claude mcp add snhp -- uvx snhp{
"mcpServers": {
"snhp": {
"command": "uvx",
"args": ["snhp"]
}
}
}MCP Servers overview
# SNHP
[](https://smithery.ai/servers/ryuxik/snhp)
**Free negotiation math for AI agents. One call, no account.** Your agent brings
the LLM; SNHP brings the game theory — your math-optimal next move in any
negotiation, single-price *and* multi-issue, LLM-free, runs locally. When you
need it **on the record**: $2 receipted sessions. When you need it to
**remember**: agent memory (blind custody — you encrypt before saving; we store
only ciphertext and cannot read it).
[](https://pypi.org/project/snhp/)
[](LICENSE)
· [snhp.dev](https://snhp.dev) · [Manifesto](MANIFESTO.md)
## 🏆 The Negotiation Leaderboard
**[arena.snhp.dev/leaderboard.html](https://arena.snhp.dev/leaderboard.html)** —
which AI walks away with the most money? Claude models, a naive
splitter, a genome evolved in a live sim, and community bots all negotiate the
same held-out multi-issue deals against the SNHP engine, scored against the
exact Pareto frontier. Every match is a real recorded negotiation, replayable
in the browser. Headline result: **frontier models, solo, lose to the naive
split-the-difference bot** — wired to the engine mid-deal, they're near-optimal.
**Put your bot on the board:** expose one HTTP endpoint speaking
[`snhp-gauntlet/1`](https://arena.snhp.dev/submit.html) and DM
[@ryuxik](https://x.com/ryuxik) the URL. The runner lives in
[`arena/gauntlet/`](arena/gauntlet/) — protocol, seats, scoring, and the
[25-line starter bot](arena/gauntlet/examples/demo_bot.py). Machine-readable
spec: [arena.snhp.dev/llms.txt](https://arena.snhp.dev/llms.txt).
## Install
```bash
uvx snhp # zero-install: runs the stdio MCP server on demand
# or
pip install snhp
```
Wire it into any MCP client (Claude Desktop, Cursor, Cline, …):
```json
{ "mcpServers": { "snhp": { "command": "uvx", "args": ["snhp"] } } }
```
Or call the math directly — plain dollars in, the move out (the `negotiate` tool):
```python
from gametheory.negotiation.plain_terms import negotiate_turn
negotiate_turn(
side="sell", walk_away=4000, target=6000,
counterparty_offers=[4200, 4500], rounds_left=6,
)
# -> {'action': 'counter', 'recommended_price': 5752.2,
# 'message': 'Thanks for the offer. The best I can do on this is $5,752.20.', ...}
```
Multi-issue deals logroll automatically — SNHP infers the other side's priorities
and proposes the package that maximises joint surplus (concede what you value
least to hold what you value most):
```python
from gametheory.negotiation.bundle import negotiate_bundle
negotiate_bundle(
issues=[
{"name": "price", "options": [100, 120, 140], "my_utility": [1.0, 0.5, 0.0], "their_utility": [0.0, 0.5, 1.0]},
{"name": "support", "options": ["basic", "priority"], "my_utility": [1.0, 0.0], "their_utility": [0.0, 1.0]},
],
my_priorities={"price": 0.8, "support": 0.2},
)
# -> recommended_offer {'price': 100, 'support': 'priority'} + the trade logic behind it
```
Hosted agent card, streamable MCP, and a live demo: **[snhp.dev](https://snhp.dev)**.
## What's here
```
snhp/ Core algorithm + NegMAS agent + B2B tournament harness
gametheory/ Productization layer (FastAPI, MCP, Tier 1/2/3 endpoints)
gametheory/negotiation/ Plain-terms single- + multi-issue (logrolling) engines
gametheory/server/ HTTP + MCP entry points
gametheory/tests/ pytest suite
SNHP_Whitepaper/ Protocol description + 3 component PRDs
```
## Develop from source
```bash
git clone https://github.com/ryuxik/snhp && cd snhp
python -m venv venv && source venv/bin/activate
pip install -e ".[test]"
python -m pytest gametheory/tests/ # test suite
uvicorn gametheory.server.http:app --reload # local API (catalog at /v1/catalog)
snhp # stdio MCP server
```
## Empirical anchor
### Two different numbers — keep them straight
There are two distinct measurements; conflating them is the easy mistake.
**1. Head-to-head competitive margin (the product-relevant number).** In a
SNHP-scaffolded LLM vs a non-SNHP LLM, how much more of the surplus does the SNHP
side capture? On the committed cross-vendor run (`gametheory/server/static/e6_cross_vendor.json`,
Sonnet+SNHP vs Haiku, n=20 paired seeds) the pooled margin is **~+12.5%**
(`mean h3_margin ≈ 0.125`, 29/40 positive signs). This is the number the shipped
tools cite as "~12% better head-to-head." Caveats: n=20, LLM-vs-LLM, single-issue
price, and the opponent is a *general* vanilla prompt — see the strong-baseline
note below.
**2. Joint-welfare lift in self-play (a cooperation metric, NOT the same thing).**
Two-Sonnet B2B contract negotiation, n=20 paired seeds:
| Condition | Joint welfare (frontier ≈ 1.57, estimated) |
|---|---:|
| Vanilla Sonnet (general prompt, no SNHP) | 1.40 |
| Pure SNHP-vs-SNHP (math only) | 1.45 |
| **Sonnet + SNHP MCP tool (both sides)** | **1.59** |
| Haiku + SNHP MCP tool (cross-model) | 1.61 |
Lift from both sides adopting the SNHP tool: **+0.186 joint welfare**, sign test
18/20, **p=0.0004**. (The 1.59/1.61 slightly exceed the 1.57 frontier *estimate* —
the frontier was estimated on a coarse grid, so treat these as "at the frontier,"
not "beyond it.") Cost: $0.025 per matchup at 2026-04 pricing.
### 3. The build-vs-buy test: SNHP vs a STRONG production prompt
Both numbers above are vs a *general* vanilla prompt. The sharper question — "why not
just prompt the LLM well?" — is answered by running SNHP against a strong production
prompt (`snhp/llm_strong_baseline.py`, whose system prompt even includes logrolling
advice). On the 4-issue contract, Haiku+SNHP-tool vs Haiku+strong-prompt, n=12 paired
seeds (`python -m snhp.strong_baseline_headtohead`, result committed at
`gametheory/server/static/strong_baseline_headtohead.json`):
| Metric | Value |
|---|---|
| Utility margin (SNHP − strong baseline) | **+0.077**, 95% CI **[+0.039, +0.115]** (excludes 0) |
| SNHP share of joint surplus | **54%** (CI [52%, 56%]) |
| Sign test | **8/12 positive, 0 negative** |
SNHP beats even a strong production prompt — but by roughly **half** the edge it shows
against a weak one. Caveats: n=12, Haiku (not Sonnet), one contract domain; re-run at
larger n / a stronger model to tighten the CI.
**Network effect**: the cooperation premium requires both sides to be
SNHP-staked. Asymmetric matchups (Sonnet+SNHP vs vanilla Sonnet) lose 0.11
utility vs symmetric scaffolded play. Peer-mode advisor only fires when
counterparty has posted a verifiable SNHP attestation.
Live demo (replay of the actual API trace at seed=42): https://snhp.dev/demo.html
### Tournament rank (honest, per-market)
In the committed round-robin (`leaderboard/results/leaderboard.json`, `n_rounds=20`),
SNHP's rank by average utility depends on the market:
| Market (BATNA) | SNHP rank | Top of field |
|---|---|---|
| Buyer's market (asymmetric) | **#1 of 21** | SNHP 0.508 |
| Seller's market (asymmetric) | **#1 of 21** | SNHP 0.520 |
| Symmetric (neutral) | **5th of 21** | Logroller 0.525, The Closer, Cialdini, Principled, then SNHP 0.512 |
So SNHP is #1 **in the asymmetric markets** and **mid-pack in the symmetric one** —
do not read this as "#1 overall." Its variance is the smallest in the field. At
`n_rounds=100` the symmetric field restabilizes further and Aspiration leads.
This NegMAS agent (`snhp/negmas_agent.py`) is a **research artifact and is NOT the
shipped product recommender** — the product claims below are measured on the
shipped code, not on this tournament.
See `gametheory/evals/README.md` for the eval/tuning runbook.
## Tiers
- **Tier 1 — Negotiation**: sell-side + buy-side recommenders, anchor-attack
detection, cryptographic first-strike commit-reveal, LLM-drafted reply
emails (paid).
- **Tier 2 — Auctions**: Vickrey / first-price BNE / English ascending,
Myerson optimal reserve, format recommendation, MC simulation.
- **Tier 3 — Mechanism design**: Gale-Shapley, asymmetric Myerson optimal
auction, Gallego-van Ryzin posted-price.
Tier 4 (coalition games) deferred until a paying buyer asks for it.
---
mcp-name: io.github.ryuxik/snhp-negotiation
What people ask about snhp
What is ryuxik/snhp?
+
ryuxik/snhp is mcp servers for the Claude AI ecosystem with 0 GitHub stars.
How do I install snhp?
+
You can install snhp by cloning the repository (https://github.com/ryuxik/snhp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is ryuxik/snhp safe to use?
+
ryuxik/snhp has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.
Who maintains ryuxik/snhp?
+
ryuxik/snhp is maintained by ryuxik. The last recorded GitHub activity is from today, with 0 open issues.
Are there alternatives to snhp?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
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