Telegram vs X — which venue called this token first

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

Goes well past the annotations: discloses privacy behavior (source rooms never identified, no message text, only a room count), freshness metadata (corpus_through / corpus_stale) so staleness is detectable, and the measured-absence semantics of seen_on_telegram:false. It also states the monetization cost ($0.03 USDC on Base), which an agent needs before invoking a paid call.

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 the core measurement and sign convention, then behavior, then caveats, then price — a sensible order. Some marketing-flavored sentences ('cannot be bought from any data vendor') and the corpus-wide 70/30 statistic are expendable for a per-token lookup, but the rest earns its place.

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?

No output schema exists, so the description carries the return-shape burden and does: timestamp pair, signed lead, room count, coverage fields, and the false-result semantics. Combined with the documented cost and freshness contract, an agent has everything needed to decide and to interpret the response.

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 100%, so the token format is already documented; the description adds meaning by explaining what an unseen/unmatched token yields (seen_on_telegram:false as a bounded absence, not proof of no discussion). That resolves the main ambiguity about this single parameter's domain of meaningfulness.

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: 'cross-venue first sighting for one Robinhood Chain token', and enumerates exactly what comes back (first Telegram timestamp, first X timestamp, lead in minutes with sign convention). This is clearly distinguishable from siblings like robinx_mentions or robinx_social_momentum, which are not per-token first-sighting measures.

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 context is clear — per-token, cross-venue, first-sighting question — and it adds interpretation guidance ('use the per-token number, never the average'). It never names an alternative sibling for a related but different question (e.g., mentions volume or social momentum), so the 'when not to use' side is implicit rather than explicit.

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