microlensingMicrolensing event table.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No |
| 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: -[
- {
- "limit": 20,
- "query": "event_year > 2010"
- }
-]New value: +[
+ {
+ "limit": 20,
+ "query": "pl_name like 'OGLE-2018%'"
+ },
+ {
+ "limit": 5,
+ "query": "sy_dist < 5000"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "limit": 20,
+ "query": "event_year > 2010"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Query result from microlensing event table",
+ "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?
The description adds no behavioral context beyond what annotations already provide. Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the agent already knows this is a safe read operation. 'Microlensing event table' does not describe any traits like filtering behavior, pagination, or output format.
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 and front-loaded: a single phrase with zero wasted words. However, its brevity comes at the cost of substance, so while it's well-structured for length, it's under-specified for utility.
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 annotations, the description is incomplete. It offers no explanation of what a microlensing event table contains, how to formulate queries, or how results are limited/ordered. For a tool with a query parameter, it leaves too much implicit.
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 0%, so the description must compensate. It does not mention 'limit' or 'query', leaving their meaning entirely to the schema (which also lacks descriptions). The examples in the schema hint at query syntax, but the description itself provides no 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 'Microlensing event table' is a noun phrase with no verb, so it doesn't explicitly state what the tool does (e.g., 'List microlensing events'). It conveys the resource but not the action, and it fails to distinguish from sibling tools like 'planets' or 'kepler_candidates', which are also astronomical data tables.
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 provides no context about querying microlensing events or when this dataset would be preferred over sibling tools like 'planets' or 'kepler_candidates'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.