social_research

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does reveal that the research is AI-generated and that the tool costs $0.01 paid via x402 in USDC, which is meaningful operational context. It does not disclose how results are delivered, potential latency, failure modes, or whether the operation is purely read-only.

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?

The description is compact and front-loaded with the tool's purpose, followed by a concise list of research areas. The price and payment method sentence is essential because it informs the agent that the call is paid, so every sentence 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?

With only one fully documented parameter and no output schema, the description gives enough context for an agent to invoke the tool correctly: provide a mint and expect AI-generated social research across several listed dimensions. It could be more explicit about the exact response format, but the listed categories provide a reasonable expectation of the output.

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?

The input schema fully documents the only parameter, 'mint', as a Solana mint address in base58, so schema coverage is 100%. The description adds no extra meaning about the parameter, but also does not need to because the schema is sufficient.

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's resource as social research for a token mint and enumerates concrete content areas such as Twitter/X activity, Telegram presence, website analysis, mentions, sentiment, and entity verification. It distinguishes itself from sibling tools by focusing on social/sentiment data, but lacks a direct action verb like 'retrieve' or 'analyze'.

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?

The use case is implied by the listed social-research outputs: an agent can infer this tool is appropriate when social signals or sentiment are needed. However, there is no explicit statement of when to use this tool versus the many sibling tools, nor any exclusions or alternative routing.

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