--- license: mit language: - en tags: - tool-use - function-calling - mcp - model-context-protocol - benchmark - quantization - leaderboard pretty_name: QuantMCP Results --- # QuantMCP Results Real benchmark results for the [QuantMCP benchmark](https://github.com/Happynood/quant-mcp-bench), measuring how quantization degrades LLM function-calling reliability against real, unmodified [Model Context Protocol](https://modelcontextprotocol.io) server tool schemas — and whether that degradation matches what [QuantCall](https://huggingface.co/datasets/happynood/quantcall-results) already measured on curated BFCL/ToolACE schemas. Every row comes from an actual `quantmcp run` execution against a real, sandboxed MCP server — no fabricated or hand-edited numbers. ## Headline finding: Cross-Benchmark Consistency (CBC) **CBC (Spearman rho) = -0.755 (n=8 model×quant pairs, 3 model families)** — see `data/cbc.json`. QuantCall's BFCL-measured quantization degradation does **not** reliably carry over to real MCP schemas: the correlation is negative (degradation directions often flip), and it got *more* negative, not less, as more data was added. With 2 model families (Qwen3-0.6B, Llama-3.2-1B) the estimate was -0.551 (n=6) — itself the product of three increasingly-averaged computations that ranged -0.824 to -0.265 before stabilizing. Adding Qwen3-1.7B as a 3rd family (a real within-family size contrast against Qwen3-0.6B) moved it to -0.755 (n=8): the sign held, the magnitude strengthened. See [`docs/RUN_REAL.md`](https://github.com/Happynood/quant-mcp-bench/blob/main/docs/RUN_REAL.md) in the GitHub repo for the full convergence table and honesty caveats — n=8 is still far too few for a rigorous p-value on a Spearman correlation. ## Files | File | Grain | Description | |------|-------|--------------| | `data/raw_results/**/*.result.json` + `*.manifest.json` | one pair per real run | Every real run this project has produced (96 result files across 3 model families × 4 server tiers, with varying quant/repeat coverage per family — Qwen3-0.6B and Llama-3.2-1B at 4 quant levels with 3 independent repeats on tiers U1-U3, Qwen3-1.7B at 3 quant levels, single run, no fp16 (its bf16 weights don't fit a 4GB card at a usable context length — see `docs/RUN_REAL.md`)), each with a full manifest (git SHA, config/fixture hashes, hardware fingerprint). The `model` field is a portable `~/models/...` path, not a specific machine's absolute path. Since Phase 7, each file also carries a `instances` array (one entry per task instance, tagged with the tool it targeted) enabling the per-tool SCI regression below. | | `data/mcp_runs.csv` | one row per real run | Flattened, path-sanitized view of every `raw_results` file | | `data/mcp_tier_breakdown.csv` | one row per server tier | Mean SVR-MCP/TSR/η per tier, annotated with that tier's real Schema Complexity Index (SCI) | | `data/cbc.json` | one row per (model, quant) pair | Cross-Benchmark Consistency deltas against QuantCall's published BFCL numbers, plus the Spearman rho itself | | `data/sci_regression.json` | one row per live tool | Per-tool Schema Complexity Index (SCI) paired with its own Δ SVR-MCP (fp16 vs. Q4_K_M), plus an OLS slope and bootstrap 95% CI across all 38 covered tools — the statistically-powered version of the SCI-vs-degradation question that `mcp_tier_breakdown.csv`'s 4-tier view alone can't answer | ## Schema: `data/mcp_runs.csv` | Column | Type | Description | |--------|------|--------------| | `model` | string | Sanitized model name (local GGUF paths stripped to a canonical name — see `report/published.py::sanitize_model_name` in the GitHub repo) | | `quant` | string | Quantization level: fp16, Q8_0, Q5_K_M, Q4_K_M (Qwen3-1.7B: Q8_0/Q5_K_M/Q4_K_M only) | | `tier` | string | MCP server tier: filesystem, git, sqlite, or memory | | `n` | int | Number of task instances evaluated | | `svr_mcp` | float | Schema-Validity Rate against the real, live tool schema (SVR-MCP, spec §4.1) | | `tsr` | float | Task Success Rate — the call was schema-valid *and* produced the correct outcome (spec §4.2) | | `vram_gb` | float | Peak VRAM usage in GB for this run | | `eta` | float | Reliability-per-VRAM: `(0.5*svr_mcp + 0.5*tsr) / vram_gb` | | `pareto_optimal` | bool | Whether this (model, quant, tier) config sits on the reliability-vs-VRAM Pareto frontier | ## Schema: `data/cbc.json` ```json { "rho": -0.755, "n_pairs": 8, "table": [ {"model": "...", "quant": "...", "baseline_quant": "...", "delta_svr_bfcl": ..., "delta_svr_mcp": ...} ] } ``` `delta_svr_bfcl` is QuantCall's published BFCL SVR delta vs. that model's own baseline quant (fp16 for every family except Qwen3-1.7B, which uses Q8_0 — see `baseline_quant` per row); `delta_svr_mcp` is this project's equivalent delta on real MCP schemas (pooled across all four server tiers, weighted by task count). ## Schema: `data/sci_regression.json` ```json { "n": 38, "slope": 0.045, "intercept": ..., "slope_ci": [-0.064, 0.170], "points": [ {"tool": "...", "tier": "...", "sci": ..., "delta_svr": ..., "n_baseline": ..., "n_quant": ...} ] } ``` `slope`/`slope_ci` describe the OLS fit of Δ SVR-MCP (fp16 minus Q4_K_M pass rate, pooled across model families weighted by n) against each live tool's own SCI. The 95% CI is a percentile bootstrap over the (SCI, Δ) pairs, not a parametric estimate. ## How to Submit 1. Run the benchmark on your hardware following [docs/RUN_REAL.md](https://github.com/Happynood/quant-mcp-bench/blob/main/docs/RUN_REAL.md). 2. Verify your `result.json` contains a `manifest` block with a git SHA and fixture hash. 3. Open a PR on [GitHub](https://github.com/Happynood/quant-mcp-bench) adding your result file under `results/`. 4. Run `quantmcp leaderboard results/ --output-dir leaderboard/` and include the regenerated CSVs in your PR. ## Links - GitHub: https://github.com/Happynood/quant-mcp-bench - Eval suite: https://huggingface.co/datasets/happynood/quantmcp-suite - Leaderboard (Space): https://huggingface.co/spaces/happynood/quantmcp-leaderboard - Sibling project (curated-schema quantization benchmark): https://github.com/Happynood/quant-toolcall-bench