Voting MCP server

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

# 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 claudedesktopconfig.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.

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:

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.

MMLU-Pro (hard, baseline 73.5%) — the informative case:

Shortened. The full README is on GitHub.