tapADQL TAP query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | e.g. "SELECT TOP 5 pl_name, sy_dist FROM ps" | |
| format | No | json (default) | csv | votable |
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Non-JSON response body (first 16000 chars) | |
| format | No | Response format (json, csv, or votable) |
Changes observed during successful MCP inspections.
Input schema / examplesPrevious value: -[
- {
- "query": "SELECT TOP 5 pl_name, sy_dist FROM ps"
- },
- {
- "format": "csv",
- "query": "SELECT pl_name, pl_mass, pl_orbper FROM ps WHERE pl_mass > 1.0"
- }
-]New value: +[
+ {
+ "query": "SELECT TOP 5 pl_name, sy_dist FROM ps"
+ },
+ {
+ "format": "csv",
+ "query": "SELECT TOP 20 pl_name, pl_massj, pl_orbper FROM ps WHERE pl_massj > 1.0"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "query": "SELECT TOP 5 pl_name, sy_dist FROM ps"
+ },
+ {
+ "format": "csv",
+ "query": "SELECT pl_name, pl_mass, pl_orbper FROM ps WHERE pl_mass > 1.0"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Query result from NASA Exoplanet Archive TAP service",
+ "properties": {
+ "body": {
+ "description": "Non-JSON response body (first 16000 chars)",
+ "type": "string"
+ },
+ "format": {
+ "description": "Response format (json, csv, or votable)",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a read-only, idempotent, non-destructive operation, but the description adds no behavioral context beyond that. It does not mention rate limits, result size limits, error behavior, or what TAP endpoint is queried, so behavioral transparency is minimal.
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 short ('ADQL TAP query') but this is under-specification rather than effective conciseness. It is a fragment without a verb, and provides no meaningful information. While it has no wasted words, it fails to serve its purpose.
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?
Despite having an output schema and well-documented parameters, the description leaves major gaps: it does not explain what data is available, what ADQL features are supported, or what TAP service is used. For a generic query tool, this is insufficient context for an agent to use it 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?
The input schema has 100% coverage with descriptions and examples for both 'query' and 'format', so the baseline is 3. The description 'ADQL TAP query' adds no additional meaning beyond what the schema already provides, making it neutral in parameter semantics.
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 'ADQL TAP query' is essentially a fragment that expands the tool name without specifying what data is queried or what service it accesses. It does not differentiate from sibling tools like 'planets' or 'kepler_candidates', and the purpose is only clear to someone already familiar with the domain.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any specific use cases, exclusions, or recommend specialized sibling tools, leaving the agent without direction on scenario-based 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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