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 x402 micropayment (0.02 USDC) vs API-key access, the null-score/insufficient_signal edge case and how to report it, the 70/30 label thresholds, and the returned fields. It does not cover latency, failure modes other than no-signal, or rate limits beyond per-call metering.

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 purpose, then computation, then return semantics and reporting rules. Dense but nearly every sentence adds actionable content; the trailing access/payment note is boilerplate but necessary for invocation.

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, yet the description fully specifies the return shape (score, bullish/neutral/bearish label with cutoffs, post count) and the null case. Combined with 100% schema coverage, an agent has everything needed to call it and interpret results.

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 description coverage is 100%, so both parameters are already documented, and the description only adds that topic is treated as a cashtag ($TOPIC). hours_back gets no added meaning in the description. Baseline 3 is appropriate when the schema does the parameter work.

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 specific verb+resource: 'keyword-polarity sentiment for a crypto cashtag on X/Twitter', and explains the exact computation (counts positive vs negative keyword posts, 0-100 score = positive share). This is much richer than a tautology. It stops short of distinguishing itself from adjacent siblings like get_social_mentions or get_smart_money_sentiment, so it is clear but not sibling-differentiated.

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 says 'Use it when the user asks about market mood or social sentiment for a token,' and adds a reuse rule ('ask once per topic and reuse') driven by metering. No exclusion or named alternative (e.g., get_social_mentions for raw mention counts) is given, so it is clear context without alternatives.

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