telemost_sentiment

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

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds crucial behavioral context: it is paid per call in USDC from the wallet and references the catalog for pricing, which is not covered by 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?

Two sentences pack the purpose, the alternative, and the cost/pricing pointer. Every word earns its place, and the key information is front-loaded.

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?

The tool has no output schema, and the description does not state the return format. It covers the main use case and cost, but parameter ambiguity and lack of output details create meaningful gaps for an agent deciding whether and how to invoke it.

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 0%, so the description carries the full burden. It mentions 'recent posts and reactions for a channel, group or topic', hinting at 'topic' and 'source', but does not explain 'period' (despite an enum), 'source' format, or how parameters relate. This leaves the agent guessing about required values.

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 clearly identifies the tool as 'Telegram sentiment analysis' with raw inputs for custom mood/opinion analysis, and distinguishes it from the LLM verdict variant by name. Though lacking a direct verb, the resource and intent are unambiguous.

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 contrasts with the LLM alternative ('For a ready LLM verdict use /v1/data/sentiment/llm') and states the use case ('to run your own mood or opinion analysis'). It also mentions the paid nature and how to find pricing, providing context for when to use it.

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