io.github.HrishiKabra/voting-mcp
Social-choice voting rules (Borda, Copeland, Condorcet, STV, opinion pool) as MCP tools.
Open source Open in the app JSON README (API)
About
Social-choice voting rules (Borda, Copeland, Condorcet, STV, opinion pool) as MCP tools.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- hrishikabra
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.3
- Last push
- 2026-07-02T07:20:03Z
- Repository state
- ativo
- Language
- Python
- Added
- 2026-08-29 03:01:57
- Updated
- 2026-08-29 03:01:57
- Origin id
io.github.HrishiKabra/voting-mcp
README
# voting-mcp
<!-- mcp-name: io.github.HrishiKabra/voting-mcp -->
**Principled social-choice aggregation as MCP tools — with a benchmark that measures the
accuracy lift over naive majority vote.**
Almost every multi-agent system aggregates votes with `Counter(votes).most_common(1)`, throwing
away preference order and confidence. `voting-mcp` ships the real rules (Borda, Copeland,
Condorcet, approval, STV, linear opinion pool) as callable MCP tools — each with its known
axiomatic behavior and explicit, documented tie-breaking — plus a reproducible benchmark that
aggregates a diverse ensemble of LLMs on a reasoning set and reports accuracy with bootstrap
confidence intervals.
The server is **pure compute**: stdio transport, no network, no file writes, no secrets — clean
against the OWASP MCP Top 10 by construction.
## Install
```sh
# run the server directly (once published)
uvx voting-mcp
# or from source
git clone https://github.com/HrishiKabra/voting-mcp && cd voting-mcp
uv sync
uv run python -m voting_mcp.server
```
Add it to an MCP client (e.g. Claude Desktop `claude_desktop_config.json`):
```json
{
"mcpServers": {
"voting": { "command": "uvx", "args": ["voting-mcp"] }
}
}
```
## Tools
Every tool takes a `profile` (`{candidates, ballots}`) and returns a `Result` with the full
co-winner set (`winners`, so ties are never hidden), the single tie-broken `winner` (or `null`
when none exists), a `ranking`, per-candidate `scores`, and a `note`.
| Tool | Ballots | Notes |
|------|---------|-------|
| `borda` | rankings | positional; Condorcet-inconsistent, clone-sensitive |
| `copeland` | rankings | Condorcet-consistent pairwise (+1 win, +0.5 tie) |
| `condorcet` | rankings | returns the pairwise winner **or an explicit no-winner on a cycle** |
| `approval` | approval sets | most-approved wins |
| `stv` | rankings | single-winner instant-runoff; clone-resistant |
| `opinion_pool` | distributions | linear pool — **preserves confidence, not an argmax vote** |
| `plurality` | rankings | baseline (most first choices) |
| `majority` | rankings | strict >50% or **no winner** |
| `aggregate_rule` | any | dispatch by a `rule` enum |
Tie-breaking is an explicit parameter (`lexicographic` default, `none`, or seeded `random`).
## Benchmark
Aggregate an ensemble of 5 models (one OpenAI-compatible client via OpenRouter) on
ARC-Challenge and compare each rule to the naive majority vote:
```sh
uv sync --extra bench
uv run python -m bench.fetch_arc --limit 200
# prints a cost estimate and STOPS; add --yes to actually call the API, --mock for a free dry run
uv run python -m bench.run_ensemble --dataset bench/datasets/arc_challenge.jsonl --limit 200 --yes
uv run python -m bench.compare --dataset bench/datasets/arc_challenge.jsonl --limit 200
```
Every raw response is cached under `bench/results/raw/`; re-runs never re-call the API, so
aggregation tweaks are free.
### Results
5-model ensemble (gpt-4o-mini · gemini-2.5-flash-lite · deepseek-v3 · claude-haiku-4.5 ·
glm-4.7), n = 200, bootstrap 95% CI. Two datasets of different difficulty; full write-up and
both plots in [`RESULTS.md`](RESULTS.md).
**MMLU-Pro (hard, baseline 73.5%) — the informative case:**
| Rule | Accuracy | 95% CI | paired Δ vs majority | p |
|------|---------:|:------:|:--------------------:|---|
| **opinion_pool** | **0.755** | [0.695, 0.815] | **+0.020** [−0.011, +0.052] | 0.225 |
| **majority_vote (baseline)** | 0.735 | [0.679, 0.788] | — | — |
| approval | 0.701 | [0.640, 0.757] | −0.035 [−0.063, −0.006] | **0.014** |
| stv | 0.693 | [0.630, 0.750] | −0.043 [−0.072, −0.015] | **0.002** |
| copeland | 0.647 | [0.580, 0.710] | −0.088 [−0.127, −0.052] | **<0.001** |
| condorcet | 0.620 | [0.550, 0.685] | −0.115 [−0.155, −0.079] | **<0.001** |
| majority (strict) | 0.590 | [0.520, 0.655] | −0.145 [−0.189, −0.105] | **<0.001** |
| borda | 0.472 | [0.405, 0.540] | −0.263 [−0.323, −0.206] | **<0.001** |
Δ is tested with a **paired bootstrap** on the per-question accuracy difference (same questions,
so shared difficulty cancels), not by eyeballing the independent CIs.
**The finding (honest):** the value isn't "fancy voting beats majority." It's that **the
confidence-preserving rule (`opinion_pool`) wins** when the crowd is uncertain (+2.0pp, the only
rule above baseline — suggestive but not significant at n=200, paired p=0.225), while **forcing
the distributions into full rankings actively hurts, significantly** — every ranking rule is
below baseline at paired p≤0.014, and `borda` collapses to 0.472 because with 10 options the
tail of the ranking is mostly noise. Aggregate the confidence; don't throw it away. On
**ARC-Challenge** (baseline 96.8%, near-ceiling) nothing separates — no rule differs
significantly. See [`RESULTS.md`](RESULTS.md).
## Develop
```sh
uv run pytest -q
uv run ruff check .
uv run mypy --strict src
# exercise the tools in the MCP Inspector:
npx @modelcontextprotocol/inspector uv run python -m voting_mcp.server
```
> Note: if you keep this repo under an iCloud-synced folder (e.g. `~/Desktop`), iCloud can spawn
> duplicate `.pth` files that intermittently break the editable install. Tests use
> `pythonpath=src`; run the server with `PYTHONPATH=src` if an import fails, or move the repo
> off the synced folder.
## Related research
The choice of rules here grows out of the author's work on voting-rule design:
[*Optimizing Voting Rules for Social Welfare and Beyond*](https://dl.acm.org/doi/10.65109/SSLT9308)
(AAMAS). That line of work asks which aggregation rules maximize welfare given how voters
actually express preferences; this project applies the same lens to LLM ensembles — where the
benchmark's answer is that confidence-preserving aggregation (the linear opinion pool) is what
pays off, and forcing cardinal beliefs into ordinal rankings destroys signal.
## License
MIT