Solana Token Intelligence

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

Annotations already carry readOnlyHint=true and openWorldHint=true, and the description is consistent with them — no contradiction. Beyond the annotations, the description adds crucial behavioral context: the tool is PAID ($0.01 USDC), requires a two-step x402 settlement flow, and returns payment requirements in-band on the first call. This cost and payment-mechanics disclosure is genuinely valuable context the annotations do not provide.

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 dense but well-organized: purpose and scope first, then payment mechanics, then demo routing. Every sentence earns its place given the unusual paid-payment flow must be explained, though at ~90 words it approaches the limit of conciseness for a single paragraph.

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 no output schema, the description partially covers return values (the synthesized 0-100 risk verdict and in-band payment requirements). The paid tool's payment flow is fully specified, the data sources and analytic dimensions are enumerated, and the free alternative is named. The main gaps are the exact response structure beyond the risk score and precise cost-limit behavior, but the essential calling information is present.

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 params (mint, _payment) are already documented in the schema — baseline is 3. The description adds marginal value by explaining the _payment payload's role in the retry flow and the in-band payment-requirement fetch, but this largely reiterates the schema's 'base64 x402 payment payload' note rather than adding new meaning.

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 states a specific verb and resource ('synthesized 0-100 risk verdict' on 'any Solana token') and enumerates the concrete data points delivered (mint/freeze authorities, holder concentration, dev holdings, organic score, liquidity cross-checked across three sources). It clearly distinguishes itself from the sibling token_intel_demo by positioning this as the paid production tool versus the free demo.

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 description explicitly routes users to the free sibling first ('Try token_intel_demo first — it is free and proves the pipeline live'), giving a clear when-to-use alternative. The payment flow is spelled out step by step (call once to fetch requirements, settle via x402 client, retry with _payment or X-PAYMENT header), though it does not enumerate exclusion conditions beyond preferring the demo for initial validation.

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

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

Annotations already mark the tool as read-only and open-world. The description adds useful behavioral context: it is free, returns a fixed sample for BONK, and exercises the exact production pipeline, which sets expectations that this is a limited demo rather than a general-purpose lookup.

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?

A single sentence conveys the free nature, the output type, the fixed sample, the production-pipeline equivalence, and the intended evaluation use case. There is no redundant or filler content.

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?

Given zero parameters, strong annotations, and a simple fixed-sample demo, the description provides all necessary context for an agent to select and invoke the tool correctly. The lack of an output schema is not a gap because the description already summarizes the returned report and purpose.

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?

There are zero parameters, so the baseline is 4. The description reinforces that no input is needed by noting the fixed sample token (BONK), making it clear the tool is not parameterized or user-configurable.

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 uses a specific verb ('returns'), names the resource ('complete three-source intelligence report'), and identifies the exact scope ('fixed sample token BONK'). It clearly distinguishes itself from the sibling token_intel by framing itself as the free demo for evaluating the paid version.

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 description explicitly states when to use this tool: to verify output quality before paying for token_intel. It names the alternative and positions this as the pre-purchase evaluation path, giving an agent clear routing guidance.

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