catalog_stars

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations to lean on, the description carries the full behavioral burden and does so well: it discloses the data-release fallback order and that failed searches are reported rather than retried, the supplement-merge behavior with merge_px replacement, the saturated flagging rule, the G-sorted order, and the 200-star inline limit with full output written to a scratch JSON file. Failure semantics and side effects are unusually explicit.

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 purpose and search scope are front-loaded, and despite being a single dense paragraph, each clause conveys distinct information (search geometry, returned fields, release fallback, supplements, ordering, output destination). It is long but justified by the tool's complexity, with only minor packing of multiple ideas per sentence.

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 7-parameter tool with a nested supplement array and no output schema, the description fully compensates by enumerating the returned per-star fields (x, y, RA, Dec, G, BP-RP, in-frame status, peak luminance, saturation) and the file/inline output behavior. An agent has everything needed to invoke and interpret the call correctly.

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 already 100%, so the schema documents each parameter, but the description adds real semantic value beyond it: it clarifies that margin_fraction extends the center-to-corner radius, that merge_px governs replacement of a Gaia star by a supplement star, that saturation_level drives the saturated flag, and that an omitted data_release triggers an ordered fallback. This meaningfully enriches several parameters.

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 names a specific verb and resource ('List catalogue stars over a plate-solved view from PixInsight's local Gaia database') and specifies the exact scope (one Gaia search centered on the image with a corner-reaching radius). This is clearly distinguishable from siblings like measure_stars or measure_star_layer, which measure detected stars rather than querying a Gaia catalogue.

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?

Usage is implied through the precondition that the view must be plate-solved, but the description never states when to prefer this over alternatives such as measure_stars or run_plate_solve workflows. No explicit exclusions or alternative routing are given, leaving the agent to infer the use case from context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.