavioral traits: outputs are derived from actual received payloads, genuinely-optional fields are marked optional, and framework handlers are ready-to-paste. However, it does not mention side effects, error behavior, or resource impact, leaving some uncertainty about whether the operation is purely in-memory or mutates state.

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 well-structured sentences that convey the core purpose, underlying logic, and parameter usage. It is somewhat long, but every clause adds information about behavior or usage, with no wasted phrases.

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 complexity (4 params, no output schema) and the absence of annotations, the description covers the essential aspects: the two output modes, the data source, and the optionality behavior. It lacks edge-case details (e.g., invalid schema_id, no payloads), but is complete enough for an agent to select and invoke the tool correctly in common scenarios.

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

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

The input schema already covers all four parameters with descriptions. The description adds valuable meaning beyond the schema: it explains the relationship between 'lang' and 'framework' (framework overrides lang), clarifies what 'lang' values produce, and indicates that 'framework' yields a handler scaffold. This additive context justifies a higher-than-baseline score.

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

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

The description clearly states the tool's purpose: generating types, validators, or handlers from a learned schema. It uses a specific verb ('Generate') and resource ('from a learned schema'), and distinguishes itself from sibling tools like get_schema by emphasizing that output is derived from actual payloads rather than model memory.

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 good context for when to use the tool ('the payoff of the knowledge layer') and explains that passing 'lang' yields a bare type while passing 'framework' overrides it for a handler. However, it does not explicitly contrast with alternatives (e.g., get_schema) or state when not to use it.

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?

There are no annotations, so the description must convey behavior. 'Fetch' implies a read-only operation, but it does not explicitly state that it has no side effects, nor does it mention authentication requirements, error behavior (e.g., 404 on missing id), or response format beyond the two mentioned fields. This is adequate but lacks depth.

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

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

The description is a single, front-loaded sentence that states the action, resource, scope, and key return fields without any redundant wording. Every word contributes to understanding the tool.

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

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

For a simple, single-parameter fetch tool with no output schema, the description adequately covers the core purpose and partially describes the return value (url and inbox_url). It does not detail error cases or authorization, but these are less critical for a straightforward get operation, making this sufficiently complete.

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

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

The input schema provides 100% coverage of the single parameter (bucket_id) with a description ('The endpoint id.'), so the schema already explains the meaning. The description's mention of 'by id' adds no additional semantic information beyond what the schema provides, thus yielding the baseline score.

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

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

Description uses the specific verb 'Fetch' and clearly identifies the resource ('one webhook endpoint') and the access pattern ('by id'), while also mentioning the return fields (url, inbox_url). This clearly distinguishes it from sibling tools like list_buckets (all buckets) and delete_bucket (delete).

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 usage context is implied by the 'by id' phrasing, suggesting this tool is for retrieving a single bucket when the id is known, rather than listing all buckets. However, it does not explicitly mention alternatives or exclusions, such as 'use list_buckets to list all endpoints', leaving the guidance implicit.

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?

With no annotations provided, the description carries the behavioral transparency burden. It discloses the return payload (headers, body, signature verification result, failure reason, delivery attempts) and implicitly indicates a read-only fetch. However, it doesn't mention error behavior, permissions, or what happens if the event doesn't exist, which would add further clarity.

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 no redundant words. It opens with the action and resource, then immediately adds value with the second sentence explaining the tool's purpose. Every word 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?

The tool has no output schema, but the description adequately explains what will be returned, covering headers, body, signature verification, and delivery attempts. It is complete for a straightforward event fetch, though it omits error scenarios and non-success response 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?

The schema already documents the only parameter 'event_id' with 'The event id.', and the description adds no additional meaning about the parameter's format, constraints, or interpretation. Since schema coverage is 100%, the baseline 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 uses the specific verb 'Fetch' and clearly identifies the resource as 'one captured event by id', listing exactly what is included (headers, body, signature verification result, failure reason, delivery attempts). This distinguishes it from sibling tools like list_events or latest_event, which list events without per-event delivery details.

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 second sentence provides clear context for when this tool is appropriate: to determine whether a forward reached its target and what came back. While it doesn't explicitly name alternative tools or exclusion conditions, the use case is specific enough to guide selection among the 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?

With no annotations provided, the description carries the full burden. It adds useful context that the schema is derived from actual account-received data, not guessed from training data, which is a behavioral trait. However, it does not disclose potential error behavior, side effects, or confirm it is a safe read-only operation beyond the verb 'fetch'.

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

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

The description is a single, well-structured sentence that front-loads the action ('Fetch one learned schema') and then elaborates with relevant detail. Every word contributes value with no redundancy or filler.

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

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

For a simple 2-parameter fetch tool with no output schema, the description adequately covers what is returned (fields, types, optionality, frequency) and notes the account-specific provenance. It could mention the return format, but the summary of contents is sufficient for most use cases.

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

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

The input schema already provides descriptions for both parameters (schema_id and version), achieving 100% coverage. The tool description does not add additional parameter-level meaning, so the 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?

The description clearly states the tool fetches one learned schema, listing exactly what details are included (fields, types, optionality, appearance frequency). It distinguishes from siblings like list_schemas (which lists schemas) and diff_schema (which compares schemas) by focusing on a single schema's full detail.

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

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

The description implies usage when a single schema's detailed structure is needed, and the context is clear. However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of a 5 but has clear context.

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

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

With no annotations, the description carries the full behavioral burden and it does well by specifying what is returned ('headers, body and delivery results') and the error condition ('404 if nothing matches'). This gives the agent a solid understanding of the tool's runtime behavior, though it does not discuss auth or rate limits.

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 with the core action, followed by helpful context and an illustrative example. Every sentence contributes value: scope, return contents, and error behavior.

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

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

For a tool with three parameters and no output schema, the description covers the essential context: what it returns, how filtering works, and what happens when no event matches. It stops short of detailing the delivery results structure, but that is likely 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 100%, so the parameter descriptions already explain 'type', 'provider', and 'bucket_id'. The description merely restates that filtering by provider and event type is possible, adding no new semantic detail 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 starts with a specific verb-resource pair: 'Get the single most recent event captured by a bucket', which clearly identifies the tool's function and scope. It also differentiates from siblings like get_event or list_events by emphasizing 'single most recent' and optional filters.

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 context for when to use the tool: as 'the one-shot answer to what did Stripe last send me'. It implies usage for retrieving a single latest event without explicitly contrasting with alternatives like list_events, but the purpose is unambiguous.

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?

With no annotations, the description carries the full burden. It indicates read-only behavior via 'List' and adds the re-anchor context, but it does not disclose potential side effects, auth requirements, or return format. The description goes beyond a tautology but lacks deeper behavioral detail.

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

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

The description is two sentences and about 40 words. It front-loads the core function, then adds the key parameter usage and a motivational scenario. Every sentence earns its place with no redundancy.

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

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

Given the tool has only one optional parameter and no output schema, the description covers the essential aspects: what it does, how the parameter works, and when to use it. It does not explain the return format in detail, but for a simple list operation this is 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 for the single parameter is 100%, so baseline is 3. The description adds meaningful context by explaining the real-world purpose of external_ref: 'fetch the specific bucket you created earlier under that name' and 're-anchor after losing the conversation,' which enriches the schema's terse 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's purpose: 'List your webhook endpoints.' It also explains a secondary behavior with external_ref to fetch a specific bucket. However, it does not explicitly distinguish from sibling get_bucket, so it is clear but not fully differentiated.

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

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

The description provides a concrete use case: 'Pass external_ref to fetch the specific bucket you created earlier under that name — this is how you re-anchor after losing the conversation.' This gives clear when-to-use context. It does not mention alternatives or exclusions, but the scenario is explicit.

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?

With no annotations provided, the description carries the burden. It discloses the sort order ('newest first'), the return content ('full request and response of each'), and the filtering behavior for success. This adds meaningful behavioral context beyond the schema.

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

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

Two sentences, front-loaded with the primary action and resource. The second sentence provides a targeted usage tip. 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?

The description is complete for a list tool: it explains the core output (full request/response), ordering, and a common diagnostic use case. It does not explain pagination or the source filter, but those are covered by the schema, and there is no output schema to complicate matters.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by specifically explaining the success:false parameter and its use case, going beyond the schema's simple boolean filter 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 'List' and the resource 'outbound delivery attempts for a bucket', with specifics like 'newest first' and 'full request and response'. This distinguishes it from sibling tools like list_events and list_buckets.

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 an explicit use case: passing success:false to see only failures, framed as the direct answer to 'which of my forwards are broken and why'. It does not name alternatives or exclusions, but the context is clear and actionable.

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

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

With no annotations, the description carries the burden of revealing behavior. It discloses the ordering ('newest first'), pagination mechanism ('Use cursor for the next page'), and nuanced filter behavior such as q being case-insensitive substring over multiple fields. This goes beyond a minimal 'list events' statement, though it stops short of describing error handling or response structure.

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

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

The description is two sentences, with the first sentence leading with the action and key resource, then listing filters, and the second sentence handling pagination. Every word serves a purpose; no filler or repeated schema information.

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

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

The description competently covers filtering, ordering, and pagination, which are the primary usage concerns. Since there is no output schema, it could have described the response shape (e.g., array of events and a cursor), but the absence is not critical for a list operation. Overall, it is mostly complete for a tool of this complexity.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds value beyond the schema by clarifying the accepted date formats for both since and until (the schema only specifies format for since), and by explaining the cursor's role in pagination. These additions enhance understanding of the parameters beyond their individual schema entries.

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

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

The description opens with 'List events captured by a bucket, newest first,' a specific verb and resource that clearly states what the tool does. It also distinguishes itself from siblings like get_event (single event) and list_buckets by focusing on listing events for a specific bucket with an explicit sort order.

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

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

The description provides clear usage context by enumerating composable filters and instructing to use cursor for pagination. It does not explicitly name alternatives (e.g., 'use get_event for a single event'), but the context strongly implies when this list tool is appropriate, which meets the 'clear context, no exclusions' level.

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?

With no annotations provided, the description carries the burden. It discloses a key non-obvious trait: 'These outlive the payloads they were learned from, so this answers "what shapes do I know about" even after old events are gone.' This is valuable behavioral context beyond the basic list action.

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: the first states the action, the second explains persistence behavior, and the third gives usage guidance. No filler or redundant phrasing; 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 list tool with full schema coverage and no output schema, the description provides sufficient context: what it lists, that schemas persist, and how to start using it. It does not specify the exact return format (e.g., whether it returns schema IDs or full definitions), but the 'Start here' guidance mitigates this gap.

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 only restates the two filters ('filtered by provider and event_type') without adding extra detail about parameter behavior, formats, or interplay. The baseline of 3 applies since the schema already documents both parameters adequately.

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 'List' and clearly identifies the resource: 'payload schemas Webhook Studio has learned from this account's real traffic.' It distinguishes from siblings like get_schema (retrieving one schema), diff_schema (comparing), and generate_types (generating types) by framing this as the discovery entry point.

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 says 'optionally filtered by provider and event_type' and explicitly positions the tool as the starting point: 'Start here to discover what you can generate types or diffs for.' However, it does not explicitly state when not to use alternatives, though the context strongly implies this is for enumeration and discovery.

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?

With no annotations, the description carries full burden and rises to the occasion. It discloses that preserve_headers defaults to true, how resign_with re-signs the payload, the signature header behavior (e.g., Stripe-Signature), and that the tool returns 'response status, latency and body'. This offers substantial behavioral context beyond the schema.

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 tight four sentences: purpose, one default behavior, one conditional behavior, and the return value. Each sentence earns its place with no filler, and the most important information is front-loaded.

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

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

Given the tool has 7 parameters, no output schema, and no annotations, the description covers the essential use case, the tricky signing flow, and the return format. It doesn't explain headers/body overrides in prose, but the schema handles those. For a test-runner tool, this is reasonably 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%, so baseline is 3. The description adds meaningful context on top: it explains the default for preserve_headers, the purpose and usage of resign_with, and gives an example of a signature header. This moves it above baseline, though not every parameter (body, headers) receives narrative treatment.

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

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

The description opens with 'Re-send a captured event to any URL' — a specific verb+object pair — and grounds it as 'the test runner for a handler you just wrote'. This clearly distinguishes it from sibling tools like get_event or forward_bucket.

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 states the primary use case ('test runner for a handler you just wrote') and provides conditional guidance for using resign_with ('If you changed the body or the receiver verifies with a different secret'). While it doesn't explicitly name alternatives like forward_bucket, the context is sufficient for an agent to know when to select 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?

With no annotations, the description carries the full burden of behavioral disclosure. It states that the tool blocks until a matching event or timeout, returns the event or nothing, and that filters prevent unrelated events from waking it. This covers the main behavioral traits without contradicting anything. It does not mention side effects or permissions, but for a simple wait operation these are not critical.

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 fluff. It states the core behavior, gives a concrete example of when to use it, and clarifies filtering and return value. Every sentence earns its place and the most important 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 tool with 4 simple parameters, no output schema, and no complex nested objects, this description is complete. It explains what the tool does, when to use it, how filters work, and what it returns on success and timeout. An agent has enough to invoke it correctly without additional context.

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

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

The schema covers 100% of parameters, so the baseline is 3. The description adds semantic value beyond the schema by explaining that the provider and type filters ensure 'unrelated traffic does not wake it', which clarifies their purpose in the overall workflow. It also reinforces the timeout behavior. This extra context justifies a score above baseline.

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

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

The description starts with a specific verb and resource: 'Block until the next matching event arrives on a bucket'. This clearly states the tool's function and distinguishes it from siblings like get_event or list_events, which are non-blocking retrieval operations. It also specifies the timeout condition, making the purpose unambiguous.

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

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

The description provides a concrete, relatable use case: turning 'go click Send test webhook in Stripe' into a synchronous step. It explains when this tool is appropriate (waiting for an event triggered externally) and implies that filters should be used to avoid irrelevant traffic. It does not explicitly name alternative tools or state when not to use it, but the context is clear enough for an agent to decide.

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