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

No annotations exist, so the description carries the full burden. 'Get' implies a non-destructive read and it discloses the returned content (widgets, queries, layout), but it says nothing about authorization requirements, rate limits, or behavior when the dashboard ID does not exist.

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?

A single front-loaded sentence with the verb and resource first and the returned contents second. No filler or redundant restatement of the name.

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 one-parameter read tool with no output schema, the description covers both purpose and return shape adequately. It stops short of noting error behavior for invalid IDs or any prerequisite, which keeps it from a 5.

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 sole dashboardId parameter is fully documented with a format example in the schema. The description adds no format or syntax detail beyond that, so the 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?

States a specific verb (Get) and resource (Datadog dashboard), and enumerates the returned scope: widgets, queries, layout. The singular 'a dashboard' implicitly separates it from the sibling list_dashboards, though it never names that sibling.

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?

Usage is only implied: use this to fetch one dashboard when you already have its ID. There is no explicit when-to-use, when-not-to-use, or reference to the list_dashboards alternative for 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the high-level output but does not mention read-only safety, permissions, rate limits, side effects, or return format.

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

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

A single concise sentence that is front-loaded with the action and resource. There is no wasted wording, making it appropriately sized for the amount of information provided.

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

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

For a simple read-only count tool with no annotations or output schema, the description is minimally adequate. However, it omits important context such as the time-window behavior implied by the 'from' parameter and the structure of the returned counts.

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 'from' parameter, so the schema already documents it fully. The description adds no additional meaning or format details beyond what the schema provides, making the 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?

States a specific verb and resource: 'Get total counts of active and up hosts in Datadog.' Clear on what it does, but does not distinguish itself from the sibling list_hosts tool, which also deals with hosts.

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?

Gives no guidance on when to use this tool versus alternatives such as list_hosts. There is no context about when a count is preferable to a full host listing.

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?

No annotations are provided, so the description carries the full burden. 'Get' implies a read-only retrieval and 'full details' signals return completeness, but it does not disclose auth requirements, error behavior for invalid IDs, or rate limits. Adequate for a simple read tool but not rich.

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?

One sentence with zero waste, and the key information is front-loaded. 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 is simple with one fully documented input parameter and no output schema. 'Full details' gives a general sense of the return, but without an output schema the description could clarify what details are returned. It is sufficient to call correctly but leaves minor 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 single parameter has 100% schema description coverage, so the schema fully documents it. The description repeats 'by ID' but adds no extra meaning such as ID format or constraints; 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 names a specific verb (Get), resource (Datadog incident), and scope (full details by ID). It clearly differentiates from list_incidents by emphasizing a specific incident, though it does not explicitly name siblings.

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

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

Usage is implied: call this when you have an incident ID and need full details. There is no explicit when-to-use, when-not-to-use, or named alternative such as list_incidents, so the guidance is minimal.

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?

No annotations are supplied, so the description carries the full burden. 'Get' plus the field list makes the read-only, retrieval nature reasonably clear, but it says nothing about auth/permission requirements, error behavior for invalid IDs, or rate limits relevant to the Datadog API.

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?

A single front-loaded sentence with no filler; the key noun (monitor) and the lookup key (ID) plus the returned payload are all stated up front.

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 single-parameter read tool with no output schema, listing the returned fields (query, message, thresholds, options) usefully previews the response shape. Only the ID-acquisition path and error semantics are left uncovered, which are minor for this complexity level.

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?

Only one parameter with 100% schema description coverage, so the schema already documents monitorId. The description's 'by ID' adds no format, type, or range detail beyond the schema (e.g., whether IDs can be strings), so it does not compensate further.

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?

States a specific verb ('Get') and resource ('Datadog monitor'), scoped to a single monitor by ID, and enumerates the returned payload (query, message, thresholds, options). This implicitly contrasts with list_monitors and search_monitors, though no sibling is named explicitly.

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

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

The phrase 'by ID' implies usage when a monitor ID is already known, but there is no explicit when-to-use guidance, no when-not-to-use, and no pointer to list_monitors/search_monitors as the way to obtain an ID. Usage is implied rather than stated.

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. 'List' implies a read-only operation, but it says nothing about pagination behavior despite start/count parameters, nor about permissions or result limits. A read tool with pagination params that never mentions paging is a meaningful transparency gap.

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?

A single efficient sentence with the resource front-loaded and no filler. It earns its place, though it is arguably too terse given the pagination parameters left unaddressed.

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 a fully documented schema and no output schema, this is minimally adequate. However, the pagination contract (start/count defaults) and the distinction from get_dashboard are left entirely to inference, so it is not 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?

Schema description coverage is 100%, so all three parameters (count, query, start) are already documented in the schema. The description's 'optional search filtering' loosely echoes the query param but adds no format, syntax, or semantics beyond what the schema provides. 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?

States a specific verb+resource ('List Datadog dashboards') and adds scope via 'optional search filtering.' It is distinguishable from the get_dashboard sibling by virtue of the list verb, though it does not explicitly name or contrast with siblings.

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

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

No when-to-use guidance beyond the implied 'listing' semantics. It does not mention when to prefer get_dashboard for a single dashboard or how it relates to other list/search tools in the same namespace. The word 'optional' hints that filtering is not required but no conditions are given.

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

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only search but never states that, nor does it mention pagination behavior, result caps (limit max 1000), default time window, or rate limits — all relevant for an event query tool.

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?

A single tight sentence with no wasted words, front-loading the action and resource. It is arguably too terse for an 8-parameter tool, but nothing in it is 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 an 8-parameter query tool with no annotations and no output schema, the description is thin: it does not explain how query, tags, source, and priority interact, nor default behavior or pagination. The schema covers parameters, but the description leaves overall usage context incomplete.

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

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

Schema description coverage is 100%, so all eight parameters (from/to/tags/limit/query/cursor/source/priority) are documented in the schema. The description adds no syntax or interaction detail, so the 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?

States a specific verb and resource ('Search Datadog events') and clarifies scope with concrete examples (deploys, alerts, comments). It does not explicitly differentiate itself from siblings like search_logs or search_traces, but the resource is distinct enough to be 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 offers no when-to-use guidance, no prerequisites, and no mention of alternatives among the many siblings (search_logs, search_traces, list_metrics). 'With filtering' hints at use but leaves selection entirely to inference.

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?

No annotations are provided, so the description carries the full behavioral burden. It reveals what data is returned but says nothing about whether this is a read-only operation, default time window behavior, pagination limits (count max 1000), 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 a single, front-loaded sentence with no wasted words. Every clause ('Datadog infrastructure hosts' and the metric list) 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?

This is a read-only list tool with no annotations, no output schema, and six parameters. The description omits pagination, filtering, sorting, and the time window, all of which are needed to call 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 description coverage is 100%, so baseline is 3. The description mentions CPU, load, and IOWait, which correspond to the sortField enum but adds no details about the parameters themselves.

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

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

The description states a specific verb and resource: 'List Datadog infrastructure hosts' and enumerates the metrics returned (status, CPU, load, IOWait). It clearly distinguishes from most siblings, which operate on different resources, but does not explicitly contrast with get_host_totals, a closely related host 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?

There is no when-to-use or when-not-to-use guidance. The description does not mention pagination (count/start), the 'from' window, or filtering, nor does it point to get_host_totals for aggregate counts, leaving the agent to infer usage 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?

No annotations are provided, so the description carries the full burden. It implies a read operation but says nothing about pagination behavior, defaults, result limits, ordering, auth requirements, or rate limits — notable given the presence of pageSize/pageOffset parameters with defaults.

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?

A single front-loaded sentence with no filler or repetition. It is efficient, though arguably sparse enough that it leans toward under-specification rather than tight conciseness.

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

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

For a simple 3-parameter read tool with full schema coverage and no output schema, the description is minimally adequate. However, with zero annotations it should say more about pagination defaults and read-only semantics to be fully self-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 query, pageSize, and pageOffset are already documented in the schema. The description's phrase 'optional search filtering' loosely maps to query but adds no syntax, format, or behavior beyond what the schema states; 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?

States a specific verb and resource ('List Datadog incidents') plus the optional filtering capability, so an agent knows exactly what the tool returns. It doesn't explicitly distinguish itself from siblings like get_incident or search_monitors, but the list-vs-get contrast is inferable.

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?

There is no guidance on when to use this versus get_incident (single incident) or when the query filter is appropriate. The word 'optional' hints that unfiltered listing is valid, but no conditions, exclusions, or alternatives are named.

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

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only search but says nothing about result caps, pagination, or how many names are returned, which matters for a discovery tool that could return thousands of metrics.

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 short sentences, zero padding, with the core purpose front-loaded ahead of the workflow hint.

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 one-parameter read tool with a fully documented schema and no output schema, the description is minimally sufficient. It leaves the behavioral surface (result limits, whether matching is prefix/substring) entirely unspecified.

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 single 'query' parameter is documented with concrete examples ('system.cpu', 'aws.ec2'). The description adds no syntax or matching-behavior detail beyond the schema, so the 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?

States a specific verb (search) and resource (Datadog metric names), which clearly separates it from query_metrics and the monitor/host/dashboard siblings. It does not explicitly name a sibling, but the resource is 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?

Gives a clear usage context: discovery of metric names prior to querying. That is the correct mental model for this tool. It stops short of naming query_metrics explicitly or stating 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?

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about pagination behavior, result caps, rate limits, or what a listing returns. For a paginated list tool with zero annotation coverage, that is a real gap.

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?

A single front-loaded sentence that states the resource and its filter surface with no filler or redundancy. Nothing could be removed without losing information.

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

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

With six parameters, no output schema, and no annotations, the description covers the filtering surface but omits pagination semantics, defaults, and any hint of the response shape. It is minimally adequate but leaves the agent to consult the schema for the rest.

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 every parameter (including page and pageSize) is already documented in the schema. The description only restates the filter fields and adds no syntax or format detail beyond the schema, so the baseline 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 gives a specific verb and resource ('List Datadog monitors') plus the filterable fields, so the purpose is immediately clear. However, it does not distinguish itself from the sibling tools search_monitors or get_monitor, leaving the agent to infer the boundary.

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 that filtering is optional but gives no when-to-use guidance, no exclusions, and never mentions when to prefer search_monitors or get_monitor over this tool. The agent must guess the intended scenarios.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return shape (summary statistics per series: avg, min, max, latest), which is useful since there's no output schema. However, it omits safety profile (read-only by nature of 'query' but not stated), rate limits, or auth requirements.

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

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

Two concise sentences with zero waste, front-loading the purpose and then the return format. No unnecessary detail.

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

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

For a read-only query tool with full schema descriptions, the purpose and return shape are covered. But the absence of any usage context relative to siblings and lack of behavioral notes leaves it adequate but with clear gaps for a tool in a crowded namespace.

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 including examples. The description adds no extra parameter guidance beyond what's in the schema, making 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?

States a specific verb (Query) and resource (Datadog timeseries metrics), and the second sentence clarifies the output shape. Easily distinguished from siblings like list_metrics or search_traces.

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

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

No when-to-use guidance, no mention of alternatives despite many sibling tools, and no note that this is for numeric timeseries rather than metadata listing. The agent must infer usage entirely.

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?

No annotations are provided, so the description carries the full burden. It helpfully enumerates the returned fields (timestamp, service, status, host, message), which compensates for the absent output schema, but omits pagination behavior (cursor), result defaults, and any rate-limit or auth context.

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

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

Two sentences, front-loaded with the action and result set, with zero filler. Every clause carries 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 12-parameter read tool with no annotations and no output schema, the description covers purpose and return fields but leaves pagination, default time-window behavior, and query-vs-filter precedence unexplained. Adequate but with clear gaps.

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

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

Schema description coverage is 100%, so every parameter is already documented in the schema. The description adds no syntax or format details beyond that, so the baseline 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 names a specific verb (Search) and resource (Datadog logs) and states the two supported modes (structured filters or raw query). It clearly distinguishes from siblings like search_traces and search_monitors, though it does not explicitly name them.

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?

Usage is implied by the phrasing 'structured filters or raw query,' but there is no explicit when-to-use, when-not-to-use, or routing guidance versus sibling search tools. Notably, it does not clarify the precedence rule that a raw query overrides structured filters, which the schema alone mentions.

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 behavioral burden but only identifies the search scope. It omits read-only/non-destructive behavior, pagination behavior, auth/rate limits, and result format, so an agent gets little beyond the basic purpose.

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

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

Two short sentences, front-loaded with the core action and scope. No filler; every clause contributes.

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 3-parameter search tool with full schema coverage, the description covers the essential search behavior. However, with no annotations or output schema, it omits pagination/result-return context and sibling-routing guidance, leaving it minimally 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 page/perPage and query are documented. The description adds meaningful detail for the query parameter by naming the fields searched (name, query, tags, message), which is not in the schema, though it adds nothing for pagination.

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

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

The description states a specific verb (full-text search) and resource (Datadog monitors), and its scope (name, query, tags, message) distinguishes it from list_monitors/get_monitor. An agent can identify it as the text-search tool without opening 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?

No when-to-use guidance or alternatives are given. It does not say to prefer this over list_monitors for text queries or mention any prerequisites, leaving selection to inference.

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?

No annotations are provided, so the description carries the full behavioral burden. It discloses the return fields (service, resource, duration, status), but omits read-only nature, pagination, rate limits, and other side-effect information, leaving significant gaps.

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

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

Two sentences, front-loaded with purpose and return details, with no redundant or filler content. 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 10-parameter tool with no output schema and no annotations, the description provides a clear purpose and lists returned fields, while the schema fully documents all parameters. It lacks behavioral guidance and alternative tool selection, but is otherwise fairly complete.

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

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

Schema description coverage is 100%, so all 10 parameters are fully documented in the schema. The description only generically mentions 'structured filters or raw query' without adding parameter-specific meaning beyond what the schema already provides.

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

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

States a specific verb (Search) and resource (Datadog APM traces/spans), clearly distinguishing it from sibling tools like search_logs and query_metrics. It also mentions the two input modes (structured filters or raw query) and the returned fields.

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

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

Provides clear context that this tool is for searching APM traces/spans, with structured filters or raw query. However, it does not name alternative tools or specify when-not to use this tool, leaving sibling selection to inference.

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