Solana token due-diligence: 3-source fused risk verdict incl. LP-lock depth. $0.01 via x402.
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Average 4.6/5 across 2 of 2 tools scored.
The two tools have clearly distinct roles: token_intel provides the paid, full-featured report for any Solana token, while token_intel_demo is a free demo limited to a fixed sample token (BONK). There is no ambiguity about which tool to use for a given purpose.
Both tools share the consistent token_intel prefix, with demo as a clear modifier. This creates a predictable naming pattern where the base tool is the production version and the suffix indicates a trial/demo variant.
With only two tools, the count is on the lower end but reasonable given the server's narrow purpose: a single paid API endpoint plus a free demo. Some might argue it feels thin, but for a focused token-intelligence service, the minimal surface is acceptable.
The tool set fully covers the domain of Solana token intelligence: one tool delivers the comprehensive report for any token, and the demo validates the pipeline. There are no obvious missing operations, such as batch queries or historical data, that would be essential for the stated purpose.
token_intelSolana token due-diligence (paid, $0.01 USDC on Base or Solana via x402)AFused safety + market read on any Solana token: mint/freeze authorities, holder concentration, dev holdings, organic score, liquidity cross-checked across three independent sources (Jupiter, DexScreener, RugCheck incl. LP-lock depth), synthesized 0-100 risk verdict. PAID: $0.01 USDC on Base or Solana (x402 v2, keyless). To pay: fetch requirements by calling this tool once (returned in-band), settle via an x402 client, then retry with the base64 payment payload either as the '_payment' argument or the X-PAYMENT HTTP header. Try token_intel_demo first — it is free and proves the pipeline live.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | SPL mint address (base58) | |
| _payment | No | base64 x402 payment payload (alternative to the X-PAYMENT header) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and openWorldHint annotations, the description reveals critical behavioral aspects: the tool is paid, calls require fetching in-band payment requirements before settling, and you must retry with a base64 payload either as an argument or HTTP header. This explains a non-obvious multi-step flow essential for correct usage, going far beyond the 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?
The description is relatively long but every sentence contributes: the core function, the cost, the exact payment process, and the demo alternative. It is front-loaded with the primary purpose and uses structured sentences. Slightly verbose in explaining payment, but no wasted words.
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 the tool's complexity (paid, multi-source data, risk verdict output), the description is highly complete: it lists the analysis dimensions, the 0-100 risk output, the payment mechanics, and the free trial sibling. With no output schema, it still sets expectations for return value. This is a thorough description for a complex tool.
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 schema already provides 100% parameter descriptions (mint as base58 SPL address, _payment as x402 payload). The description adds meaningful context by clarifying that _payment can be supplied via the X-PAYMENT header as well, and explains the payment requirement/retry flow, enriching the parameter semantics beyond the schema.
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 states the tool performs a fused safety and market read on Solana tokens with specific data points (mint/freeze authorities, holder concentration, dev holdings, organic score, liquidity cross-check). It distinguishes itself from the sibling token_intel_demo by explicitly naming it as a free alternative, making its unique purpose as the full paid due-diligence tool obvious.
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 provides clear context: it is a paid tool ($0.01 USDC), and it explicitly recommends trying token_intel_demo first as a free alternative. Payment instructions are detailed, covering the retry flow and payload options. However, it does not explicitly state scenarios where the tool should not be used, though the demo alternative implies a trial-first approach.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
token_intel_demoFree demo — full pipeline, fixed sample (BONK)AFree. 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 parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description's addition of 'free', 'fixed sample token (BONK)', and 'exact production pipeline' enhances the tool's behavioral profile without contradiction. It does not detail report contents or limitations beyond the fixed sample, but no contradiction exists.
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 a single, front-loaded sentence beginning with 'Free.' It is concise, every clause adds value (sample, pipeline, purpose), and no words are wasted.
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 the tool has no parameters and no output schema, the description adequately conveys what it returns, that it is a demo, and how it relates to token_intel. It does not elaborate on what the 'three-source intelligence report' contains, but this is a minor gap for a demo tool with a sibling reference.
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?
With zero parameters and 100% schema coverage, the description need not explain inputs. It adds context that the tool uses a fixed sample token, making it clear why no parameters are required. This meets the baseline for a no-parameter tool.
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 states the tool returns a complete three-source intelligence report for a fixed sample token (BONK), using a specific verb ('returns') and resource ('intelligence report'). It also distinguishes itself from the sibling token_intel by being a 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 frames this as a free demo to verify output quality before paying for token_intel, which tells the agent when to use this tool versus the paid alternative. It also notes it exercises the exact production pipeline, setting expectations for parity.
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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