token-intel
Server Details
Pay-per-call Solana token rug-risk intelligence via x402. $0.01 USDC, no API key.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.9/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of ambiguity between tools. The agent will always select the correct tool.
With a single tool, naming consistency is not an issue. The name 'get_token_intel' is clear, descriptive, and uses a conventional verb_noun pattern.
The server has only one tool, which is too few for a comprehensive intelligence domain. While the tool itself is detailed, users might benefit from separate tools for different queries (e.g., individual risk factors).
The single tool provides a thorough analysis covering multiple risk aspects: verdict, risk score, confidence, LP-lock details, holder concentration, authority flags, and multi-source breakdown. No obvious gaps for the stated purpose.
Available Tools
1 toolget_token_intelAInspect
Solana token rug-risk intelligence for $0.01 USDC (x402). Returns verdict CLEAN|LOW_RISK|MODERATE_RISK|HIGH_RISK|RUGGED, 0-100 risk score, confidence, LP-lock depth (pool TVL, locked %, burn %), top-10 holder concentration with lock status, mint/freeze/close authority flags, and per-source breakdown from RugCheck + GoPlus + on-chain data.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Base58 Solana SPL token mint address to analyze |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool costs $0.01 USDC and returns a comprehensive risk analysis with multiple data points. No annotations are provided, so the description carries full burden. It does not mention rate limits or authentication, but the cost and data sources are transparent.
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 long sentence but packs all essential output fields without redundancy. It could be improved with bullet points for readability, but it is concise and front-loads the purpose.
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 no output schema and no annotations, the description covers all key return fields (verdict, risk score, LP depth, holder concentration, authority flags, per-source breakdown). However, it lacks guidance on interpreting the risk score or verdicts, which slightly reduces completeness.
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 has 100% coverage with a single parameter (mint) described with regex and description. The tool description does not add any new information about the parameter beyond what the schema provides, so the baseline score of 3 is appropriate.
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 provides 'Solana token rug-risk intelligence' and lists the specific verdict, risk score, confidence, and other outputs. It uses a specific verb ('get') and resource ('token intel'), making the purpose 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?
The description implies usage for assessing token risk but does not explicitly state when to use it vs. alternatives. There are no sibling tools, so differentiation is not required, but the description could mention typical use cases like pre-trade analysis.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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