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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
modeYesAnalytics mode: facilitator_stats=network-wide settlements | endpoint_intel=per-URL analytics | agent_caller_profile=per-wallet analytics | price_radar=price distribution by category
asyncNoIf 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.
categoryNoTool category for price_radar mode. Defaults to all.
period_daysNoLookback window in days (5-90, default 30)
endpoint_urlNoMCP endpoint URL for endpoint_intel mode (e.g. https://mcp.gapup.io/mcp)
wallet_addressNoEVM wallet address for agent_caller_profile mode (0x...)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
sourcesYes
price_radarNo
quality_scoreYes
endpoint_intelNo
facilitator_statsNo
agent_caller_profileNo

TDQS

A4.4/5.0
Behavior5/5

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

Annotations indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds critical behavioral details: cache TTL (30 min LRU), source timeout (8s), fallback behavior (CDP API vs RPC scan), and data source uniqueness. No contradictions with annotations.

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?

The description is well-structured with bulleted modes and front-loaded purpose. It is comprehensive but slightly verbose; every sentence adds value for a complex tool. Could be tightened slightly, but effectiveness justifies the length.

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 the tool's complexity (4 modes, multiple data sources, caching, env var), the description covers all necessary context: network, contract address, data sources, fallback, cache, timeout, and optional API key. An output schema exists, so return values are not required in the description.

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% with detailed parameter descriptions. The tool description does not add significant meaning beyond what the schema provides; it reiterates mode names but no per-parameter enrichment. Baseline of 3 is appropriate since schema already carries the load.

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?

The description clearly identifies the tool as providing real-time analytics on x402 protocol USDC micropayments, detailing four distinct modes (facilitator_stats, endpoint_intel, agent_caller_profile, price_radar) and unique data sources. It differentiates itself from siblings like x402_liquidity_monitor and x402_payment_flow_analyzer by focusing on aggregated intelligence with proprietary telemetry.

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?

The description implicitly guides usage by explaining each mode's purpose (e.g., 'facilitator_stats — network-wide settlements', 'endpoint_intel — per-URL analytics'). However, it lacks explicit 'when-not-to-use' or direct comparisons with sibling tools, which would improve clarity.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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