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Verse's eclipse skill

skill_eclipse
Read-only

Ask Verse's "eclipse" skill (Sky and earth), one of the proven skills with the most evidence that Verse offers over MCP (read-only): rated proven on a-to-mind.com from evidence (real asks in Verse's benchmark, its tests), never from how it describes itself. Asks like "next eclipse", "next lunar eclipse", "upcoming eclipses". The skill's own code answers it on the server, the same code that draws its card on a-to-mind.com (no model), with the sources its card cites; an ask the skill does not take is refused, not guessed. Times are worked out on the server's clock (UTC). The card itself is at the "open" link in the result.

Input Schema

NameRequiredDescriptionDefault
askYesThe ask in plain words (3 to 600 characters), e.g. "next eclipse".
response_typeNoWhat to get back: 'text' (default) or 'raw_data' (the structured result as JSON). 'image' is not offered yet.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior5/5

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

Beyond the readOnly/openWorld annotations, it discloses that execution is server-side deterministic code with cited sources, that times are computed on the server's UTC clock, and that out-of-scope asks are refused — real behavioral context an agent cannot get from the annotations. Nothing here contradicts readOnlyHint=true or destructiveHint=false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The operative content (what it answers, examples, server-side computation, UTC, refusals, card link) leads well, but several clauses are promotional filler about a-to-mind.com ratings and evidence methodology that don't help an agent decide or call the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does explain the return context (sources cited, card at the 'open' link) and the refusal behavior. It omits any mention of the raw_data/image response_type options, but those are fully covered by the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents 'ask' (3–600 chars, with an example) and the response_type enum. The description reinforces the natural-language ask format but adds nothing about response_type, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific resource (Verse's 'eclipse' skill, 'Sky and earth') and gives representative asks ('next eclipse', 'next lunar eclipse', 'upcoming eclipses'), so the agent can immediately tell this is the astronomy/eclipse answer skill rather than a generic one. It also clarifies the answer is produced by the skill's own server code, not a model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It supplies clear triggering examples and states that an ask the skill doesn't take is refused rather than guessed, which is useful behavioral guidance. It never explicitly contrasts with the other astronomy-flavoured siblings (moon_phase, meteor_showers), so routing among those is left to the topic match.

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

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