bva-corpus

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

Annotations already declare readOnlyHint and idempotentHint; the description adds meaningful context about being educational, that lists are representative, and what return information includes, exceeding annotation coverage.

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

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

The description is a single paragraph of four sentences, front-loaded with main purpose, no waste, each sentence adds value.

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

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

Given the schema, annotations, output schema, and sibling tools, the description fully covers the tool's behavior, limitations, and usage 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 description coverage is 100%, so the parameter descriptions are already clear. The tool description doesn't add significant new meaning beyond the schema's parameter 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 checks if a condition is a presumptive service connection, specifies covered exposure categories, and distinguishes it from sibling tools like list_conditions or search_bva_decisions.

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 explains when to use the tool (for presumptive conditions) and covers the scope, but does not explicitly state when not to use or name specific alternatives, though siblings imply context.

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?

Beyond annotations (readOnlyHint, idempotentHint), the description adds that ratings do not add linearly, supports bilateral factor, returns step-by-step breakdown, and is a deterministic estimate. This provides useful behavioral context without contradicting annotations.

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

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

The description is 3 sentences, front-loaded with purpose, then key behaviors, then disclaimer. Every sentence adds value with 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?

Given the tool's complexity, the description covers the algorithm, official reference, bilateral factor, output structure, and deterministic nature. An output schema exists, so return values are handled. Minor omission: max items limit is in schema but not in 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?

The input schema is fully documented with descriptions for percent, label, and bilateral. The description adds context about the formula and rounding but does not add significant parameter-specific meaning beyond the schema. 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 calculates a veteran's VA combined disability rating from individual ratings using the official formula. It specifies the verb 'calculate' and resource 'combined rating' and distinguishes itself from siblings which are about conditions, appeals, or reps, with no similar calculation tool.

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 combining disability ratings with examples and formula details. It does not explicitly state when not to use or name alternatives, but given no similar siblings, the 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 idempotentHint=true, so the description doesn't need to reiterate safety. It adds value by clarifying the educational, non-legal-advice nature and tailoring behavior, enhancing transparency beyond annotations.

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

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

The description is moderately concise at four sentences. It front-loads the core purpose and each sentence adds value, but could be slightly tightened without losing 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 simple educational tool with two optional boolean parameters and an output schema, the description fully explains what it returns and the tailoring logic. No gaps are present given the tool's complexity.

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 documents both boolean parameters. The description goes further by explaining that the parameters optionally tailor the suggestion, adding semantic context not present in 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 explains a veteran's appeal options under the AMA, listing the three review lanes and three Board dockets. It distinguishes itself from sibling tools like check_presumptive_conditions or estimate_combined_rating, which address different veteran needs.

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 implicitly indicates use when a veteran needs to understand appeal options after a VA decision. It notes the educational nature and optional tailoring, but lacks explicit when-to-use/not-use guidance compared to 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?

The description discloses key behavioral traits: it leads with free options, filters by multiple criteria, and mentions fee regulations for attorneys/agents. This adds value beyond the annotations (readOnlyHint, idempotentHint) by explaining the ordering and legal constraints. No contradiction with annotations.

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

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

The description is concise with three sentences: the first states the purpose, the second explains representative types, and the third adds legal context. It is front-loaded with the main action and avoids unnecessary 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 description is fairly complete for a read-only lookup tool. It covers purpose, filters, fee rules, and a disclaimer. The presence of an output schema reduces the need to describe return values. However, the inconsistency about 'language' filtering is a minor gap.

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

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

The description adds meaning by explaining the rep_type enum values and the fee implications for attorneys/agents. However, it mentions filtering by 'language' which is not a parameter in the input schema, causing confusion. Schema coverage is low (25%), so the description should compensate more fully, but it does not fully clarify all parameters (e.g., 'limit' is not explained).

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 a VA-accredited representative for disability claims or appeals. It specifies the verb 'Find' and the resource 'representative', and distinguishes itself from sibling tools like 'check_presumptive_conditions' by focusing on locating a human representative. The mention of filtering by state, city, type, and language further clarifies the scope.

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 context on when to use this tool: when one needs accredited representation for a claim or appeal. It explains the types of representatives and that free options are shown first. While it doesn't explicitly state when not to use it or contrast with siblings, the context is sufficient for typical use cases.

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

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

Annotations already declare readOnlyHint and idempotentHint, so the description's main addition is clarifying the educational nature and non-predictive role. This adds useful context beyond annotations, ensuring agents understand it is statistical analysis, not a decision 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 three sentences with no redundant text. It front-loads the action and output, then adds value with usage context and disclaimers. Every sentence contributes meaningfully.

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 complexity and output schema existence, the description covers core functionality and output nature. However, missing parameter explanations for is_pact_act and sample_limit, and lack of detail on return structure (though output schema may compensate), leave the tool only partially complete for agent use.

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

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

With only 20% schema description coverage, the description names three of five parameters (conditions, service-connection theory, toxic-exposure basis) but omits 'is_pact_act' and 'sample_limit'. It does not explain enum values or parameter constraints, leaving significant gaps for agent understanding.

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 with specific verbs ('analyze') and resources ('similar real BVA decisions'), detailing output (grant/deny/remand breakdown, grant rate, examples). It distinguishes itself from siblings like 'search_bva_decisions' by emphasizing similarity analysis and educational statistics.

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

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

The description implies usage for understanding trends in similar appeals ('how have appeals like mine gone'), but does not provide explicit when-to-use or when-not-to-use guidance compared to sibling tools. It includes a caveat about not being prediction/legal advice, which helps, but lacks explicit alternatives or 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?

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds behavioral context: it is educational only, not legal advice, and provides overviews and deep dives with typical durations, tips, and do/don't guidance, which aligns with 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?

The description is a single, well-structured paragraph that front-loads the purpose ('Plain-English guide to the 8 stages'), then explains usage patterns, and ends with a disclaimer. Every sentence is necessary; 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?

Given the tool's simplicity (one optional parameter, read-only, idempotent, with output schema), the description covers the tool's purpose, usage, behavior, and limitations comprehensively. It includes educational-only disclaimer, satisfying 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?

Schema coverage is 100%, with the parameter 'stage' having a description in the schema. The description adds meaning beyond the schema by explaining that omitting the parameter gives an overview and providing a stage number (1-8) gives a deep dive, including what 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 it provides a 'Plain-English guide to the 8 stages of a VA disability claim', using specific verbs ('guide') and resources ('8 stages'). It distinguishes from sibling tools (e.g., 'check_presumptive_conditions', 'estimate_combined_rating') by focusing on process overview and deep dives.

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: 'whenever a veteran asks what happens after filing, where they are in the process, what a C&P exam is, or what to do at their current stage.' It also differentiates between calling with no arguments (overview) or with a stage number (deep dive). No explicit when-not-to-use, but the 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 idempotentHint=true, so the tool is safe and idempotent. The description adds value by detailing the report's contents (e.g., appeal outcomes, ratings, PACT-Act pathways) and its non-predictive, educational nature. This goes beyond what annotations 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 extremely concise—three well-structured sentences. It front-loads the core action ('return a full corpus-grounded report'), lists key report elements, and ends with a disclaimer. Every sentence adds value without redundancy.

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

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

Given the tool's complexity, the description adequately covers inputs, output contents, and qualification (educational). The output schema exists to define return structure, so that gap is acceptable. It does not mention error handling or edge cases, but overall it gives a clear picture of what the agent will get.

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?

Both parameters have schema descriptions (condition as slug/alias, example_limit with default/min/max). The tool description adds context by explaining how example_limit relates to 'a few representative decisions' and reinforces condition input flexibility. Schema coverage is sufficient, and the description clarifies usage beyond the schema.

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

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

The description clearly states the tool returns a 'full corpus-grounded report' for a single condition, listing specific components (appeal outcomes, service-connection theories, ratings, etc.). It uses verbs like 'return' and distinguishes this comprehensive tool from siblings like 'list_conditions', 'search_bva_decisions', and 'get_corpus_stats' by calling it 'the richest single call' for condition queries.

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 positions this as the primary tool for broad condition overviews ('The richest single call...'). It specifies the input format (slug or alias) and clarifies the output is educational, not predictive. However, it does not explicitly state when to avoid this tool or name sibling alternatives, leaving some implicit guidance.

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 provide readOnlyHint=true and idempotentHint=true. The description adds context that counts reflect only quality-filtered, publicly visible decisions, which is a useful behavioral nuance beyond 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?

Three concise sentences. The first lists return values, the second provides usage guidance, the third adds a qualifier. No wasted words, all information is relevant and front-loaded.

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

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

The description fully covers the tool's purpose and output for a zero-parameter tool. It lists all returned statistics and adds a qualifier about data filtering. With an output schema expected, the description is 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?

There are zero parameters, so the baseline is 4. The description does not need to add parameter information; it appropriately focuses on the output and usage.

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 live corpus statistics and lists specific data points (number of decisions, outcome distribution, date range, accredited representatives). It distinguishes itself from siblings by providing a general overview rather than specific case processing.

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 when to use: 'to establish the scale of the data behind an answer, or when someone asks how much data do you have / how current is it.' It does not mention when not to use or alternatives, but sibling context makes differentiation 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 indicate read-only and idempotent; description adds critical context that counts reflect quality-filtered, publicly visible decisions, enhancing transparency beyond 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?

Two sentences with no wasted words, each sentence providing essential information about purpose, usage, and data scope.

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 zero parameters and output schema presence, description covers purpose, usage, and data quality notes fully. No gaps identified.

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?

No parameters in schema (0 required), so description does not need parameter details. Baseline set to 4 for zero-parameter tools.

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 standardized conditions with counts and body-system categories, distinguishing it from related tools like 'get_condition_report' by mentioning slug retrieval.

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

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

Provides explicit use cases: discovering conditions, getting slugs, comparing prevalence or win rates. Does not include when-not-to-use, but context with sibling tools implies alternatives.

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 idempotentHint=true, establishing the safety profile. The description adds that results are 'vetted decisions with a plain-English summary, the deciding factor, and a link', adding context beyond annotations but not contradicting them.

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 concise sentences, front-loading the action ('Search real...') and listing filters efficiently. Every phrase adds value 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?

Given the complexity (9 parameters, 22% schema coverage), the description outlines the key return fields (summary, factor, link) and the presence of an output schema reduces burden. However, it omits important behavioral details like pagination or result limits beyond the limit parameter.

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 only 22%, and the description does not add detailed parameter semantics beyond listing filter categories (e.g., 'keyword, claimed condition, disposition'). For the many parameters without schema descriptions, the description offers no additional guidance on format, syntax, or constraints.

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 searches real U.S. BVA decisions by multiple criteria and lists specific filters. It distinguishes from sibling tools like find_similar_appeals or get_corpus_stats by focusing on searching actual decisions.

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

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

The description explicitly says 'Use to ground any claim about how VA appeals have actually been decided.' This provides clear context for when to use the tool, though it doesn't explicitly mention when not to use it or compare to alternative tools.

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