NobodyNamed

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

With no annotations, the description carries the full burden of behavioral disclosure. It states that the output is full yearly series for each name, which implies a read-only operation, but it does not describe the output format, year range, or behavior for names with no data. This basic transparency is adequate but lacks depth.

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

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

The description is a single, front-loaded sentence that immediately states the purpose, then elaborates on the benefit. Every word earns its place; there is no fluff or repetition of 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?

The tool is simple (one parameter, no annotations, no output schema), and the description adequately explains the core behavior and intended use case. It mentions the output (full yearly series) and the purpose (plotting/contrasting). However, it does not detail the exact return structure, which would be expected without an output schema, so a slight deduction is made.

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 provides a clear description of the 'names' parameter, including min/max items and an example. The description adds context about the output (full yearly series) but does not add new meaning to the parameter itself. Since schema coverage is 100%, 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 clearly states the tool's function: side-by-side comparison of 2-3 names, with full yearly series for each. This distinguishes it from siblings like get_name_data (which likely handles a single name) and other name-specific tools. The verb 'comparison' and resource 'names' are explicit.

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 compare 2-3 names and plot or contrast their trends. It does not explicitly name alternatives or give exclusion criteria, but the context is clear and distinct from sibling tools that handle single names or different groupings.

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 for behavioral disclosure. It indicates a read operation via 'Returns' but does not disclose result size, ordering, gender filtering, or whether 'top' is a fixed number. This is minimal viable transparency for a simple read tool.

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

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

The description is a single sentence, front-loaded with the verb 'Returns', and contains no redundant words. Every element earns its place, 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?

The tool is simple with one well-defined parameter, but no output schema exists and there is no mention of return value details (e.g., fields, limits). Given the presence of many sibling tools, the description lacks sufficient context on when to use it versus alternatives, making it only adequately complete for basic 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%; the single parameter 'decade' is already well-described with an example ('1980s'). The description adds no extra parameter semantics, so baseline 3 is appropriate even though it does not repeat what the schema already provides.

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

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

The description uses a specific verb ('Returns') and clearly identifies the resource ('top baby names') and scope ('entire calendar decade'). This distinguishes it from sibling tools like get_year_names, which likely returns yearly data, making 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 implies usage for decade-level aggregation (e.g., 'aggregated across an entire calendar decade') but provides no explicit guidance on when to prefer this tool over alternatives like get_year_names or search_names. There are no exclusions or alternative tool mentions, leaving selection partially 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, the description carries the full burden of behavioral disclosure. It does provide useful detail about the response contents (timeseries, trend categories, peak stats), but it does not disclose behavior for unknown names, whether the timeseries is separated by sex, or any potential limitations or errors. This is acceptable for a read-only lookup but not rich.

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

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

The description is a single sentence that efficiently front-loads the main action and resource, then lists specific output attributes. There is no filler or redundancy, 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?

Given the absence of an output schema, the description does a solid job of enumerating return contents: yearly timeseries, trend classification, peak year, and peak count. It lacks edge-case details (e.g., name not found) and does not clarify whether the series is split by sex, but these are minor for a simple one-parameter 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 has one parameter with 100% coverage, including case-insensitivity. The tool description adds no additional parameter semantics beyond what the schema already states, 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 the tool returns a full yearly birth count timeseries (1880–2025) for a specified name, including trend classification, peak year, and peak count. This specific verb+resource combination distinguishes it from sibling tools like get_name_debuts or get_names_by_status.

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 for retrieving comprehensive historical data for a single name, providing clear context for when to use it. However, it does not explicitly mention alternative tools or conditions when this tool should not be used, so it falls 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 and does well: it discloses the tool's inclusion criteria (including names crossing the 5-birth threshold, not just true novelties) and emphasizes it returns 'every' name, implying a complete list. It does not mention output format or pagination, but for a read-only query tool, this is adequate.

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

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

The description is a single, front-loaded sentence with the core action first, followed by a clarifying list. While the list is useful for defining 'debut', it adds length; the text is not overly verbose but could be slightly tighter, earning a 4 rather than 5.

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 tool with no annotations or output schema, the description provides enough context: it states what it returns, the data source (SSA records), and the precise meaning of 'debut'. It could be improved by mentioning the return format or edge cases (e.g., empty results), but it is sufficiently complete for an 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 input schema already provides 100% coverage for the single parameter 'year' with a clear description. The tool description does not add additional semantics beyond restating 'given year', so the baseline of 3 is appropriate—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 clearly states the tool returns names first appearing in SSA records for a given year, using a specific verb ('Returns') and a precise resource ('every name that appeared in SSA records for the first time'). It also explains the scope with categories (novelties, celebrity imports, invented spellings, threshold-crossing), which differentiates it from sibling tools like get_year_names or get_name_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 provides clear context on when to use the tool (for finding debut names in a specific year) and implies that it is the right choice for this focused query. However, it does not explicitly mention alternatives or exclusions, such as telling the agent to use get_year_names for all names or search_names for general search, so it falls 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 provided, the description carries the full burden. It does disclose the nature of the output (geographic spread, state-level adoption), but it omits details such as time granularity, data structure, or potential limitations. This is adequate but not rich.

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

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

The description is a single, information-dense sentence that efficiently lists all key aspects (origin, peak, state adoption, never-adopted count). No filler 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 simple 2-parameter tool with no output schema, the description provides sufficient context for selection and basic understanding. It lacks precise return format or time units, but given the simplicity, it is mostly complete.

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

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

Schema description coverage is 100% (name and sex both have descriptions). The tool description adds no extra parameter meaning, so 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 the tool's function: 'Returns the geographic spread of a name over time' and enumerates specific outputs (origin, national peak, state adoption, never-adopted count). This distinguishes it from siblings like get_name_data or get_name_debuts, which likely serve different purposes.

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 a clear use case: any query requiring geographic/diaspora analysis of a name. It does not explicitly name alternatives or exclusions, but the focused scope provides enough context for an agent to select this tool over siblings.

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 notes the profile is 'precomputed', implying a fast read-only operation with no side effects. It also discloses that the data includes estimates (e.g., 'estimated living population') and cultural interpretation (e.g., 'events/media tied to spikes'), setting expectations about the nature of the data. It doesn't mention error handling or data freshness, but the core safe/read-only nature is well implied.

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, information-dense sentence that starts with the primary action and then lists the output components. While it is a bit long and could be structured as bullet points, every clause earns its place by describing a distinct part of the profile. It's concise relative to the amount of information conveyed.

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?

There is no output schema, so the description must convey what the tool returns. It lists six major data categories, giving a solid mental model of the response structure. It doesn't specify exact field names or types, but given the complexity of the response, the description provides sufficient context for an agent to understand the tool's purpose and output. It could mention the 'sex' parameter's effect on the profile, but the schema covers that.

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 baseline is 3. The description does not add additional meaning to the 'name' or 'sex' parameters beyond what the schema already provides. It focuses on the output rather than input semantics, so there is no added parameter insight.

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 returns a precomputed profile for a name and enumerates specific data components (estimated living population, median age, popularity wave shape, cultural catalysts, historical demographic profiles, regional anomalies). This is a specific verb+resource with detailed output scope that distinguishes it from sibling tools like get_name_data or get_year_names.

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 guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or that it should be preferred over get_name_data for enriched details. The sibling list exists but the description makes no reference to it, leaving the agent without decision 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, the description carries full responsibility for disclosing behavior. It states 'curated list' without explaining what curation entails (e.g., truncation, ordering, exclusion). There is no mention of limits, return format, or pagination, leaving significant 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 is front-loaded with the core action. It avoids wasted words, though it is somewhat under-specified; the brevity still works in its favor.

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 tool with high schema coverage, the description conveys the essential action and filter. However, the term 'curated' is vague, there is no output schema, and no alternative tool guidance is provided. Given the surrounding context of many sibling tools, more detail would improve completeness.

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 fully documents the sole parameter 'kind' with enum descriptions, achieving 100% coverage. The description adds no extra meaning beyond referencing 'trend status,' so it relies on the schema's thorough documentation. Baseline 3 is appropriate.

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

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

The description uses a specific verb ('returns') and identifies the resource ('list of names') along with the filtering dimension ('trend status'). It is clear, but it does not explicitly distinguish itself from sibling tools like get_name_data or get_decade_names.

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 given on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or which sibling tools might be more appropriate for other use cases. The usage context is only implied by the name and schema.

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

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

No annotations are provided, so the description carries the full burden. It discloses the algorithm (cosine similarity) and interpretation ('rose and fell together'), which is useful. However, it does not describe the return format, whether the original name is included, limits, or error handling for unknown names, leaving some behavioral gaps.

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

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

The description is a single sentence, front-loaded with the key action and outcome, and includes a helpful parenthetical clarification. Every word earns its place with no 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?

The tool is relatively simple with only two parameters and no output schema. The description explains the core functionality and algorithm, which is sufficient for an agent to invoke it, though a note about return values (e.g., list of names, perhaps with scores) would be even better.

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% since both 'name' and 'sex' have descriptions. The tool description adds context about the algorithm but does not add parameter-specific details beyond the schema. With full schema 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 uses a specific verb ('Finds the names') and clearly states the resource (names with similar popularity trajectories) and the method (cosine similarity on yearly series). This clearly distinguishes it from sibling tools like search_names or compare_names.

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 implies when to use: when you have a name and want to find names that rose and fell together. It does not explicitly name alternatives or exclusions, but the context is unambiguous, so it's clear without being explicit.

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 bears full responsibility for behavioral disclosure. It is transparent about the output content, but does not mention details like whether data is cached, if any rate limits apply, or performance characteristics. For a simple read-only metadata retrieval, this is adequate but not rich.

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

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

The description is a single sentence that fully lists the three return components without any filler or redundancy. Every word earns its place, 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?

Given the tool has zero parameters and no output schema, the description fully enumerates what the user receives (top-10 names, birth counts, year range). It is complete enough to set expectations correctly for this simple metadata endpoint.

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 description naturally does not need to explain parameter meaning. The baseline of 4 applies because there is no param information to convey beyond 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 clearly states the verb 'Returns' and lists three specific resources: top-10 names per year, total birth counts per year, and the full year range. This uniquely identifies the tool as a metadata overview, distinguishing it from sibling tools like get_year_names or search_names.

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

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

Usage is implied by the description—if an agent needs dataset-level metadata such as top names or birth counts, this is the tool to use. However, the description provides no explicit when-to-use/when-not-to-use guidance or alternatives compared to sibling tools, which makes it acceptable but not strongly guiding.

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 burden of disclosure. It transparently explains the scope (top 100 names of each sex) and the comparison nature (vs. prior year), and lists the output categories. However, it does not disclose edge-case behavior (e.g., what happens for the earliest year with no prior data), nor does it detail the output structure or whether it includes rank values. It is adequate but not rich.

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

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

The description is a single, information-dense sentence, front-loaded with the core concept and followed by a clear list of output categories. Every word earns its place; there is no fluff.

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 tool with one parameter, no output schema, and no annotations, the description provides a solid understanding of what is returned (gainers, losers, new entrants) and the scope (top 100 per sex). It does not mention whether results are ranked or how the two sexes are presented, but these are minor gaps for an agent to choose and invoke 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?

Schema coverage is 100%: the only parameter 'year' is described as 'Birth year to compare against the prior year.' The tool description echoes this by saying 'vs. the prior year,' adding no new semantic information beyond the schema. 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 what the tool does: it provides year-over-year rank changes for the top 100 names of each sex, including biggest gainers, losers, and new entrants. This is specific and distinguishes it from siblings like get_year_names (which likely lists names for a year) and compare_names (which likely compares specific names).

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: if the user needs year-over-year rank movements for the top names, this is the tool. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. Usage context is clear but not directly stated.

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

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

No annotations are provided, so the description carries the full transparency burden. It discloses the core behavior (returning top names for a year) but does not specify the return format (e.g., list length, ordering) or other traits like authentication or rate limits. The minimal detail is insufficient for a tool with no output 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 a single, front-loaded sentence that efficiently states the action and resource. It contains no superfluous information and reads clearly.

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 tool, the description covers the essential purpose, and the year range is already present in the schema. However, the lack of an output schema and any mention of sibling differentiation means it is not fully complete, though it remains adequate for the tool's simplicity.

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 documents the single 'year' parameter with constraints (1880–2025) and a description ('Birth year'). The tool description only reiterates the year range without adding additional meaning. With 100% schema coverage, the 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 uses a specific verb ('Returns') and clearly identifies the resource ('top baby names for a given birth year') with a concrete year range (1880–2025). This distinctly separates the tool from siblings like get_decade_names or compare_names.

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 retrieving top names for a specific year but does not explicitly enumerate when to use this tool versus alternatives such as get_decade_names or search_names. No exclusions or conditions are given, so guidance is only 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 of behavioral disclosure. It does disclose the maximum number of suggestions (up to 10) and the matching criterion (prefix). However, it does not mention potential edge cases (e.g., empty prefix, no matches), sorting or case behavior, or the exact structure of the returned suggestions.

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 redundancy. It front-loads the core purpose and immediately follows with the key behavioral limit. Every word earns its place, and there is no extraneous 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?

The tool is simple with one parameter and no output schema, so the description must explain return values. It says 'suggestions' but does not specify the exact return type or format. Given that the description provides the domain ('US baby names'), the filter (prefix), and the count limit, it is mostly complete, but the omitted output structure is a noticeable 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%, so the parameter 'q' is fully described in the schema as a 'Name prefix to search for.' The description adds no new semantic meaning beyond the schema, merely referencing the 'given prefix.' This is exactly the baseline case where 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 clearly states the tool's purpose: 'Autocomplete search for US baby names.' It specifies the resource (US baby names), the action (autocomplete search), and the output (up to 10 suggestions matching a prefix). This distinguishes it from sibling tools, which are data retrieval functions rather than search.

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 clear context for use: it is an autocomplete search returning suggestions for a given prefix. It does not mention explicit alternatives or when not to use this tool, but the context is unambiguous and sufficient for an agent to select it over sibling tools, many of which are for specific data lookups.

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