Meridian Trace — Medical Device Registrations

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 adds important context: the tool reflects observed practice rather than regulatory determination, risk class is not portable across markets, and sample sizes are included. This goes well beyond the schema, though it does not address auth, rate limits, or error behavior.

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

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

The description is moderately sized and front-loaded with the core purpose. The explanatory sentence about risk class non-portability is valuable context, and the closing caveat 'Observed practice, not a regulatory determination' earns its place. It is slightly verbose but every sentence contributes useful 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?

Given no output schema and no annotations, the description does a strong job of explaining what the tool returns, what inputs it accepts, and how to interpret the risk-class data. It could be more complete by describing the response format or pagination, but for a classification lookup tool with three optional parameters, it covers the essential 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?

Schema coverage is 100% with each parameter described, so the baseline is 3. The description adds meaningful semantics by clarifying that the three parameters are alternative lookup modes, that GMDN term matching is partial, and that any one of the three can be used. This exceeds pure schema repetition, though it does not specify precedence if multiple parameters are supplied.

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 tool as a classification view of a device type, specifying the exact outputs: GMDN hierarchy, FDA product code mappings with device counts, and market-by-market risk class with sample sizes. This distinguishes it from siblings like find_similar_devices and get_market_coverage by emphasizing the unique 'not published anywhere' risk-class data.

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

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

The description gives clear context for when to use the tool: when you need classification, FDA mappings, or cross-market risk class for a device type. It also specifies accepted inputs (GMDN code, partial GMDN term, or FDA product code). However, it does not explicitly name alternatives or state when not to use it, so it stops short of 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 transparently discloses the data source (predicates cited in each clearance's own summary), the time/number range (64,567 clearances, 1992-2026), and the exact output for both k_number (forward and reverse citations) and product_code (ranked predicates and recent clearances). It does not mention pagination or error behavior, but the data source and direction of results are well disclosed, adding significant context beyond what a generic query tool would provide.

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

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

The description is information-dense yet efficiently structured. It opens with a clear one-line definition, then elaborates on the two query modes in separate clauses, and closes with use cases. Every sentence adds value, and there is no fluff or repetition. The structure is easy to scan and parse.

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 adequately explains what the tool returns for each parameter mode: k_number yields cited predicates and reverse citations, while product_code yields ranked predicates and recent clearances. It also mentions the limit parameter controls row count. Given the tool's complexity and the absence of an output schema, this description is comprehensive enough for the agent to know what to expect and how to use the tool effectively.

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?

Although schema coverage is 100%, the description adds substantial semantic meaning to each parameter. For k_number, it explains both forward ('what that device cited') and reverse ('which later devices cited IT') lineage. For product_code, it describes the output as 'predicates that code leans on most, ranked by how often they are cited, plus recent clearances.' This goes far beyond the schema's simple labels and gives the agent a clear understanding of what each parameter does and what results to expect.

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: 'US 510(k) predicate lineage' with specific details about the data source (actual citations from summaries) and the two query modes (by k_number and by product_code). It explicitly distinguishes from similarity-based tools by noting it's 'not a similarity guess' and describes the reverse direction of citations, which clearly sets it apart from sibling tools like find_similar_devices.

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

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

The description gives explicit use cases: 'Use when choosing a predicate, assessing substantial equivalence, or mapping who is clearing devices in a classification.' It implicitly contrasts with similarity tools by stating it's based on actual citations, but does not name specific alternatives or state when not to use it. This is clear guidance but could be stronger with explicit 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 provided, so the description carries the full burden. It discloses key behaviors: matching on GMDN, excluding the same manufacturer, spreading results across markets, and each result carrying a match band. It does not mention rate limits or auth, but given the tool's nature, these disclosures are quite informative for agent selection.

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

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

The description is compact and front-loaded. Three sentences cover purpose, exclusions, use cases, and prerequisite. 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 tool is moderately complex with 4 optional parameters and no output schema. The description explains the matching methodology, what results to expect (competitive set, match band), and typical use cases. It could mention edge cases (e.g., no results if no matching GMDN), but the core behavior is well 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 coverage is 100% with parameter descriptions, so baseline is 3. The description adds notable value by explaining the relationship between device_name and registration_id (device_name is an alternative that finds the closest registration), and clarifies that market is used to restrict the reference lookup. This goes beyond the schema's brief 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 clearly states the tool's purpose: finding competing and comparable devices for a registration, matched on GMDN type rather than product-name text. It distinguishes itself from siblings by specifying the matching basis (GMDN) and the competitive set nature (excludes same manufacturer, spreads across markets).

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 guides when to use this tool: for competitive landscape, classification precedent in other markets, and understanding how a device type is described elsewhere. It also contrasts with alternatives by noting it matches on GMDN rather than product-name text, and mentions getting a registration_id from get_registrations.

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 describes the output fields in detail (source registry, market, registrations count, last crawl time), implying a read-only operation. While it doesn't explicitly state side effects or authentication, the nature of the query (verification) makes it clear that it is a safe read operation.

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, concise and directly to the point. It efficiently conveys the purpose, content, and usage without unnecessary elaboration. The structure is clear and well-organized.

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 absence of an output schema, the description adequately explains what the tool returns (source registry, market, registration count, last crawl time) and when to use it. This provides sufficient context for a user to understand the tool's functionality and output.

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 is empty, but the description explicitly states 'No arguments.' This eliminates any ambiguity about required inputs and adds clarity beyond the schema, which provides no parameter information.

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 retrieves coverage information (source registry, market, registration count, last crawl time). It distinctly differentiates from sibling tools like classify_device or search_manufacturer by focusing on coverage and freshness.

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

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

Explicitly instructs when to use the tool: 'verify coverage and freshness for yourself before relying on other tools' and answers coverage-related questions. This provides clear guidance on appropriate usage scenarios and contrasts with other tools that may assume coverage.

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 important behavioral context about regional data semantics (local party vs. OEM) and the insights derivable from the output, but it does not address response shape, error behavior, pagination, or any access prerequisites. Given the simple read-only nature of the tool, this is adequate but not comprehensive.

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 definition of the output and followed by a purposeful explanation of regional importance and use cases. Every clause adds value—no filler or redundant repetition of the tool 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?

Given the tool has one parameter, no output schema, and no annotations, the description provides adequate context for selecting the tool but not full detail on the return structure. It explains the type of entities returned and the geographic nuance, but an agent invoking it would not know the exact fields per license holder. The description is sufficient for selection, only partially 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?

The schema provides no description for manufacturer_id (0% coverage), so the description must compensate. The description indirectly defines the parameter by referring to 'a foreign manufacturer's registrations', implying manufacturer_id identifies that manufacturer. However, it does not specify the ID format, source of the ID, or any constraints beyond the schema's required flag, which is only a partial compensation.

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 tool's output—local entities (importers, distributors, regulatory consultants) that hold a foreign manufacturer's registrations. It explicitly distinguishes this tool from siblings by explaining that in Malaysia, Indonesia and Thailand the registry names local parties rather than the OEM, and positions this as 'the only way' to see market access control and partnerships.

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 states when to use the tool: when you need to know who controls market access, who a competitor is partnered with, or whether a manufacturer uses one or many local partners. It implies that other registration tools return OEM-level data and therefore should not be used for this local-entity insight, but it does not explicitly name alternative tools or provide exclusion criteria.

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 transparency. It discloses the return structure (per-market counts, source registries, absent markets) and explains how name/ticker resolve (same as search_manufacturer, best match). However, it does not explicitly state that the tool is read-only or mention any data freshness or access limitations, but for a query tool this is largely acceptable. The description is transparent about what the user gets and the resolution behavior, so a 4 is appropriate.

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 well-structured, leading with a punchy 'THE cross-market question' then detailing outputs and an example, and ending with usage guidance. While it is slightly verbose (the example parenthetical is illustrative but not necessary), each sentence contributes meaning. It is front-loaded and easy to scan, warranting a 4.

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 there is no output schema, the description adequately explains what the tool returns: per-market registration and active-registration counts, source registries, and absent markets. It also provides context on the identification parameters (name, ticker, manufacturer_id) and their resolution behavior. For a read-only query tool with three parameters, this is sufficiently complete, though it could mention if any authentication or data-freshness caveats exist, which would push it to 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?

Although schema coverage is 100% (every parameter has a description), the tool description adds value beyond the schema by explaining the resolution behavior for 'name' and 'ticker' (same as search_manufacturer, best match) and noting ticker coverage limitations (only US-listed registrants, many private companies not reachable). This clarifies usage nuances that the schema alone does not convey, earning 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 purpose: to answer the cross-market question by listing both registered and unregistered markets. It uses a specific verb phrase ('every market where this manufacturer IS registered and every market where they are NOT') and explicitly distinguishes itself from get_registrations by focusing on market presence rather than individual 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 explicitly says 'Prefer this over get_registrations when the question is about market presence rather than individual products.' This provides clear guidance on when to use this tool versus an alternative, and also hints at the context (market-access teams looking for gaps). It effectively sets expectations for usage.

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

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

Since no annotations are provided, the description carries full responsibility. It discloses ordering (by first-seen date), exclusions (US PMA supplements), and performance characteristics (filtering speed), which are significant behavioral details that inform the caller's expectations.

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 four sentences, front-loaded with the main purpose and filters, then logically adding ordering, exclusions, and performance notes. It uses em dashes effectively and contains no fluff, making it concise and 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?

Given the absence of an output schema, the description adequately covers the essential aspects: purpose, filters, ordering, exclusions, and performance. It does not mention pagination or error scenarios, but these are not critical given the tool's simplicity and the presence of parameter constraints in the 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 schema descriptions cover 100% of parameters, providing a baseline of 3. The description adds context by clarifying that market, risk class, and device type are optional filters, and highlights the performance impact of filtering by market, which slightly enhances understanding beyond the schema alone.

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: retrieving recently added registrations in Meridian, with optional filters. It distinguishes itself as a competitor-monitoring feed, explicitly excluding US PMA supplements and ordering by first-seen date, which sets it apart from other listing tools.

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

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

The description implies usage for competitor monitoring and notes performance differences when filtering by market, giving practical guidance. However, it does not explicitly contrast with sibling tools like search_manufacturer or get_registrations, so the 'when-to-use' aspect is partially 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 full burden and does well by explaining the provenance block, how derived fields are flagged, and that unlisted fields are verbatim from the registry. This is valuable behavioral context beyond the schema. It does not mention rate limits or side effects, but the read-only nature is implied and provenance disclosure is a strong transparency point.

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 paragraph of two sentences, but the second sentence is long and dense, explaining provenance. It front-loads the core purpose and filtering capabilities while keeping the provenance explanation compact. No redundancy or fluff; the structure logically flows from what it returns to how to interpret the data.

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 no output schema, the description compensates by explaining the return content (fields and provenance block) and the meaning of derived vs. verbatim fields. It does not describe pagination defaults (though those are in the schema) or error handling, but for a read-only listing tool with filtering, the description covers the essential behavior. The mention of provenance is particularly useful for interpreting results.

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 80% of parameters with descriptions (page, limit, market, status); manufacturer_id lacks a description but is self-evident. The description does not add new parameter semantics beyond what the schema provides—it merely mentions filtering by market and status, which is already documented. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool lists individual registrations held by a manufacturer, enumerating fields like product name, registration number, status, dates, risk class, and source registry. It differentiates from get_market_coverage by scope, explicitly stating when to use that alternative. The verb 'get' plus resource (registrations) makes the purpose unmistakable.

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

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

The description provides explicit guidance on when to use this tool vs. get_market_coverage: 'Use get_market_coverage instead when the question is which markets a company is in.' It also implies usage for listing registrations with filtering by market and status, giving clear context for when this tool is appropriate.

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 and does well by explaining the data provenance (registry approval dates), the historyQuality flag with detailed meanings, and the caveat that current_state_only markets undercount withdrawn products. This goes beyond a simple 'get' and informs the agent about data quality and interpretation.

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 and packs a lot of information: the metric, data provenance, data-quality caveats, and use cases. Each sentence contributes meaning without fluff, though the density may require careful reading. It is concise but not minimal.

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 6-parameter tool with no output schema, the description covers the core concept, historical depth, the historyQuality flag, and intended analytical use cases. It does not detail the return format or multi-competitor comparison mechanics, but it provides enough context for an agent to understand what the tool offers and its limitations.

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?

All 6 parameters already have schema descriptions, so the baseline is 3. The description does not add parameter-specific guidance, such as the relationship between name, ticker, and manufacturer_id, but it does add context about the output's historyQuality flag, which relates to how the data is generated.

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's output as new registrations per year by market, and distinguishes it from siblings by noting it is built from registry approval dates and reaches back as far as 56 years. Although it lacks an explicit verb like 'returns', the noun phrase is specific and unambiguous about the resource and metric.

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 explicitly states 'Use for entry velocity, launch cadence, and comparing two competitors' expansion over time,' which gives clear context for when to select this tool. It does not name alternative tools or exclusions, but the use cases are sufficient for an agent to differentiate it from a generic listings 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. It discloses key behaviors: name unification across scripts, subsidiary resolution, and returning up to 5 candidates with confidence and market footprint. However, it does not explicitly state that the tool is read-only or mention any authorization requirements, though as a search tool this is implied. The description adds substantial behavioral context beyond what the name alone suggests.

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, and every sentence adds value: the first defines what it does and its primacy, the second explains unique behavior and output format. There is no fluff or repetition, making it highly concise and 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?

For a tool with 2 parameters and no output schema, the description covers the essential context: it explains the core function, the normalization behavior, the return format (up to 5 candidates with confidence and footprint), and contrasts with a generic web search. This is sufficient for an agent to understand when and how to use it, especially given the high schema coverage.

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 covers both parameters (name, country) with clear descriptions, yielding 100% coverage. The description adds meaning to the 'name' parameter by explaining that it supports spelling variants and scripts, which is not in the schema. It does not add detail to 'country', but the schema already explains its usage. This goes beyond the baseline 3 for high schema coverage.

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

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

The description clearly states the tool resolves a company name to a Meridian manufacturer entity and positions it as the entry point for all other tools, distinguishing it from siblings like classify_device or get_registrations that focus on other aspects. The verb 'resolve' plus the resource (manufacturer entity) makes the purpose specific and 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?

It explicitly says it is 'the entry point for every other tool,' signaling when to use it first. It also contrasts with a plain web search by explaining that a web search cannot unify names across scripts, providing a clear when-not scenario and rationale for using this tool instead of generic search.

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