token_intel_demoFree. Returns the complete three-source intelligence report for a fixed sample token (BONK), exercising the exact production pipeline — verify output quality before paying for token_intel.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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.
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The two tools are clearly distinguished: token_intel is the paid production tool for any Solana token, while token_intel_demo is explicitly a free fixed-sample demonstration. There is no realistic ambiguity about which tool an agent should call.
Both tool names follow the same snake_case pattern, with token_intel_demo cleanly extending the base token_intel name as a demo variant. The naming is predictable and consistent.
Two tools is on the thin side, and one is only a demo wrapper for the other, leaving effectively one production operation. The count fits the narrow single-purpose scope but feels minimal.
The core promised capability—a fused safety and market intelligence report for any Solana token—is covered, and the demo helps verify the pipeline before payment. Missing extras like raw source breakdowns or historical tracking are minor gaps rather than critical failures.