caomGeneric CAOM (Common Archive Observation Model) query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Raw MAST request body (service + params). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, but the description adds no behavioral disclosure. It does not mention that this is a raw MAST passthrough, response characteristics, or any operational caveats. The description remains silent on behavior beyond the annotations.
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 (six words), which makes it concise in length but not in substance. This is under-specification rather than efficient conciseness; it omits essential guidance and leaves the agent without adequate context.
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 a nested query object, an output schema, and a complex raw request interface, the description provides no overarching context. It fails to explain what CAOM is, the types of queries supported, or how the tool fits into the broader MAST ecosystem. The schema carries all the weight, which is insufficient for an agent to confidently invoke the tool.
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 single parameter 'query' has a schema description ('Raw MAST request body (service + params)') that is 100% coverage. The tool description itself adds no parameter semantics, but the schema already explains the parameter structure, including an example with service and params. This meets the baseline for high schema coverage.
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 states 'Generic CAOM (Common Archive Observation Model) query,' identifying the resource (CAOM) and the action (query). However, it is vague, lacking specifics about what the query does, what it returns, or how it relates to sibling tools. It is not a tautology but offers minimal clarity about the tool's actual function.
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. There is no mention of use cases, prerequisites, or comparison to sibling tools like cone_search or mission_search. An agent would have no basis to decide when to invoke this tool.
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.