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

With no annotations, the description carries the full burden and does well: it discloses the data provenance (open API by Cumulo, snapshot refreshed every 15 minutes, credited in each answer) and a real trust caveat (validator names and hosts are self-typed and untrusted). It does not state auth/rate-limit expectations or pagination behavior, but the freshness and trust disclosures are substantive context beyond 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?

Front-loaded with what is returned, then filtering/sorting, then provenance, then the sibling pointer — a sensible order for a list tool. It is dense but mostly earns its length; phrases like 'credited in each answer' are minor filler.

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 9-parameter, no-annotation, no-output-schema tool, the description covers the returned fields, data freshness, trust caveats and the single-validator alternative, which is what an agent needs to select it. Gaps are pagination semantics (limit/offset behavior) and auth expectations, though those are partially visible in the schema.

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 high (78%), with sort, registered, chain, status, country and provider already documented inline, so the schema does most of the heavy lifting. The description restates the filter/sort axes ('Filter by registered or not, status, name, country or provider; sort by stake, latency, uptime or name') and confirms the default chain (mocha-5) but adds little syntax or edge-case meaning beyond the schema. Baseline 3 is appropriate.

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?

States a precise verb+resource (list the validators of the Celestia Fibre network) and enumerates the exact measurement payload (host readiness, stake rank/share, RTT, 24h/7d/30d uptime, country, provider). It is clearly distinguishable from siblings like list_celestia_validators and get_fibre_validator without opening any schema.

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?

Explicitly routes the agent: 'Use get_fibre_validator for one validator with its signing record, data retention, probes and diagnosis.' It also enumerates the filter and sort dimensions available for this list view. It stops short of stating when-not to use this tool (e.g. for authoritative/staked data), so it is strong but not fully exhaustive.

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