easydeploy-ai-mcp

Official

by EasyDeploy-AI

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Get prediction

get_prediction

Read-onlyIdempotent

Fetch prediction status and results by prediction ID, including class labels, probabilities, and batch download links for completed jobs.

Instructions

Fetch prediction status and result by prediction id.

Ad-hoc completed: output has the predicted label or value per row. For a classifier, probabilities has each row's probability_<class> values, in the same order.
Batch completed: download_url and curl_command are included automatically. curl_command is the HTTP GET that downloads the scored CSV through EasyDeploy's tokenized gateway; it carries a short-lived download token, never the API key.
Classifiers, ad-hoc and batch, also return classLabels: the label each <class> in probability_<class> stands for, such as {"0": "B", "1": "M"}. Regressors, models that give no probabilities, and predictions completed before these fields existed return neither field.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prediction_id`	Yes

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

A3.5/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real behavioral context: batch completions auto-include download_url/curl_command, the curl command carries a short-lived download token rather than the API key, and which fields are omitted for regressors. That is useful disclosure beyond the annotations.

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?

Purpose is front-loaded and the remaining detail is organized as bullet points. It is dense, but each bullet is a distinct behavior; no filler sentences.

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

Completeness4/5

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

For a single-parameter read tool with an output schema present, the description is more than complete; annotations cover safety and the return-shape detail is arguably redundant with the output schema but not harmful. Nothing needed to invoke it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 0%, so the description should compensate, but it only restates that the parameter is a 'prediction id' with no format, source, or constraint details. Marginal value over the bare schema.

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

Purpose4/5

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

States a specific verb and resource ('Fetch prediction status and result by prediction id'), which clearly separates it from list_predictions (enumerate) and run_prediction (execute). It does not explicitly name a sibling, but the verb+resource+key is unambiguous.

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

Usage Guidelines2/5

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

There is no when-to-use guidance: no mention of when to poll this versus list_predictions, no note that it requires a prediction id produced by run_prediction/run_batch_prediction, and no indication of behavior when the prediction is still running. The description offers no routing context.

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