bva-corpus

List conditions

list_conditions

Read-onlyIdempotent

List the standardized conditions the Veterans’ Rights corpus organizes Board of Veterans’ Appeals decisions under, each with the number of vetted decisions, grants, and remands (full-corpus counts) and the body-system category. Use this to discover which conditions have data, to get the exact slug for get_condition_report, or to see which conditions occur in this collection. Counts describe whole decisions mentioning each condition, not condition-specific wins or individual chances. Counts reflect only quality-filtered, publicly visible decisions.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Tool-specific result payload.
`link`	Yes	Tagged deep link into the matching veterans-rights.com page.
`disclaimer`	Yes	Standing not-legal-advice / not-VA framing.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 18, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": false,
+  "properties": {
+    "data": {
+      "description": "Tool-specific result payload.",
+      "type": "object"
+    },
+    "disclaimer": {
+      "description": "Standing not-legal-advice / not-VA framing.",
+      "type": "string"
+    },
+    "link": {
+      "description": "Tagged deep link into the matching veterans-rights.com page.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "data",
+    "link",
+    "disclaimer"
+  ],
+  "type": "object"
+}

AddedJun 17, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds crucial behavioral nuance: counts represent whole decisions, not condition-specific wins, and counts reflect only quality-filtered, publicly visible decisions. This goes beyond the annotations and clarifies data semantics.

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

Conciseness5/5

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

The description is well-structured: main purpose in the first sentence, followed by usage guidance, then important caveats about count semantics and filtering. Every sentence adds value, and the length is appropriate for the tool's complexity.

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

Completeness5/5

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

Given the tool has no parameters and an output schema exists (signaled), the description covers all necessary context: what data is returned, the key caveat about counts, and how it relates to get_condition_report. Nothing essential is missing for correct invocation.

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

Parameters4/5

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

With zero parameters, the baseline is 4. The description doesn't need to explain parameters, but it does describe the output fields (counts, grants, remands, body-system category), which aids the agent even though no inputs are required.

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

Purpose5/5

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

The description uses a specific verb ('List') and names the exact resource: standardized conditions with counts and body-system categories. It clearly states what the tool does and implicitly differentiates itself from the sibling get_condition_report, making it easy for an agent to understand its role.

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

Usage Guidelines5/5

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

Explicitly states when to use the tool: 'discover which conditions have data, to get the exact slug for get_condition_report, or to see which conditions occur in this collection.' This provides clear context and references the alternative tool, leaving no ambiguity about selection.

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

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