XF Model Source MCP server

XFMS — Xpansion Framework Model Source

Pick the right LLM for your task — without the Twitter vibes.

State what you're using the model for. XFMS aggregates evidence from eight independent benchmark sources, normalizes it onto a common scale, lets your intent decide which dimensions matter, and returns a ranked shortlist with plain-English rationale for every pick.

XFMS is one module of the Xpansion Framework — a unified architecture for governing AI-assisted work.

What this repository is

A thin Python client and command-line tool for calling the hosted XFMS API at xfms.xpansion.dev. About 250 lines of code. It turns a one-liner into a ranked LLM shortlist.

What this repository is not: the recommender engine, the score catalog, or the ingestion pipeline. Those run on the hosted service. The methodology behind every pick is published in full at docs/methodology.md — every claim there maps to code that runs at request time, you just don't run it locally.

What you say:

"Fixing bugs in our Python codebase."

What you get:

Top picks:
   1. 0.842  GPT-5.5                 (openai/gpt-5.5)         via OpenAI
   2. 0.811  Claude Opus 4.7         (anthropic/claude-opus-4.7) via Anthropic
   3. 0.798  Gemini 3.1 Pro Preview  (google/gemini-3.1-pro-preview) via Google

Inferred quality weights from your purpose:
  • structured_output_reliability  42.0%  ← BigCodeBench, Aider Polyglot
  • instruction_following          28.0%  ← LiveBench, Tau-Bench
  • factuality                     20.0%  ← MMLU, GPQA
  • coherence                      10.0%  ← LongBench

─── Explanation ───
Picked GPT-5.5: strong on structured output and instruction following —
the two dimensions that dominate code-edit work. Beats Claude on Aider
Polyglot and matches it on LiveBench reasoning, at roughly 60% of the
per-token cost.

Want to see how the picks actually behave on your kind of query? Add --ab:

─── A/B probe ───
Ran 5 test queries against the top picks.
  • GPT-4o-mini  avg_latency=5579 ms  total_cost=$0.00156  successes=5
  • GPT-5.5      avg_latency=8190 ms  total_cost=$0.07640  successes=5
  • GPT-5.4      avg_latency=8783 ms  total_cost=$0.03493  successes=5

Commentary:
  Across 5 real test queries, GPT-4o-mini was both cheapest ($0.0016 total)
  and fastest (5579 ms avg). Clear winner — 98% cheaper and 36% faster
  than the slowest pick.

What XFMS does for you

Beyond ranking, XFMS gives you these levers to honor what you actually meant:

Shortened. The full README is on GitHub.