easydeploy-ai-mcp

Official

by EasyDeploy-AI

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Get model report

get_model_report

Read-onlyIdempotent

Retrieve a model's EDA training report to review metrics, feature analysis, and performance summary after training. It waits if the report is still generating and can target a specific version.

Instructions

Load the EDA training report (metrics, feature analysis, performance summary).

project_id is optional — the backend resolves it from model_id when omitted.

Omit model_version_id to use the latest version for this model. Pass an id from list_model_versions to read a specific version.

Default response is summary only (token-efficient). Set full_report=true for full detail.

The report is generated after training finishes, so this tool waits for it: 45 s by default (the server's EDA_REPORT_MAX_WAIT_SECONDS, never more than 60 s), since some hosts end a tool call at 60 s. If it is still being generated then, the result has timed_out: true and next_steps: nothing failed, call get_model_report again to keep waiting.

How the model was validated. Model search scores candidates by cross-validation: 10-fold stratified shuffled CV on ROC-AUC for classifiers, 10-fold shuffled CV on negative MSE for regressors; a version created with time_series_mode uses forward-chaining CV (TimeSeriesSplit) instead. The winning pipeline is then refit on all rows. There is no separate test set. Accuracy, the confusion matrix, per-class precision/recall and in-sample ROC-AUC are training fit (computed on the same rows the model learned from), not a holdout estimate. The CV score is the report's out-of-sample estimate, but it is measured inside the training file; the holdout estimate comes from scoring your own test file with run_batch_prediction, not from this report. The prose summary is written by an LLM and may use looser wording than these numbers.

metrics (returned next to the report, passed through unchanged): {taskType, crossValidation: {metric, score, strategy, folds, strategySource ('recorded'|'platform_default'), note}, trainingFit: {note, accuracy?, rocAuc?, confusionMatrix?, perClass?, mse?, rmse?, mae?, r2?, explainedVariance?, mape?}, additionalCv?: {r2?, rmse?}, warnings: []}. metrics is null when it cannot be derived, with metricsUnavailableReason saying why. crossValidation.strategy names the CV used for that run (time_series_split for forward-chaining); read warnings before quoting any number.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model_id`	Yes
`project_id`	No
`full_report`	No
`model_version_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 29, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only/idempotent, but the description adds substantial non-obvious behavior: a 45s default wait (capped at 60s), the timed_out:true + next_steps contract on timeout, the null metricsUnavailableReason path, and an instruction to read warnings before quoting numbers. This is exactly the context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded and dense with no filler, and the operational rules come first. The extended paragraph on CV strategy and training-fit caveats is relevant for interpreting numbers but is long relative to the calling decision, slightly diluting an otherwise tight definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite an output schema existing, the description still specifies the metrics object shape, the strategySource enum values, and the null case, and it resolves the 'accuracy is training fit, not holdout' ambiguity that would otherwise cause misuse. Nothing an agent needs to call and interpret this tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description bears the full burden and does so: it explains that project_id is optional and backend-resolved, that omitting model_version_id yields the latest version, and that full_report trades tokens for detail. Only model_id is left implicit, but its meaning is self-evident from the report context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb and resource — 'Load the EDA training report' — and enumerates the contents (metrics, feature analysis, performance summary), which cleanly separates it from siblings like get_model, get_model_version, and get_training_status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit routing rules: omit model_version_id for latest, pass an id from list_model_versions for a specific version, project_id optional, full_report=true for detail, and that the real holdout estimate comes from run_batch_prediction rather than this report. An agent knows exactly when to call it and what to call instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.