mcp-proxyml 0.1.2

MCP server for the ProxyML API

mcp-proxyml

MCP server for the ProxyML API. Gives Claude (and other MCP clients) direct access to ProxyML's surrogate modelling and explainability tools.

Prerequisites

A ProxyML API key. Sign up at proxyml.ai.

Installation

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "proxyml": {
      "command": "uvx",
      "args": ["mcp-proxyml"],
      "env": {
        "PROXYML_API_KEY": "your-api-key-here"
      }
    }
  }
}

The config file is at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Then restart Claude Desktop.

Claude Code

claude mcp add-json proxyml '{"command":"uvx","args":["mcp-proxyml"],"env":{"PROXYML_API_KEY":"your-api-key-here"}}'

Other MCP clients

pip install mcp-proxyml
PROXYML_API_KEY=your-key mcp-proxyml

Environment variables

Variable	Required	Description
PROXYML_API_KEY	Yes	Your ProxyML API key

Tools

Schema

Tool	Description
proxyml_infer_schema	Infer a feature schema from a local CSV file — no data sent to the server
proxyml_get_schema	Retrieve a stored schema by name
proxyml_put_schema	Upload or replace a feature schema

Training

Tool	Description
proxyml_synthesize_data	Generate synthetic samples from the stored schema
proxyml_train_surrogate	Train a linear surrogate on samples scored by your model
proxyml_list_surrogates	List trained surrogate models, newest first
proxyml_predict_batch	Get surrogate predictions for a list of instances

Explainability

Tool	Description
proxyml_get_summary	Feature importances and model summary
proxyml_export_surrogate	Full coefficient export for audit and governance
proxyml_explain_local	Per-feature contribution breakdown for a single instance
proxyml_explain_local_batch	Per-feature contributions for multiple instances in one call
proxyml_find_counterfactual	Find the nearest point that flips the prediction
proxyml_diff_models	Compare feature importances between two surrogate versions

CI/CD

Tool	Description
proxyml_detect_drift	Compare two versions and return a structured pass/fail against coefficient and fidelity thresholds

Account

Tool	Description
proxyml_get_usage	Current tier, request count, and quota — useful as a pre-flight check

Typical workflow

1. proxyml_infer_schema      — point at a CSV, get a schema back
2. proxyml_put_schema        — upload it
3. proxyml_synthesize_data   — generate synthetic samples
4. [score samples with your model]
5. proxyml_train_surrogate   — send samples + predictions, get a surrogate
6. proxyml_get_summary       — see which features drive predictions
7. proxyml_explain_local     — explain a specific decision
8. proxyml_find_counterfactual — find what would need to change

Steps 1–2 are one-time setup. Steps 3–5 can be repeated to retrain as your model changes; use proxyml_diff_models to compare versions.

Agentic workflows

Drift detection in CI/CD

proxyml_detect_drift is designed for use in deployment pipelines. It wraps proxyml_diff_models and applies thresholds to produce a structured pass/fail:

On model deployment:
1. proxyml_train_surrogate          — train surrogate on new model version
2. proxyml_detect_drift(a, b)       — compare against previous version
   → passed: false                  — block deployment or flag for review
   → passed: true                   — proceed

Thresholds can be tuned per use case:

proxyml_detect_drift(
  version_a="<previous>",
  version_b="<new>",
  coefficient_threshold=0.15,   # tighter for high-stakes models
  fidelity_threshold=0.02
)

Dev model validation without production data

Validate a model trained in a lower environment by comparing its predictions against a surrogate trained on production data — no production data required in the dev environment.

This workflow requires a step the MCP server can't do on its own (scoring with your dev model), but works naturally in Claude Code where the agent can execute code directly:

1. proxyml_synthesize_data(num_points=100)   → synthetic samples
2. [agent runs: dev_predictions = dev_model.predict(samples)]
3. proxyml_predict_batch(samples)            → surrogate predictions
4. [agent computes MAE and compares to tolerance]

Example prompt for Claude Code:

Using ProxyML, validate my dev model against the production surrogate.
Synthesize 100 samples from the "default" schema, score them with my model
at dev_model.predict(), get surrogate predictions with proxyml_predict_batch,
then compute the mean absolute error and tell me whether it's within 0.1.

The surrogate acts as a proxy for production behaviour — if the dev model agrees with it within tolerance, it's likely behaving consistently with what was trained on real data.

Counterfactual investigation

When a model makes a decision that needs explaining — a rejected loan application, a flagged transaction, a declined insurance quote — chain proxyml_explain_local and proxyml_find_counterfactual to answer both "why?" and "what would need to change?":

1. proxyml_explain_local(instance)              → which features drove this decision
2. proxyml_find_counterfactual(instance, target) → nearest point that flips it

Example prompt:

My model rejected this application: [age=34, income=42000, loan_amount=15000, ...].
Using ProxyML, explain why it was rejected and find the minimum changes that
would result in an approval. Highlight which changes are realistic given that
age is immutable.

Claude will call proxyml_explain_local to surface the top contributing features, then proxyml_find_counterfactual with the target outcome, and interpret the difference in plain language.

Iterative surrogate improvement

When proxyml_train_surrogate returns a low fidelity warning or other training diagnostic, the agent can use it to guide the next iteration rather than stopping:

1. proxyml_train_surrogate(samples, predictions)
   → warning: "Surrogate fidelity is low (R²=0.52)..."
2. proxyml_synthesize_data(num_points=500)       — increase sample count
3. [re-score with model]
4. proxyml_train_surrogate(larger_samples, predictions)
5. proxyml_detect_drift(v1, v2)                  — confirm improvement, not regression

Example prompt:

Train a surrogate for my regression model using the "default" schema with 200
samples. If fidelity is below 0.7, keep doubling the sample count and retraining
until it passes or you reach 1600 samples. Use proxyml_detect_drift after each
retrain to confirm the model is improving rather than just changing.

The training warnings (convergence, sparsity, class imbalance, high correlation) are designed to be actionable — the agent can read them and decide whether to adjust num_samples, revisit the schema, or flag for human review.

Governance report

Claude can generate a governance report from existing tools without a dedicated endpoint. Example prompt:

Using ProxyML, generate a governance report for surrogate version <id>.
Include: task type, training date, fidelity metrics, top 5 features by importance,
any warnings from training, and a plain-English summary of what drives predictions.
Format it as a structured document suitable for attaching to a deployment ticket.

Claude will call proxyml_get_summary (and proxyml_list_surrogates to find metadata) and compose the report.