wyoloservice2_mcp (wisrovi/wyoloservice2_mcp) is an MCP server listed on the M8ven Trust Index. It scores 56 out of 100, grade D. It declares 8 tools. No publisher has claimed this listing.

Caution

56/100

wyoloservice2_mcp

Item: wyoloservice2_mcp
Rating: 56
Author: M8ven

Enables AI agents to connect to the NeuralForgeAI cluster for inspecting and controlling YOLO training jobs, including launching, monitoring, canceling studies, and verifying dataset paths.

→ wisrovi/wyoloservice2_mcp · listed on glama

Caution. Specific findings reduced this grade. They are listed on the page. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

wisrovi

Source: Glama

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings

⚠️

Tool descriptions don’t match what handlers do

1 tool describes read intent but its handler mutates — generate_training_yaml (line 209: with open(output_path, 'w', encoding='utf-8') as f:)

✅

No credential exfiltration, no sensitive file access, no obfuscation

Static analysis found nothing flowing your secrets to unexpected places.

✅

Open source with a license and README

Anyone can audit the code, the license is declared, and the publisher documents what it does.

// tools this server exposes8 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

set_cluster_credentials

Save the cluster IP and Samba CIFS credentials to a local configuration file. The agent should call this tool when the user provides the cluster IP and credentials.

get_cluster_status

Get the overall status of the NeuralForgeAI cluster, including health metrics, active celery workers (invokers), and the current tasks queue.

get_study_details

Get detailed telemetry and status of a specific YOLO training study. Returns progress, active invoker, and current trial metrics. IMPORTANT WORKFLOW FOR AGENTS: When the user asks 'how is my training going?' (or similar) without providing a study_id: 1. DO NOT ask the user for the study_id immediate…

cancel_study

Cancel a running training study by its ID. This will stop the active trials and terminate the executor containers.

generate_training_yaml

Generate a NeuralForgeAI training YAML configuration file and save it to disk. This allows the user to inspect the file before launching the training. Returns the absolute path to the generated YAML file.

launch_training

Submit a locally saved YOLO training YAML configuration to the NeuralForgeAI cluster. Use this after the user has reviewed and approved the YAML file generated by `generate_training_yaml`.

check_dataset_path

Verify if a dataset path exists on the remote Samba share by spinning up a lightweight Docker container.

validate_dataset_advanced

Validates a YOLO dataset structure by running an inspection script inside a Docker container connected to the remote CIFS share. Supports detect/segment (yaml) and classify (directory).

// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

8/8 tools missing one or more hints — set_cluster_credentials (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_cluster_status (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_study_details (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +5 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Descriptions match behaviour

1 tool describes read intent but its handler mutates — generate_training_yaml (line 209: with open(output_path, 'w', encoding='utf-8') as f:)

Rename the tool, rewrite the description, or move the side-effect into a separate clearly-named tool.

Tool inputs are validated

7/8 tool handlers declare input schemas (88%)

Declare an inputSchema with zod/joi/yup on every tool definition.

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail

The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.

// improvement guidance — verified publishers only

We have 5 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.

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commit: e264b6c92402329a21340b16874b71ecfcd78f61

code hash: 9178f2bcac871fa29ebdfa26c1d47324aa8db193d36da56cc43c1978fb227031

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