Drone Intelligence

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

No annotations are provided, so the description carries the burden. It indicates the operation returns a comparison (a read-like behavior) and specifies the input format (slug). However, it does not disclose any side effects, output structure, or data source details. Since this is a non-mutating retrieval tool, the minimal transparency is acceptable but not exceptional.

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

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

The description is a single, concise sentence that front-loads the tool's purpose and includes an example. No unnecessary words or overlapping with schema details.

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

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

This is a simple tool with one parameter and no output schema. The description adequately communicates the tool's core function and input format. It does not explain what the comparison includes, but for a straightforward retrieval of a comparison by slug, it is sufficiently complete for an AI agent to select and invoke the tool.

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

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

The schema already documents the 'slug' parameter with a clear description and an example. The tool description also provides an example (droneshield-vs-dedrone), adding marginal value but not going beyond the schema's coverage. With 100% schema description coverage, a 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 states the tool performs a side-by-side company comparison, with the resource being companies and the mechanism being a slug. The title 'Get a company comparison' and the tool name 'compare_companies' reinforce the purpose. It clearly distinguishes from siblings like get_company_profile or get_briefing, which are not comparison-focused.

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 the tool is used when a side-by-side company comparison is needed, and the slug example shows how to specify the comparison pair. However, it does not explicitly state when to use this tool instead of alternatives, nor does it mention any exclusions or prerequisites.

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 the tool returns the full text and lists the sections, which is useful. However, it does not mention error behavior, authentication requirements, or potential limitations (e.g., only for Drone Intelligence Signal Dossiers).

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 efficiently communicates the tool's function and key output components without any waste. Every word adds value.

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

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

For a simple retrieval tool with one parameter and no output schema, the description provides an adequate outline of what is returned (the sections). It lacks notes on errors or return format, but given the simplicity, this is acceptable.

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 schema already describes the slug parameter with an example. The description adds context by indicating the slug identifies a briefing and that the returned content includes specific sections, which helps an agent understand the parameter's role beyond the schema.

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

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

The description clearly states that the tool returns the full text of a Drone Intelligence Signal Dossier briefing, identified by slug, and lists the sections included (executive summary, signals, assessment, sources). This specific verb+resource+scope distinguishes it from siblings like get_intelligence_page or get_company_profile.

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 you need the full briefing content by slug. It does not explicitly state when not to use it or mention alternatives, but the specificity of the purpose provides clear context for an AI agent.

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 full responsibility for disclosing behavior. It lists the profile contents but does not explicitly state that this is a read-only operation, nor does it describe error behavior or response format. This is insufficient for a tool without annotations.

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

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

The description is a single, concise sentence that front-loads the core information and avoids redundancy. It efficiently communicates the tool's purpose and content without unnecessary 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 tool is simple (one required parameter, no output schema), and the description lists the key output components (positioning, facts, funding, contracts, products). However, it omits details about response format, error cases, or usage context, leaving some gaps for an agent to infer.

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 fully describes the slug parameter with an example (droneshield). The description merely says 'by slug', which adds no new semantic information beyond the schema. Since schema coverage is 100%, a 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 defines the tool as returning a company profile with specific content categories (positioning, sourced facts, funding, contracts, products). It identifies the input as a slug and scopes it to Drone Intelligence, which helps distinguish it from broader tools like get_intelligence_page or get_products.

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 guidance on when to use this tool versus siblings such as compare_companies or get_briefing. It only mentions the slug parameter, which is a requirement, not a usage scenario, and no alternatives or exclusions are provided.

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 full responsibility for behavioral disclosure. It lists the content sections, giving some idea of the return structure, but does not explicitly state that the operation is read-only, discuss error handling, authentication, or any side effects. The 'get' verb in the title implies a safe read, but the description itself is minimal.

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, compact sentence that front-loads the resource type and then lists the included sections. Every word adds 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?

The tool has only one parameter and no output schema, so the description is relatively complete for a simple retrieve-by-slug operation. However, with many sibling tools, it lacks differentiation guidance and does not explain the broader context of market intelligence pages or when the returned sections would be useful. It is adequate but not enriched.

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 100% coverage for the single 'slug' parameter with a description and example. The description merely reiterates 'by slug' without adding syntax, format, or additional constraints, so it does not improve on 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 identifies the resource as 'a Drone Intelligence market intelligence page' retrieved 'by slug' and enumerates its contents (overview, sections, key players, outlook, sources). This clearly distinguishes it from sibling tools like get_company_profile or get_briefing, though the description itself is a noun phrase rather than a explicit verb phrase.

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 no guidance on when to use this tool versus alternatives like get_briefing or get_company_profile. No exclusions, prerequisites, or context are mentioned, leaving the agent to infer usage solely from the tool's name and sibling list.

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 of behavioral disclosure. It indicates that the output is 'machine-readable' and includes specific data fields, which is useful context. However, it does not disclose any side effects, permissions, or limitations, leaving some behavioral ambiguity.

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

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

The description is a single, concise sentence that front-loads the key information (commercial products, machine-readable, specific data fields). There is no unnecessary verbosity, and it is appropriately sized for the tool's simplicity.

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 no parameters and no output schema, the description provides sufficient context about the returned content (prices, checkout URLs, licence terms). It could be more explicit about the action (e.g., 'returns' or 'retrieves') and output format, but it is reasonably complete for a simple read-only tool.

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

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

The input schema has zero parameters, so there are no parameter semantics to clarify. Per the rubric, a baseline of 4 is appropriate when there are no parameters, and the description does not need to compensate for missing schema details.

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

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

The description clearly identifies the resource (commercial products) and the scope of returned data (prices, checkout URLs, licence terms). The verb 'get' is implied by the tool name rather than stated explicitly, and it does not distinguish itself from the sibling tool 'list_catalogue', but the scope is specific enough to be useful.

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 (retrieving product information) but provides no explicit guidance on when to choose this tool over siblings like 'list_catalogue' or 'get_intelligence_page'. There is no mention of alternatives or exclusions, so it is minimally adequate.

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

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

With no annotations, the description carries the full burden. It discloses pagination (50 per page), filtering by type, and that the result is slugs. However, it does not describe the exact return shape (e.g., array of strings, object with metadata) or behavior for out-of-range pages, which is a notable 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, focused sentence that immediately conveys the core purpose, filter options, and pagination. Zero wasted words; the structure is clean and front-loaded.

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

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

For a straightforward list tool with two parameters and no output schema, the description covers the essential behavior but omits the return format and pagination semantics beyond page size. Given the sibling tools, it is enough to distinguish purposes but not fully self-contained.

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

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

Schema description coverage is 0%, so the description must compensate. It explicitly lists the enum values for type and adds the page size (50 per page), which is helpful. However, it does not name the 'page' parameter or explain its default/minimum, leaving some burden on 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 states it returns paginated slugs filtered by content type, which clearly distinguishes it from sibling content-fetching tools like get_briefing or get_products. It uses a noun phrase rather than an explicit verb, but the tool name 'list_catalogue' makes the action clear.

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

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

No explicit guidance on when to use this tool versus alternatives. It does not mention that it should be used to discover slugs before fetching specific content, nor does it exclude any use cases. The intended usage is only implied by the word 'slugs'.

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 transparency burden. It discloses the result limit (up to 10 matches) and the return fields (title, type, URL, excerpt), which is useful behavioral information. It does not state explicit read-only behavior, but the search action and return data imply it.

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 and scope, and includes essential return details without any filler. 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?

For a simple one-parameter search tool with no output schema, the description covers the content types searched and the result format, which is enough for an agent to select and invoke it. It omits advanced details like pagination or sorting, but those aren't critical for core use.

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

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

The input schema provides a full description for the only parameter 'query' (including an example), covering 100% of the semantics. The description does not add extra parameter guidance, 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 a specific action ('Search') and a specific resource ('Drone Intelligence briefings, market intelligence pages, company profiles and comparisons'), and distinguishes itself from sibling tools that retrieve individual items (e.g., get_briefing, get_company_profile) by emphasizing search across multiple content types.

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

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

The description implies when to use the tool (when you need to search across multiple content types), and its scope is clear. However, it does not explicitly mention alternatives or provide 'when not to use' guidance, though the sibling tool names suggest concrete use cases.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses a genuine prerequisite — a tracker licence token — which an agent cannot learn from the schema, but says nothing about whether zero-count groups are returned, pagination, or output shape.

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

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

Two sentences, zero waste, with the operation and grouping options front-loaded and the licence prerequisite trailing. Nothing to trim.

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 aggregate with no output schema and no annotations, the definition covers what the tool does and its auth requirement. It leaves open the return structure and grouping edge cases, which matters somewhat since no output schema exists to document them.

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

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

Schema description coverage is 0%, but the single parameter is a self-documenting enum of theatre/layer/awardee, and the description echoes those values. It adds no meaning beyond the schema — no guidance on which dimension to pick for which question.

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 — counts of tracker award lines — plus the grouping dimensions, which separates it from the raw listing sibling tracker_awards. It never names that sibling explicitly, so the differentiation is inferable rather than stated.

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 word 'counts ... grouped by' implies this is the aggregation path rather than the row-listing path, but no alternative (tracker_awards, compare_companies, tracker_vendor_matrix) is named and no when-not condition is given. Usage is left to inference from the phrasing.

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. It usefully discloses the dataset scope ('67 checked counter-UAS award lines') and the licence-token requirement, but it does not describe return format, pagination, or other operational behavior beyond what 'Query' implies.

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 short sentences, front-loaded with the resource and scope, followed by filters and prerequisite. Every sentence earns its place with no waste.

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

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

For a simple read/query tool with fully described optional parameters, the description covers purpose, scope, filters, and authentication. The main remaining gap is that no output schema exists and the description does not hint at the return shape or default behavior when no filters are supplied.

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 schema already documents all three parameters with examples. The description restates the filters and adds the term 'kill-chain layer' for the 'layer' parameter, but it does not add material syntax or behavior beyond the schema.

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

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

The description uses a specific verb ('Query') and resource ('67 checked counter-UAS award lines'), making the tool's purpose clear. It does not explicitly differentiate itself from siblings such as tracker_aggregate or tracker_vendor_matrix, so it falls short of a 5.

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 the tool can filter by theatre, kill-chain layer, or awardee substring and that a tracker licence token is required, which gives implied usage context. However, it does not say when to use this tool versus alternatives like tracker_aggregate or tracker_schema, and it provides no exclusions.

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

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

No annotations are present, so the description carries the full burden. It only discloses the token requirement, but fails to mention side effects, return format, or whether the operation is read-only. This is minimal and insufficient.

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 extremely concise, with two short sentences, no redundant phrasing, and the key information (what it is and the token requirement) 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?

With no output schema, the description should clarify what the tool returns, but it only describes the ledger's subject matter. It is adequate for a vaguely defined data source but leaves gaps about return type or content granularity.

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 tool has zero parameters, so the baseline is 4. There is nothing for the description to add beyond what is already vacuously covered by 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 identifies a specific resource—the capital and acquisition ledger for counter-UAS vendors—but lacks a clear verb indicating the action (e.g., 'lists', 'retrieves'). It adds context beyond the title but remains vague about what the tool actually does.

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 guidance is provided on when to use this tool versus sibling tools. The only usage-related note is the licensing requirement, which is a prerequisite rather than a context for 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?

No annotations are provided, so the description must disclose behavior. It mentions the licence token requirement, but it doesn't explicitly characterize the operation as read-only or describe the response format. The metadata framing implies no side effects.

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 concise sentence packs the tool's purpose and the key prerequisite without redundancy.

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

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

Despite having no output schema, the description lists the metadata components and the auth requirement, providing adequate expectation-setting for a simple schema retrieval tool. It could mention that the response is a schema object.

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 tool accepts no parameters, so the baseline is 4. The description adds no parameter-specific information, which is appropriate given the empty 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 specifically names the 'paid Counter-UAS Tracker dataset' and enumerates its contents ('fields, theatres, layers, counts, data-through date, licence'), clearly distinguishing it from sibling data-query tools like tracker_aggregate and tracker_awards.

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 understanding dataset metadata but doesn't explicitly state when to use it versus sibling tools. It provides a prerequisite ('Requires a tracker licence token') but no guidance on alternatives or conditions.

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

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

No annotations are provided, but the description discloses that a tracker licence token is required and describes the kind of data returned for each vendor. It does not cover pagination, result limits, or error behavior, but for a read-only query tool it adds meaningful behavioral context.

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

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

The description is two compact sentences with the resource and return content front-loaded, followed by filtering and licensing requirements. Every sentence adds useful information without repetition.

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

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

With no annotations and no output schema, the description carries the burden well by explaining the data returned and the licence prerequisite. It is nearly complete for this simple lookup tool, with only minor omissions around result limits or pagination.

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 both parameters are already well documented in the schema. The description restates the filtering dimensions (layer or vendor name) but does not add syntax or 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 gives a specific verb ("Query") and resource ("43-vendor counter-UAS matrix"), and clarifies the returned contents. It is clearly distinct from sibling tools by subject, though it does not explicitly name a sibling alternative.

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 explains the available filters and that a tracker licence token is required, which implies appropriate usage. However, it does not say when to use this instead of siblings such as tracker_aggregate or search_content.

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