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Agent Kiosk — 23-in-1 B2A Intelligence, Action & x402 API

by io.github.ralf9090
OverviewDiscussionsAdmin

token_alpha

Read-onlyIdempotent

Real-time crypto & DeFi order-flow intelligence. Analyzes buy vs. sell transaction pressure, liquidity depth, volume velocity, FDV, and top DEX pools for any token ticker or contract address.

Input Schema

NameRequiredDescriptionDefault
chainNoOptional chain filter (default: 'all')
queryYesToken symbol (e.g., 'AERO', 'PEPE', 'VIRTUAL') or contract address (0x...)
x_api_keyNoOptional ak_live_... 30-day Prepaid API Pass key to unlock the full unmasked dataset
payment_signatureNoOptional x402 v2/v1 Base64 or JSON EIP-3009 USDC payment signature to unlock the full unmasked dataset directly via MCP

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / payment_signature
      Added value: +{
+  "description": "Optional x402 v2/v1 Base64 or JSON EIP-3009 USDC payment signature to unlock the full unmasked dataset directly via MCP",
+  "type": "string"
+}
    • addedInput schema / properties / x_api_key
      Added value: +{
+  "description": "Optional ak_live_... 30-day Prepaid API Pass key to unlock the full unmasked dataset",
+  "type": "string"
+}
  2. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds the analytical content but omits the key behavioral trait that results are masked unless an x_api_key or payment_signature unlocks the full dataset (that lives only in the schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences that front-load the purpose and then enumerate the metrics. The leading tagline is slightly marketing-flavored, but no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only, no-output-schema tool, the description conveys what is analyzed, which is the main thing an agent needs. The payment-gated masking behavior is a notable omission but is covered by the schema's x_api_key and payment_signature parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so all four parameters are already documented in the schema. The description's 'token ticker or contract address' maps to query but adds no syntax or format detail beyond it, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Analyzes') and resource (buy vs. sell pressure, liquidity depth, volume velocity, FDV, top DEX pools) scoped to a token ticker or contract address. An agent can tell it produces order-flow analytics, but nothing distinguishes it from sibling token_due_diligence or whale_alpha_radar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative routing is given. 'For any token ticker or contract address' defines scope, not selection guidance, so the agent must infer when this beats token_due_diligence.

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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