cone_searchSearch MAST around (RA, Dec).
| Name | Required | Description | Default |
|---|---|---|---|
| ra | Yes | ||
| dec | Yes | ||
| limit | No | ||
| mission | No | e.g. "HST", "JWST". Optional. | |
| radius_arcmin | No | Default 1.0 |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Array of observation records matching cone search criteria |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only (readOnlyHint=true), idempotent (idempotentHint=true), and non-destructive (destructiveHint=false), so the description does not need to repeat those. The description adds little beyond the resource name (MAST) and does not disclose additional behaviors like rate limits, pagination, or result ordering, but this is acceptable given the strong annotation coverage.
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, front-loaded sentence with no wasted words, making it highly concise and easily parseable. However, it is almost too terse, omitting useful context that could be added without bloating the description.
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 tool with 5 parameters, 2 required, and an output schema, this description is insufficient. It does not explain what MAST is, how to interpret the parameters, default behaviors (e.g., radius_arcmin default is in schema), or any other contextual clues needed for proper invocation. The description relies entirely on the schema and annotations, which are not fully populated.
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 only 40% (only mission and radius_arcmin have descriptions). The description does not compensate for the lack of parameter explanations; it merely repeats the coordinate parameters (RA, Dec) and gives no insight into limit, radius_arcmin, or the meaning of mission beyond what the schema already provides. Since coverage is low, the description needed to add more value.
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 the tool performs a search on MAST around a central coordinate (RA, Dec), making the verb and resource explicit. It does not explicitly differentiate from sibling tools like mission_search or search_within, but the specific mention of MAST and cone search context gives reasonable clarity.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of typical use cases, prerequisites, or situations where a different tool would be more appropriate, leaving the agent to infer usage from the name and description alone.
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.
Most tools have distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, all of which query the Pipeworx database with different levels of structure. The detailed descriptions help differentiate them, but the overlap is notable.
All tool names use snake_case consistently, which is good. However, the naming conventions vary: some are descriptive phrases (e.g., ai_visibility_check), others are verb_noun (e.g., list_subscriptions), and some are compound nouns (e.g., entity_profile). Lack of a single pattern reduces consistency slightly.
34 tools is on the higher side for a single server, but it may be justified given the broad scope of Pipeworx data sources. However, the server name 'Mast Nasa' implies a focus on astronomy, yet only a few tools relate to that domain, making the count feel inflated and unfocused.
The tool set is comprehensive for the Pipeworx data platform, covering querying, grounding, entity resolution, comparison, subscriptions, and more. However, for the implied NASA/Mast domain, the surface is severely incomplete with only four astronomy-specific tools, leaving obvious gaps.