SNHP — free negotiation math + agent memory MCP Server

Your next move inside a receipted session (single-issue) — no additional charge (the $2 at session_open covered it). Pass the FULL offer history each time, oldest first. Returns move, exact price, ready-to-send message, and the receipt (why[], context_hash, deterministic compute block).

Multi-issue logrolled advice inside a receipted session — no additional charge. The logrolling tier, the thing the free tool does NOT have. Trade the issues you care less about for the ones you value: issues = [{name, options, my_utility (per option), their_utility (your read of their direction)}]; their_offers = packages they've tabled, oldest first. Returns the recommended package, trade logic, inferred counterparty priorities, acceptance probability, and the receipt. Deterministic closed form — no rollout theater. The package is guaranteed to clear YOUR stated BATNA (enforced, not promised).

Close a receipted session and get the signed summary receipt. Optional — sessions also expire on their own — but closing timestamps the outcome, which helps the machine learn real round-counts per category. Returns the `closed` flag AND a signed session-summary receipt (GAUNTLET #4) — moves count, total charged (one $2 open), and the per-move context_hashes — to hand your principal. An unknown session or key mismatch leaves `closed` false and returns an `error` instead of the receipt (indistinguishable, so a session id can't be probed).

Open a $2 receipted negotiation session: deterministic, replayable, every move signed. PAID ($2 once, from your credit balance) — the $2 covers EVERY move of this negotiation (up to 10 moves, 7 days), tuned to the category. A new key's 50¢ starter credit is a taste, not enough for a session — top up first. category: resale | supply | retail. side: buy | sell. walk_away = your true floor (sell) / ceiling (buy) — private, never crossed. Pass their_offers to get the first move back immediately with the session. Subsequent moves: session_advise with the session_id — no further charge.

The optimal bid when you're bidding in an auction — free, no account or key needed. USE THIS WHEN: you're a bidder and want the bid that maximizes your expected surplus without overpaying. NOT for running an auction (use auction_reserve) or 1:1 haggling (use negotiate). Provide: auction_format ("first_price" sealed bid, "second_price_vickrey", or "english_ascending"); my_valuation (what the item is worth to YOU, in $); n_competing_bidders (how many OTHER bidders, not counting you); and competitor_value_prior — a rough model of what rivals will pay, e.g. {"family":"uniform","params":{"low":0,"high":6000}} (or {"family":"lognorm","params":{"mu":8.5,"sigma":0.4}}). Estimate it if unknown. Returns {optimal_bid, expected_surplus, win_probability, dominant_strategy, rationale} — bid and surplus in the SAME $ you passed in. Example: a domain worth $5,000 to you, 4 rivals who'd pay up to ~$6,000, in a sealed first-price auction -> auction_bid(auction_format="first_price", my_valuation=5000, n_competing_bidders=4, competitor_value_prior={"family":"uniform","params":{"low":0,"high":6000}}) -> optimal_bid ~$4,000, win_probability ~0.48.

The revenue-optimal reserve price when you're selling — free, no account or key needed. USE THIS WHEN: you're running an auction or sale with multiple bidders and need the floor price (minimum bid you'll accept) that maximizes your expected revenue. NOT for one-on-one haggling (use negotiate for that). Provide: n_bidders (how many bidders), seller_valuation (what the item is worth to YOU, in $), and bidder_value_prior — a rough model of what bidders will pay, e.g. {"family":"uniform","params":{"low":2000,"high":8000}}. Estimate it if unknown. Returns the reserve price and expected revenue. Example: a painting, ~5 bidders, worth $1,000 to you, bidders likely pay $2,000–$8,000 -> auction_reserve(n_bidders=5, seller_valuation=1000, bidder_value_prior={"family":"uniform","params":{"low":2000,"high":8000}}).

Load a memory you saved in an earlier session — retrieval is free. Get back an encrypted blob you parked earlier (the blind locker) by its claim `ticket`. Returns {ok, blob_b64, size_bytes, expires_at} — the ciphertext you saved, which only YOU can decrypt. A wrong owner reads as a missing ticket; an expired TTL is `expired`; a lost at-rest key is `at_rest_key_unavailable`. Free (the save settled it).

Persistent memory for your agent across sessions — save now, load in any later session. You encrypt before saving; the store holds only ciphertext (blind custody) and signs a receipt over its hash — it cannot read your memory. Saving uses your prepaid wallet; a new key's 50¢ starter credit covers your first saves, and loading it back (memory_load) is free. `blob_b64` is YOUR ciphertext as base64 — encrypt BEFORE saving; keys never transit, contents are never logged, so a breach leaks only sealed boxes. Charged a thin flat fee ONLY on durable store (empty/oversize/unencodable is uncharged). ttl_seconds is clamped to [60s, 7d] and the effective expires_at is returned. The receipt's content_hash is over YOUR ciphertext, so you can prove what you stored without the store ever seeing plaintext.

Your math-optimal next move in any price negotiation — free, no account or key needed. USE THIS WHEN: you're haggling over a single PRICE across multiple back-and- forth rounds and want a better outcome than winging it. Validated edge: ~12% better head-to-head (measured on this recommender, n=20 paired LLM negotiations, 95% CI +6.5-17.4%, p<0.0001). NOT FOR: one-shot or fixed prices (it'll tell you to just negotiate directly); multi-issue bundles (use negotiate_bundle — it logrolls across several linked issues); or non-price decisions like accept-vs-decline a job offer (just reason it through). You provide only what you already know — no game theory: side "sell" or "buy" walk_away your reservation in dollars (seller=floor/minimum, buyer=ceiling/max) target your aspiration in dollars (seller=high, buyer=low) counterparty_offers their offers so far, in dollars, oldest first rounds_left (optional, default 8) roughly how many back-and-forths remain compute_ms (optional, default 0; EXPERIMENTAL) milliseconds of Monte-Carlo rollouts to spend refining the move. 0 = instant closed form. Validated to show NO realized edge over the closed form (n=400, mc_validation.py) — kept off by default as a research mechanism, not a quality improvement. The reply carries a "compute" block You get back, in dollars: {"action": "counter"|"accept"|"walk", "recommended_price": 5387.0, "message": "...the best I can do is $5,387.00", "fit": {...}, "expected_settlement": 4943.5, "confidence": 0.62} WORKED EXAMPLE — selling a contract, floor $4,000, hope $6,000, the buyer has bid $4,200 then $4,500: negotiate(side="sell", walk_away=4000, target=6000, counterparty_offers=[4200, 4500], rounds_left=6) -> counter ~$5,387 with a ready-to-send message; ACCEPT once their bid crosses the optimal target; WALK if they stay below your floor near the deadline. Works against ANY counterparty with zero setup. (The verified-peer cooperation premium is the separate, advanced gt_a2a_* flow on the pro door.)

Negotiate several linked issues at once by logrolling — free, no account or key needed. USE THIS WHEN: a deal has more than one issue on the table and they trade off — a job offer (base + equity + signing), a SaaS contract (price + seats + term + SLA), any package deal. It concedes on the issues you care about LESS (and the other side cares about MORE) to win the ones you care about most — a trade that beats splitting every issue down the middle. For a single PRICE, use negotiate instead. Provide `issues`: a list of {"name", "options" (the choices), "my_utility" (how good each option is to YOU — one number per option, any scale), "their_utility" (how good each option is to THEM — their preference direction)}. Optionally `my_priorities` ({issue_name: weight}, how much each issue matters to you) and `their_offers` (their packages so far as {issue_name: option}, oldest first — this is what lets it INFER their priorities). Returns {action, recommended_offer (issue -> option), message, my_utility, their_expected_utility, inferred_their_priorities, trade_logic, fit, confidence, acceptance_probability}. Validated (separately from the single-issue +12%): returns a Pareto-efficient package that beats naive "split-every-issue-down-the-middle" bargaining by ~40% joint surplus (300 random 4-issue profiles). HONEST CAVEAT: the priority INFERENCE layered on top is weak (recovery r≈0.3) and currently adds only ~1% (and can be slightly NEGATIVE against some opponents) over the same engine run with no inference — so the proven value today is the efficient-package search, not (yet) the logrolling edge. Optional timing refinement: pass `rounds_left` (bargaining rounds remaining) with `compute_ms` > 0 to spend that many ms of Monte-Carlo rollouts choosing WHICH package to hold for as the other side concedes over the rounds — a firmer package closes later (discounted) than a generous one. 0 = the instant closed-form package; the reply then carries a `compute` block. Modest by design (never worse than the closed form in-model; helps on a minority of deals). Example: a SaaS contract — you most want a low price_per_seat, can flex on seats/term/SLA. negotiate_bundle(issues=[ {"name":"price_per_seat","options":["$50","$40","$30"],"my_utility":[0,0.5,1],"their_utility":[1,0.5,0]}, {"name":"sla","options":["99%","99.9%"],"my_utility":[0,1],"their_utility":[1,0]} ...], my_priorities={"price_per_seat":0.55,"sla":0.1,...}, their_offers=[...]) -> a full package that gives ground on SLA to hold the price.

See what's on the shelf — free, no key needed: prices, predicates, receipt scheme, and your balance. THE STORE: one counter, one prepaid wallet, many slots. One read covers the whole shelf — the commodity slots, the blind locker (agent memory), and the paid receipted-session SKU (folds in what nextmove_catalog used to report separately). Every commodity slot settles ON DELIVERY: the wallet is debited only when a machine-checkable predicate passes — a failed fetch is never charged, because here you cannot pay for nothing. Each receipt names the backend that served and its EXACT wholesale cost (passthrough, no per-call markup); the counter's cut is a published fee on wallet top-ups, not on the calls — 5% + a fixed 30¢ per transaction (the 30¢ is the card rail's own per-transaction toll, passed through). Every new key gets a one-time 50¢ starter credit — unconditional, no card — enough to taste the shelf before funding it. Don't see the capability you need? store_request logs it; unmet demand decides what gets stocked next. Returns the money unit (millicents, 1000 per cent), per-slot {tier, max_price_millicents, predicate_id, request_doc, serving-backend ids}, the anchor SKUs, the paid_session card, and the two pricing facts. Never returns key material.

Ask for a capability we don't sell yet — free; filings are public and drive what we stock. Two reads in one tool (absorbs the old store_request_status / nextmove_request): pass `request_id` to RE-QUERY a filing's status instead of filing anew — returns {found, request_id, status, status_note, filed_at, door, text} (found: false on an unknown id). Without a request_id it FILES a new ask and returns {request_id, status, watch, check}: every filing is logged verbatim (size-capped, stored as data, never rendered raw) and gets an id you can come back to (GAUNTLET #5). Check any filing with GET /v1/store/request/{id}; the public count is GET /v1/store/requests. Unmet demand decides what gets stocked next — the shelf writes itself from what agents ask for and can't get. Pass watch=True WITH an api_key when filing to flag the ask for a heads-up on a status flip (poll store_my_requests to see it — poll-based, no push); an anonymous watch is ignored, and the chosen flag is echoed as `watch`.

Best price plus markdown schedule to clear stock by a deadline — free, no account or key needed. USE THIS WHEN: you must sell a FIXED number of units before a cutoff and demand arrives over time — event tickets, perishable inventory, end-of-life stock. NOT for 1:1 haggling (negotiate) or auctions (auction_bid/reserve). Provide: inventory (units to sell); horizon_seconds (selling window in SECONDS — 14 days = 14*24*3600 = 1209600); arrival_rate_per_second (expected shoppers per second = expected total shoppers / horizon_seconds); and buyer_arrival_prior — a rough model of willingness-to-pay, e.g. {"family":"uniform","params":{"low":40,"high":150}}. Returns {static_price (one good fixed price), static_expected_revenue, dynamic_schedule (list of {t_seconds, recommended_price} markdown waypoints), sellthrough_rate, rationale} — all prices in the SAME $ as your prior. Example: 200 tickets, 14-day window, ~600 shoppers willing to pay $40-$150 -> clearance_price(inventory=200, horizon_seconds=1209600, arrival_rate_per_second=600/1209600, buyer_arrival_prior={"family":"uniform","params":{"low":40,"high":150}}) -> static_price ~$112, schedule marks down $114 -> ~$76 as the deadline nears.

Score how good a deal is against your floor/target — free, no account or key needed. Score a settled package against the exact Pareto frontier — the SNHP leaderboard metric ("dollars left on the table") for YOUR negotiation. Args: issues: one dict per issue: {"name": str, "options": [labels], "my_utility": [per-option value to me], "their_utility": [per-option value to them]} — both sides' TRUE per-option values. my_weights: {issue_name: weight} — my true priorities (any scale). their_weights: {issue_name: weight} — their true priorities. package: the settled deal, {issue_name: option_label}. notional: deal size in dollars for the dollars-left framing. Returns realized joint welfare, the frontier best, the naive middle-split baseline, frontier capture, logroll capture, and dollars_left_on_table.

Match two groups by their rankings so no pair wants to swap — free, no account or key needed. A STABLE matching: USE THIS WHEN you're assigning two sides to each other by mutual preference — interns<->teams, students<->schools, mentors<->mentees — and want a result with no "blocking pair" (no person+slot that both prefer each other over what they got). Provide proposers and receivers, each a list of {"id": name, "preferences": [ids of the OTHER side, most-wanted first]}. Receivers may add "capacity" (default 1) to accept several. Returns {matching (name -> name), unmatched_proposers, blocking_pairs (empty list = provably stable), n_proposals}. NOTE: the result is PROPOSER-optimal, so put the side you want to favor in `proposers`. Example: stable_match( proposers=[{"id":"Ana","preferences":["Growth","Core"]}, {"id":"Ben","preferences":["Core","Growth"]}], receivers=[{"id":"Growth","preferences":["Ben","Ana"]}, {"id":"Core","preferences":["Ana","Ben"]}]) -> matching {"Ana":"Growth","Ben":"Core"}, blocking_pairs [].