Fee Optimizer MCP
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- AlicenseAqualityCmaintenanceEnables AI agents to fetch real-time exchange trading fees, compare rebate-adjusted costs, obtain referral links, calculate trading costs, and identify compliant exchanges for crypto trading.684 npmMIT
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- FlicenseAqualityDmaintenanceEnables AI agents to compare AI model pricing plans, run cost scenarios, find break-even points, and get plan recommendations using TokenLens data.4-
- AlicenseAqualityCmaintenanceProvides live cryptocurrency market data from over 100 exchanges, enabling AI agents to fetch prices, order books, funding rates, and more for trading analysis and arbitrage opportunities.132MIT
- AlicenseNot gradedqualityCmaintenanceEnables real-time crypto market data, funding rates, cross-exchange arbitrage, portfolio analytics, exchange skills, and trading via your own connected exchange keys through natural language agent workflows.1MIT

OptimToken MCPofficial
AlicenseNot gradedqualityAmaintenanceEnables AI assistants to fetch live, dated prices for LLM models and cloud compute instances across providers, compare and recommend models, and estimate monthly costs based on workload-specific token shapes and constraints.MIT
TDQS
Scored across 19 tools
There is a large cluster of heavily overlapping 'compute a cross-venue cost' tools—compare_exchange_fees, compare_total_cost, calculate_annual_cost, calculate_savings, recommend_exchange, analyze_persona, compare_personas, compare_countries, and volume_what_if all return ranked fee comparisons with only subtly different lenses. The descriptions do cross-reference each other (e.g. 'for the all-in cost of a trade use compare_total_cost instead'), which helps, but an agent asked a generic 'which exchange is cheapest' question faces genuine selection ambiguity.
Nearly all tools follow a consistent snake_case verb_noun pattern (calculate_savings, compare_total_cost, get_funding_rates, analyze_persona, recommend_exchange). The only outlier is volume_what_if, which drops the verb prefix, but the set is otherwise predictable and readable.
At 19 tools the surface is on the heavy side for a fee-optimization server, and several tools appear consolidatable (e.g. compare_exchange_fees / compare_total_cost / calculate_annual_cost are variants of the same comparison). The breadth of the domain (multi-exchange, multi-cost-leg, personas, countries) justifies many of them, but 19 is borderline.
Coverage of the fee-optimization domain is exceptionally thorough: trading fees, funding rates, execution/spread, withdrawal fees, fiat on/off-ramps, token discounts, consumer-interface markups, stablecoin regional access, API-verified account tiers, referral links, data provenance, plus persona/country and what-if analysis. No obvious operational gaps or dead ends for the stated purpose.