gapup-mcp

usdc_x402_payments_intel

Read-only

Real-time analytics on x402 protocol USDC micropayments for MCP endpoints on Base network. Unique competitive advantage: aggregates internal production telemetry (our own traffic data) with on-chain USDC Transfer events and Bazaar marketplace listings — data no external competitor can access. Four modes: (1) facilitator_stats — Coinbase x402 facilitator settlement statistics (volume, count, top payees/payers). Uses Coinbase CDP API if COINBASE_X402_API_KEY is set; falls back to Base mainnet RPC scan of USDC transfers to known facilitator addresses. (2) endpoint_intel — Per-MCP-endpoint analytics: tx count, USDC volume, unique callers, success rate, catalog size. For gapup-mcp.io endpoints: reads internal JSONL telemetry (richest data source, unique). (3) agent_caller_profile — Anonymous profile of a calling agent wallet: tx count, USDC spent, top endpoints, inferred persona (depth-seeker / bulk-scanner / generalist / researcher / explorer). Wallet anonymised via SHA-256. (4) price_radar — USDC price distribution by tool category (data_lookup / synthesis / compliance / competitive) from Bazaar + internal catalog. Returns median, P25, P75. Network: Base mainnet. USDC contract: 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913. Cache: 30 min LRU. Timeout per source: 8s. Optional env: COINBASE_X402_API_KEY (higher-fidelity facilitator stats).

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Analytics mode: facilitator_stats=network-wide settlements \| endpoint_intel=per-URL analytics \| agent_caller_profile=per-wallet analytics \| price_radar=price distribution by category
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`category`	No	Tool category for price_radar mode. Defaults to all.
`period_days`	No	Lookback window in days (5-90, default 30)
`endpoint_url`	No	MCP endpoint URL for endpoint_intel mode (e.g. https://mcp.gapup.io/mcp)
`wallet_address`	No	EVM wallet address for agent_caller_profile mode (0x...)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`sources`	Yes
`price_radar`	No
`quality_score`	Yes
`endpoint_intel`	No
`facilitator_stats`	No
`agent_caller_profile`	No

TDQS

A4.4/5.0

Behavior4/5

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

Adds behavioral context beyond annotations: caching (30 min LRU), timeout (8s per source), optional env var, anonymization via SHA-256, fallback behavior. No contradiction with annotations (readOnlyHint, etc.).

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?

Well-structured: opening sentence, competitive advantage, four modes listed with details, then env/cache/timeout. Slightly lengthy but every sentence adds value; front-loaded with main purpose.

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

Completeness5/5

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

Given complexity (4 modes, multiple data sources, env-dependent behavior, caching, timeout), the description covers all essential aspects. Output schema exists, so no need to detail return values. Comprehensive.

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

Parameters4/5

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

Schema coverage is 100% with descriptions, but the tool description provides additional meaning: e.g., facilitator_stats uses CDP API if key set else RPC scan, category defaults to 'all', period_days range. This enriches understanding beyond schema.

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

Purpose5/5

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

Description states specific verb 'analytics' on 'USDC x402 micropayments' for MCP endpoints, lists four distinct modes with clear explanations, and differentiates from siblings via unique data sources (internal telemetry + on-chain + Bazaar).

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

Usage Guidelines4/5

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

Clear context for when to use each mode (e.g., facilitator_stats for settlement statistics, endpoint_intel for per-endpoint analytics), but does not explicitly say when not to use this tool versus sibling x402 tools (e.g., x402_payment_fraud_detector).

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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