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Tuning Engines MCP server

by cerebrixos·io.github.cerebrixos/tuning-engines·v0.3.5·6 stars
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Domain-specific LLM fine-tuning — sovereign models trained on your data, zero infrastructure.

B72/100grade B
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Safety scan
B72/100

full report

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6 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

Tuning Engines tools (17, 3 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • cancel_jobwrite action

    Cancel a running or queued fine-tuning job. The job will be charged for any GPU time already used.

  • create_jobwrite action

    Fine-tune an LLM on a GitHub repository using Tuning Engines.

  • delete_modelwrite action

    Delete a trained model from cloud storage.

  • estimate_job

    Get a cost estimate for a fine-tuning job before submitting it. Returns estimated cost, cost range, current balance, and whether balance is sufficient. Always estimate before creating a job.

  • export_model

    Export a trained model from Tuning Engines cloud storage to your S3 bucket.

  • get_account

    Get your Tuning Engines account details and settings.

  • get_balance

    Check your Tuning Engines account balance and recent transactions.

  • import_model

    Import a model from S3 into Tuning Engines cloud storage so it can be used as a base for future fine-tuning jobs.

  • job_status

    Get live status of a fine-tuning job including current status, GPU minutes used, estimated charges, remaining balance, and delivery progress. Use this to monitor a running job.

  • list_jobs

    List fine-tuning training jobs on Tuning Engines. Returns recent jobs with status, base model, agent type, GPU usage, and cost. Use this to check on existing training runs or find a job ID.

  • list_models

    List your trained and imported models on Tuning Engines.

  • list_supported_models

    List the supported base HuggingFace models available for fine-tuning on Tuning Engines.

  • model_status

    Check the status of a model import or export operation.

  • retry_job

    Retry a failed fine-tuning job from its last checkpoint. Creates a new job that resumes training where the failed one stopped, saving GPU time. Each retry is billed separately.

  • show_job

    Get full details of a specific fine-tuning job including status, base model, agent type, GPU minutes, cost, error messages, and whether it can be retried from checkpoint.

  • show_model

    Get details of a specific trained model.

  • validate_s3

    Validate S3 credentials by testing read/write access to the specified bucket. Use before submitting a job with S3 export.

Public scan report

scanner v0.1.10 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan23 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 41 days ago12/15
  • Maintainer identitynamespace and repository owner differ4/10
Overall 72/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Tuning Engines compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
72/100
higher than 40% of 4,460 AI and agents servers
Tools
17
the typical AI and agents server has 6
Tools that change things
18%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
6
more than 80% of 2,711 AI and agents servers

What its tools let an agent do: 13 read · 2 create · 2 delete

Install Tuning Engines in Claude Code, Cursor or VS Code

Runs npx -y tuningengines-cli on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add tuning-engines -- npx -y tuningengines-cli
Add to Cursor

What the publisher says

From the Tuning Engines repository's README, as published. We do not edit it. Read it on GitHub

Tuning Engines CLI & MCP Server

Govern every AI workflow through one API.

Tuning Engines is a governed AI runtime for model, agent, skill, and MCP workflows. Route inference through one OpenAI-compatible API, apply RBAC and traffic policies, request approvals for high-risk actions, inspect traces and usage, and connect durable orchestration frameworks such as LangGraph and Temporal. The same CLI and MCP server also manage domain-specific fine-tuning of open-source models.

Training Agents

Tuning Engines uses specialized agents that control how your data is analyzed and converted into training data. Each agent produces a different kind of domain-specific fine-tuned model optimized for its use case. Current agents focus on code, with more coming for customer support, data extraction, security review, ops, and other domains.

Cody (code_repo) — Code Autocomplete Agent

Cody fine-tunes on your GitHub repo using QLoRA (4-bit quantized LoRA) via the Axolotl framework (HuggingFace Transformers + PEFT). It learns your codebase's patterns, naming conventions, and project structure to produce a fast, lightweight adapter optimized for real-time completions.

Best for: code autocomplete, inline suggestions, tab-complete, code style matching, pattern completion.

te jobs create --agent code_repo \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-cody-model

SIERA (seracoderepo) — Bug-Fix Specialist

SIERA (Synthetic Intelligent Error Resolution Agent) uses the Open Coding Agents approach from AllenAI to generate targeted bug-fix training data from your repository. It synthesizes realistic error scenarios and their resolutions, then fine-tunes a model that learns your team's debugging style, error handling conventions, and fix patterns.

Best for: debugging, error resolution, patch generation, root cause analysis, fix suggestions.

te jobs create --agent sera_code_repo \
  --quality-tier high \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-siera-model

Quality tiers (SIERA only):

  • low — Faster, fewer synthetic pairs (default)
  • high — Deeper analysis, more training data, better results

Coming Soon

Supported Base Models

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Tuning Engines: common questions

Is Tuning Engines MCP server safe?
Mostly: it is graded B (72/100). Read the Tuning Engines safety report
How do I install Tuning Engines?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Tuning Engines need an API key?
Yes. The registry entry asks for TE_API_KEY.
Is Tuning Engines maintained?
The last commit was 78 days ago (2026-08-18). The latest release is v0.3.5.
What can I use instead of Tuning Engines?
Servers from other publishers that do the same job: Summer Engine MCP server, DCC-MCP Core MCP server and mcptoon MCP server.
How does Tuning Engines compare with other AI and agents MCP servers?
safety score 72/100 (higher than 40% of 4,460 AI and agents servers); tools 17 (the typical AI and agents server has 6); tools that change things 18% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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