list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, effectively communicating safety. The description adds details about returned fields but does not disclose additional behavioral traits such as pagination or rate limits. With strong annotations, a baseline score is appropriate.
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. Front-loaded with purpose, then return fields, then usage guidance. Efficiently structured.
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, no output schema needed), rich annotations, and full schema coverage, the description is complete. It sufficiently explains what the tool does and when to use it.
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 a clear description for the only parameter, include_inactive. The description does not add further semantics beyond the schema, so a baseline score of 3 is suitable.
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 'list the caller's active subscriptions'—a specific verb (list) and resource (subscriptions) with scope ('caller's active'). It also enumerates return fields, distinguishing it from siblings like subscribe and unsubscribe.
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 when-to-use guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Does not explicitly mention when not to use or alternatives, but the context among sibling tools like subscribe/unsubscribe makes this acceptable.
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.
Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded are three variants of the same router, while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The descriptions are detailed, but an agent must read extensively to avoid selecting the wrong tool within each cluster.
Naming is a mix of conventions: get_*/search_* for NASA tools, ask_pipeworx_* and polymarket_* family prefixes, plus one-off names like entity_profile, bet_research, deep_research, recent_changes, and scan_dependency. There is no consistent verb_noun or family-wide pattern, making tool selection unpredictable despite each individual name being readable.
36 tools is heavy for a server named Nasa, and only 5 of them are actually NASA-related; the rest form a sprawling general data-research, prediction-market, memory, and subscription toolkit. The count is borderline defensible for a broad data assistant, but it is clearly unjustified under the server's stated NASA identity.
As a general data-research assistant the surface is quite complete: discovery, routing, grounded verification, entity profiles, comparisons, memory, subscriptions, and feedback are all covered. As a NASA server, however, there are notable gaps—no EONET events, Earth observation, exoplanet archive, or TLE/mission-specific data—and the large non-NASA tool surface does not fill those gaps.