Real-time DEX token momentum, liquidity health, and whale flow monitoring on Base Mainnet gated with gasless x402 (EIP-3009) micropayments.
Interactive API Docs: https://x402-intel-engine-production.up.railway.app/docs
OpenAPI Schema: https://x402-intel-engine-production.up.railway.app/openapi.json
Settlement Chain: Base Mainnet (8453)
Asset: USDC (0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913)
Endpoint | Price (USDC) | Description |
|
| 5m volume, buy pressure, momentum score |
|
| DEX liquidity depth & turnover ratio |
|
| 1h whale accumulation & distribution signals |
Related MCP server: hyperd-mcp
Add the server to your claude_desktop_config.json:
{
"mcpServers": {
"x402-dex-intel": {
"command": "python",
"args": ["/path/to/mcp_server.py"],
"env": {
"AGENT_PRIVATE_KEY": "0x_YOUR_FUNDED_BASE_WALLET_PRIVATE_KEY"
}
}
}
}Tier 1 ($0.02 USDC): Fetches real-time 5m momentum, buy/sell transaction count,
volume, and buy-pressure score for any cryptocurrency or DEX token.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden. It discloses that data is real-time, covers a 5m window, includes several specific metrics, and notes a $0.02 USDC cost. However, it does not disclose whether the tool is safe/read-only, what may cause failures, or any rate limits or token formatting requirements.
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 that conveys useful information efficiently: cost, data freshness, returned metrics, and acceptable token types. There is no fluff or repetition of the schema properties.
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?
For a one-parameter tool with an output schema, the description is largely complete: it specifies the input domain, the key metrics returned, and the Tier 1 cost. It could be more complete by mentioning any preconditions such as token format or chain context, but given low complexity, it covers most necessary context.
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 0%, so the description must compensate. It adds that 'token' can be any cryptocurrency or DEX token, which gives the parameter broad scope. It does not clarify whether the token should be a symbol, address, or standardized identifier, but the schema contains only one required parameter and the description supplies the essential semantic.
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 has a specific verb ('Fetches') and states a clear deliverable: real-time 5m momentum, buy/sell counts, volume, and buy-pressure score. It also distinguishes from sibling tools like get_orderbook_depth and get_whale_flow by focusing on token momentum/sentiment rather than order books or whale activity.
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?
No explicit guidance is given about when to use this tool versus the sibling alternatives. The mention of 'Tier 1 ($0.02 USDC)' provides pricing information but not use-case direction. 'Any cryptocurrency or DEX token' defines token scope but not the situational choice between this and get_orderbook_depth or get_whale_flow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tier 2 ($0.05 USDC): Fetches simulated orderbook bid/ask depth, liquidity imbalance,
and estimated slippage (in bps) for $10k trades.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden and does well by disclosing that the data is 'simulated' and that slippage is estimated for $10k trades. It also surfaces the pricing tier, which is useful behavioral context. It could add more about output shape or data freshness, but the output schema covers some of that.
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, front-loaded sentence with no filler. It packs pricing, data type, and key metrics efficiently while remaining readable.
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?
For a one-parameter tool with an output schema, the description covers purpose, cost, simulation nature, and slippage context. It is slightly incomplete in that it omits token parameter semantics and any comparison to sibling tools, but the overall picture is sufficient for basic selection and invocation.
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 0%, and the description does not mention the 'token' parameter at all. The token's meaning is somewhat inferable from the tool name and context, but the description fails to explicitly connect the parameter to token symbol, address, or accepted format, leaving a gap.
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 ('Fetches') with a concrete resource ('simulated orderbook bid/ask depth') and adds differentiating details: liquidity imbalance, estimated slippage in bps, and $10k trade size. This clearly distinguishes it from sibling tools like get_whale_flow and get_market_pulse.
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 orderbook depth and slippage analysis, and the 'Tier 2 ($0.05 USDC)' cost hints at when it applies, but it provides no explicit guidance on when to prefer this tool over the sibling tools. No alternatives or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tier 3 ($0.10 USDC): Fetches 1h smart money accumulation, net whale inflow (USD),
and cluster accumulation confidence signals.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions that the tool fetches data (implying read-only) and includes cost information, but it does not disclose authentication needs, rate limits, or potential side effects. Given the lack of annotations, this is insufficient.
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 concise sentence, front-loaded with cost and then the action. It avoids redundancy and is easy to parse, though it could have included more detail on the parameter without becoming verbose.
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 only one parameter and an output schema, the lack of return value explanation is acceptable. However, the absence of usage guidance and parameter semantics makes the description incomplete for a simple tool, limiting its overall usefulness.
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 0%, so the description must explain the parameter. However, it does not elaborate on what 'token' means (e.g., address, symbol, format). The only clue is the name, which is not sufficient for unambiguous use.
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 verb 'Fetches' and the specific resource: 1h smart money accumulation, net whale inflow (USD), and cluster accumulation confidence signals. It differentiates from sibling tools which focus on orderbook depth and market pulse, making its distinct purpose evident.
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 no explicit guidance on when to use this tool versus alternatives. The usage is implied by the tool's name and description—for whale flow analysis—but there are no explicit conditions, exclusions, or mentions of preferable scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0get_market_pulseget_orderbook_depthget_whale_flowEach tool targets a distinct market data aspect: orderbook depth, whale flow, and market pulse. There is no functional overlap, and an agent can easily select the right tool based on the specific intelligence needed.
All tool names follow a consistent 'get_' prefix followed by a descriptive noun phrase (orderbook_depth, whale_flow, market_pulse). The naming pattern is uniform and predictable.
With only 3 tools, the server is tightly scoped to its purpose of providing DEX market intelligence. Each tool covers a distinct data category, and the count is well-suited for a focused, specialized server.
The tool surface covers the core market intel needs: orderbook, whale activity, and market momentum. Potential minor gaps like historical data or token metadata exist, but for the stated purpose, the coverage is nearly complete without dead ends.
Pay-per-query x402 business intelligence on Base, settled in USDC via the native 402 payment flow.
Pay-per-call crypto market intelligence for AI agents. USDC on Base via x402.
32 paid x402 endpoints for crypto, Zora & on-chain analysis. 10 MCP tools. USDC on Base.
On-chain security and market intelligence for trading agents on Base.
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