Get statistical information about field values in the ClinicalTrials.gov database.
| Name | Required | Description | Default |
|---|---|---|---|
| field_names | No | Field names to get statistics for | |
| field_types | No | Field types to filter by (ENUM, STRING, DATE, INTEGER, NUMBER, BOOLEAN) |
Changes observed during successful MCP inspections.
Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / properties / field_names / anyOfRemoved value: -[
- {
- "items": {
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
-]Input schema / properties / field_names / defaultRemoved value: -nullInput schema / properties / field_names / itemsAdded value: +{
+ "type": "string"
+}Input schema / properties / field_names / typeAdded value: +"array"Input schema / properties / field_types / anyOfRemoved value: -[
- {
- "items": {
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
-]Input schema / properties / field_types / defaultRemoved value: -nullInput schema / properties / field_types / itemsAdded value: +{
+ "type": "string"
+}Input schema / properties / field_types / typeAdded value: +"array"Output schema / (root)Previous value: -{
- "additionalProperties": true,
- "type": "object"
-}New value: +nullDoes 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 but fails to specify what 'statistics' entails (counts, distributions, null percentages?), whether the operation is expensive, or what happens when called with zero parameters (which is allowed since no parameters are required).
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, efficient sentence with no redundant words. However, given the complete absence of annotations and output schema, it is arguably too brief to provide sufficient context for proper tool selection.
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 tool with full schema coverage, the description is minimally adequate, but gaps remain regarding the return value format (what statistics are returned) and the zero-parameter behavior. Without an output schema, the description should have briefly characterized the statistical output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for both parameters ('Field names to get statistics for' and 'Field types to filter by'), so the baseline score is 3. The description itself adds no parameter-specific context, but does not need to compensate given the comprehensive schema documentation.
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 ('Get') and identifies the resource ('statistical information about field values') and scope ('ClinicalTrials.gov database'). However, it doesn't explicitly differentiate from sibling get_available_fields, which may also return field metadata, leaving ambiguity about which tool to use for discovery versus analysis.
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 no guidance on when to use this tool versus alternatives (particularly get_available_fields), nor does it mention prerequisites such as needing valid field names from get_available_fields first. No when-not-to-use or exclusion criteria are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.