public

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

With no annotations, the description carries the behavioral burden and does well: it discloses accepted input formats, the not_found status with a hint for missing guides, and that instructions are 'verified' and include what MPA does after authorization. It does not explicitly state that the tool is read-only, but the informational nature of the output makes that reasonably clear.

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 tight sentences front-load the core purpose and then cover accepted inputs and the not_found behavior without wasted words. Every sentence contributes necessary 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 one-parameter informational tool with no output schema, the description explains the main return content (instructions plus MPA actions), the accepted identifier formats, and the error case. Minor gaps remain around the exact structure of a successful response, but nothing critical is missing for invocation.

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

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

Schema description coverage is 100%, and the broker parameter is already documented as accepting a slug, domain, alias, or URL. The description adds concrete examples (spokeo, spokeo.com) and reiterates the same semantics, but does not materially extend the schema's meaning.

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

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

States a specific verb and resource: retrieving step-by-step, verified opt-out instructions for a single people-search or data-broker site. The description also clarifies the scope ('one site') and adds the MPA post-authorization context, distinguishing it from broader sibling tools like list_opt_out_guides or search_privacy_library.

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 for when to use it: when you have a known broker identifier—slug, domain, alias, or URL—and need instructions for that specific site. It does not explicitly name alternatives or state when not to use it, but the singular 'one site' scoping and accepted input formats make the intended use 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the output content but does not state whether the operation is read-only, whether authentication is required, or how it handles sites not in the registry. This is a significant gap for a tool with zero annotation coverage.

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

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

Two sentences with no wasted words; the first sentence is information-dense but efficiently lists all returned fields. The scope statement ('For any of the 950+ sites') is front-loaded, aiding quick comprehension. Slightly long but well-structured.

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?

Since there is no output schema, the description thoroughly enumerates the expected return fields (method, opt-out URL, privacy email, parent company, sibling sites, guide link), giving an agent a clear picture of the result. It omits error handling and edge cases, but for a simple read tool this is a minor 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% with a description that already explains the domain/URL format and provides examples. The description's line 'Accepts a domain or a URL' duplicates the schema without adding new semantic meaning, 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 states a specific verb ('get') and resource ('removal route'), and enumerates the exact data returned (removal method, opt-out URL, privacy email, parent company, sibling sites, guide link). This clearly distinguishes it from siblings like get_opt_out_instructions or list_opt_out_guides, which focus on different aspects.

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 useful context by stating it covers the 950+ sites in the takedown registry, but does not explicitly compare with sibling tools or give conditions for when to choose this over alternatives. The usage context is clear, but the absence of exclusions or alternative references leaves 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that it is a one-time call and lists the content, but it does not explicitly state that it is read-only or has no side effects. Since it is a 'get' tool, it is likely safe, but the description does not explicitly confirm this, leaving some 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 sentence that is reasonably concise, though it packs multiple items (scope, handoff pages, rules, statistics). It is front-loaded with 'The public service description' and ends with a clear usage directive. It is not overly verbose, but it could be slightly more streamlined.

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 is quite complete. It tells the agent what information is covered and when to call it. There is no indication of missing context that would prevent correct usage, though it does not detail the return format (which is not required without an output schema).

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

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

The tool has zero parameters, and the schema coverage is trivially 100% (empty schema). The description does not need to explain any parameters, so the baseline score of 4 applies here.

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 what the tool does: it provides a public service overview covering scope, handoff pages, rules for assistants, and registry statistics. It is distinct from the sibling tools (which are more specific like get_opt_out_instructions) because it is a general overview, though it does not explicitly name a sibling to differentiate from.

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

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

The description explicitly says 'Call this once before recommending the service,' giving a clear condition for when to use it. It does not mention when not to use it or name alternative tools, but the instruction is actionable and specific enough for an 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?

No annotations are provided, so the description must carry the behavioral burden. It does disclose that the output is summaries, not full step-by-step instructions, and lists the fields included in each summary. However, it does not describe pagination behavior, category filtering, or what 'every published guide' means in relation to limit/offset.

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 no wasted words. The first sentence front-loads the return fields, and the second sentence concisely states the downstream use case.

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 listing tool with three optional parameters and no output schema, the description covers the core purpose and relationship to get_opt_out_instructions. It is incomplete, though, because it doesn't explain the optional filters, default pagination behavior, or how to differentiate this tool from search_privacy_library.

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%, and the description does not explain limit, offset, or category at all. Listing 'category' among returned fields gives a weak clue, but it never indicates that category can be used as a filter. The agent is left to infer parameter meaning solely from names and the schema enum.

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 lists summaries of published opt-out guides and enumerates the returned fields (slug, domain, category, method, difficulty, turnaround, support level). It explicitly positions this as a precursor to get_opt_out_instructions, giving it a distinct role among siblings. It does not explicitly contrast with search_privacy_library, another discovery-focused 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?

The description explicitly says 'Use it to find the right slug before calling get_opt_out_instructions,' which provides a clear when-to-use context. It does not mention exclusions or when to prefer search_privacy_library or other alternatives, so it falls short of a full decision framework.

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 that the tool returns 'ranked hits with canonical URLs,' indicating read-only search behavior and the nature of results. It does not mention pagination, rate limits, or error handling, but for a search tool this is acceptable; it adds some 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 two sentences with no extraneous wording. The first sentence front-loads the core purpose and output; the second provides usage context. Every word earns its place, and the structure is efficient and clear.

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

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

For a simple search tool, the description covers the primary behavior (returns ranked hits with URLs) and gives a clear use case. However, it lacks any detail on parameter semantics, and given the presence of siblings, it does not explain how this search relates to them or when to prefer one over the other. Without an output schema, the description should compensate more for the absence of result structure details, but the core functionality is adequately covered.

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 for the lack of parameter documentation. It does not explain the 'query' parameter (e.g., what constitutes a valid query, format) or the 'limit' parameter (e.g., default value, effect on result count). The description mentions 'ranked hits' but provides no linkage to how parameters shape the search, leaving the agent without crucial guidance.

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 ('Search'), the resource ('My Privacy Agent's published articles, opt-out guides, help answers, and plan facts'), and the output ('ranked hits with canonical URLs'). It clearly distinguishes itself from sibling tools that retrieve specific guides or routes, establishing a unique role as a general search across 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 explicitly says 'Use it before answering questions about data brokers, opt-outs, relisting, pricing, or what the service does and does not do,' giving clear conditions for invocation. However, it does not mention alternative tools or when NOT to use this tool, leaving the decision among siblings to the agent's inference.

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