atlas-fda

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context beyond that: the data source is FDA's official list, it contains 1,500+ authorizations, it is tracked weekly by Atlas, and it is a joined queryable dataset rather than the raw openFDA feed.

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

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

Three sentences with no filler. The description front-loads the authoritative source and scope, lists concrete operations, and closes with the dataset's value proposition. Every sentence contributes distinct 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 read-only query tool with four optional parameters and no output schema, the description covers the source, the update cadence, and all main query modes. The only minor omission is the shape of the returned records, but the absence of an output schema and the clear list-oriented purpose keep this gap small.

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 four parameters. The description's mention of filtering by panel/year and checking a submission number mirrors the schema rather than adding new semantic detail, which lands at the baseline 3.

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/resource combo: list FDA AI-enabled medical device authorizations, with explicit operations like filtering by panel/year or checking a submission number. It also distinguishes itself from openFDA by noting this is the joined, queryable version.

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 tool's intended use cases are clearly enumerated: list recent authorizations, filter by panel or year, or check a submission number. It notes openFDA lacks an AI field, implying this tool is the right choice for AI-specific queries, though it does not explicitly name sibling tools or exclusion 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?

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the output includes review times and recall indicators, and that results are 'recent clearances'—but it does not explain what 'recent' means, whether recalls are simply flags or include details, or how ordering works. With annotations carrying the read-only signal, a 3 is fair.

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 crisp sentences with zero filler. The main purpose is front-loaded, and the supporting detail (review times, recall indicators, starting-set framing) is delivered in the second sentence. 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 list/read tool with two parameters, full schema coverage, and read-only annotations, the description is nearly complete. It could add expected result behavior (e.g., ordering, default pagination, what 'recent' means), but these are minor gaps given the tool's low complexity and the annotations already cover safety. The output schema is absent, but the description's mention of review times and recall indicators partially compensates.

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 both parameters. The description adds the semantic context that product_code is the seed for the predicate candidate search and that results are recent clearances, but it doesn't add anything beyond the schema for limit. Baseline 3 is appropriate because the schema does the heavy lifting.

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

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

The description opens with a specific verb ('List') and resource ('predicate device candidates for a 510(k) submission') and further narrows by mechanism ('recent clearances under an FDA product code, with review times and recall indicators'). It also distinguishes the tool as 'the starting set for predicate research,' which differentiates it from siblings like get_clearance or search_records. The title is also helpful.

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 clearly conveys when to use the tool: when beginning predicate research for a 510(k), using an FDA product code. It does not explicitly name sibling alternatives (e.g., get_clearance for a specific clearance, search_recalls for recall details, search_records for broader searches), so a direct when-not-to-use statement is absent. Still, the framing 'starting set for predicate research' gives a clear context and implies this is a scoping/candidate-listing first step.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that this is a monitoring feed returning new items, but does not disclose details like ordering, inclusivity of the date, or default behavior when kind is omitted.

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 short sentences with no filler. It front-loads the core behavior and then gives a direct usage cue, making it easy for an agent to quickly evaluate.

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 monitoring feed, the description plus schema is mostly sufficient for an agent to select and invoke the tool. The lack of an output schema is partially compensated by the clear 'new clearances and recalls' framing, though a bit more detail on default behavior would round it out.

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 50%: 'since' has an ISO date description, while 'kind' only has an enum. The description mentions both clearances and recalls, aligning with the enum, but it does not explain what happens when kind is omitted or how 'since' is interpreted regarding inclusivity.

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 resource ('FDA clearances and recalls') and a clear temporal scope ('since a date'), and labels the tool as 'the monitoring feed.' This distinguishes it from sibling tools like get_clearance and search_recalls, which are for individual or search-based lookups.

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 states when to use the tool: 'Use for "what changed this week" questions.' It does not list exclusions or compare against alternatives, but the intended context is clear.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it returns full detail and includes recalls that reference the clearance, which is meaningful for an agent deciding between this and search_recalls.

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 sentence that front-loads the primary purpose and then adds the recall-related behavior. Every word earns its place, with no padding or 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?

For a one-parameter, read-only lookup, the description covers the main behavior and return scope well. It does not specify behavior for an unknown K-number, but given the simple fetch-by-ID nature and existing annotations, this is a minor gap rather than a blocking omission.

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 schema already identifies k_number as the required identifier with an example. The tool description merely repeats 'by K-number' without adding new format, validation, or usage semantics, 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?

The description clearly states a specific verb/resource pair: retrieving full detail for one FDA 510(k) clearance by K-number. It also adds a distinguishing scope detail—including related recalls—so it is identifiable against sibling search and stats 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 gives clear context: use this when you need complete detail for a single clearance identified by K-number. It does not explicitly name alternatives or exclusion cases, but the contrast with search-oriented siblings is reasonably clear from 'full detail for one'.

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?

Annotations already convey read-only and non-destructive behavior. The description adds useful return-value context (specific metrics), but does not disclose data scope, time period, formatting, or any rate/access limitations. It does not contradict the 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?

A single sentence with no filler. It front-loads the tool's purpose and immediately states the key output metrics. Every word contributes 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 one-parameter read-only tool, the description identifies the required input and the key return values. It lacks deeper context such as data source or valid code enumeration, but overall it gives enough for correct 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?

With schema description coverage at 0%, the description must define the parameter. It only restates 'product code' without explaining what constitutes a valid code, its format, or how to discover it. The description offers little beyond the parameter name itself.

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 resource (product code) and the exact statistics returned (median and 90th percentile days to decision, clearance count, recall count). It lacks an explicit verb but is unambiguous, though it does not differentiate from siblings like get_clearance or search_recalls.

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 no explicit guidance on when to use this tool versus alternatives. Siblings such as get_clearance and search_recalls likely overlap in clearance and recall data, but the description does not mention exclusions or selection 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?

Annotations already show this is read-only and non-destructive. The description adds useful context by clarifying that results include recall details with links and that searches can be done by product code, firm, or free text. It does not describe pagination or behavior when no filters are supplied, but the annotations lower the bar.

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 filler. The main action and search dimensions come first, and the use case follows naturally.

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 with no required parameters, the description is mostly complete: it covers search fields, return behavior, and use case. A small gap is that it does not state what happens when no search filters are provided, and 'recall details with links' is somewhat vague as an output description.

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 four parameters are already documented in the input schema. The description's mention of product code, recalling firm, and free text largely mirrors the schema rather than adding new semantic detail.

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 operation, 'Search FDA device recalls', and lists the exact search dimensions: product code, recalling firm, and free text. It also communicates the intended use case, making it easy to tell apart from sibling tools like find_predicates and search_records.

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 use cases: vetting a predicate candidate or monitoring a market. It does not name sibling alternatives or give when-not-to-use guidance, so it stops short of full usage routing.

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?

Annotations already establish that this is read-only and non-destructive, so the description adds useful context beyond them: the 777k+ corpus size, full-text matching, and 'ranked matches with links.' It does not mention pagination or limits, but that is a minor gap at this complexity level.

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 tightly worded sentences front-load the search scope and then state the return behavior. There is no filler, no repetition of annotations, and every phrase 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 two-parameter, fully documented, read-only tool, the description is nearly complete: it gives the corpus, the match semantics, and the result format ('ranked matches with links'). The only notable omission is explicit sibling-tool routing, but the agent has enough information to call this 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 coverage is 100%; both query and kind already have meaningful descriptions, and kind has an explicit enum. The tool description reinforces that this is full-text search but does not add parameter-level meaning beyond what the schema 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?

The description opens with a specific verb and resource: 'Full-text search across 777k+ FDA 510(k) clearances, device recalls, and registered clinical trials.' It also states the output shape. It does not explicitly contrast itself with sibling tools like search_recalls, so differentiation is left to inference.

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 corpus scope and full-text search behavior imply when this tool is useful, but there is no explicit when-to-use or exclusionary guidance. The agent is not told, for example, to prefer search_recalls when only recalls are needed or find_predicates for predicate lookups.

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