{"uid":"cap_Oj4rArgTHGuM1agILvFF9","slug":"pareto-optimal-negotiator-mcp-c229fd46","name":"Pareto Optimal Negotiator MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/pareto-optimal-negotiator-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.3","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.3/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_lekaY5idZ5_e56kwnJoh_","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.3","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Finds Pareto-optimal outcomes in multi-party negotiations by analyzing trade-offs and identifying mutually beneficial agreement zones.","exampleAgentPrompt":"We have two parties in a procurement negotiation — the buyer wants the lowest price and fastest delivery, while the supplier wants higher margins and longer lead times. Can you find the Pareto-optimal agreement point that maximizes joint value without making either side worse off?","exampleUseCases":[{"title":"Salary negotiation between employer and candidate","prompt":"A candidate is asking for $130k and full remote work, while the employer is offering $110k with 3 days in-office. Find the Pareto-optimal compromise that maximizes overall satisfaction for both sides without either being worse off than their walk-away point."},{"title":"Resource allocation across competing project teams","prompt":"Three internal teams are competing for a shared engineering budget of $500k. Team A needs $300k, Team B needs $250k, and Team C needs $200k. Can you find a Pareto-optimal allocation where no team can gain more budget without another losing critical funding?"},{"title":"Multi-party licensing deal optimization","prompt":"We have a software vendor, a distributor, and an end-user enterprise all negotiating terms on a licensing deal — price, support level, and contract length are all on the table. Help us identify the Pareto frontier of agreements where all three parties are as well-off as possible."}],"resultDescription":"Returns a structured analysis identifying the Pareto-optimal outcome(s) from the negotiation scenario, including recommended agreement terms, trade-off analysis, utility scores for each party, and an explanation of why the solution is Pareto-efficient (i.e., no party can improve without harming another).","failureModes":["Underspecified negotiation scenario with missing party preferences leads to incomplete analysis","Contradictory or infeasible constraints may produce an empty Pareto frontier","Highly complex multi-party scenarios may return approximate rather than exact optima","Missing utility weights or objective functions may cause the engine to assume equal weighting","Malformed input string may return a parsing error"],"whenToPreferThis":"Choose this endpoint when you need to find mathematically grounded, fair, and efficient negotiation outcomes across two or more parties with competing objectives — especially when you want to avoid zero-sum framing and instead identify mutually beneficial trade-off zones. Prefer it over simple rule-based arbitration when trade-off complexity is high or when you need to justify an outcome as objectively optimal.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:11:23.831Z","isFirstParty":false,"canonicalSlug":"pareto-optimal-negotiator-mcp-c229fd46"}