mcpField
E
Edge Delta MCP Server

Edge Delta MCP Server

Local
github.com/edgedeltaCloud & Infra10Updated 1w ago

About Edge Delta MCP Server

Edge Delta MCP Server connects MCP clients to Edge Delta APIs for developers and tools managing observability pipelines. It supports configuration changes with validation and rollback, plus offline tests and live data captures to check transformations.

Key features

  1. 01Inspect pipeline configurations and history
  2. 02Validate, save, deploy, and revert changes
  3. 03Add pipeline data sources
  4. 04Capture live before-and-after samples
  5. 05Dry-run OTTL and processor nodes

Use cases

  1. 01Review Edge Delta pipeline settings
  2. 02Test processor changes on sample logs
  3. 03Verify transformations against live data

MCP support

Exposes Edge Delta pipeline and observability operations as tools for MCP clients, including configuration management, dry-runs, and live captures.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"edgedelta": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"buildx"
]
}
}
}
Run commanddocker run -i --rm buildx

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocibuildx

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Edge Delta MCP Server

The Edge Delta MCP Server is a Model Context Protocol (MCP) server that provides seamless integration with Edge Delta APIs, enabling advanced automation and interaction capabilities for developers and tools.

Use Cases

  • Extract and analyse observability data from Edge Delta.
  • Build AI‑powered tools and applications that interact with Edge Delta’s platform.

Pipeline Configuration Tools

The server exposes tools to inspect and change Edge Delta pipelines. Write operations follow a safe loop: validate → save → deploy, with rollback.

ToolWhat it does
get_pipelines / get_pipeline_config / get_pipeline_historyDiscover pipelines and read their config and version history.
validate_pipeline_configValidate a config YAML without saving it. Returns {valid, reason}.
save_pipeline_configSave the full config YAML as a new version (does not deploy).
deploy_pipelineDeploy a saved version to the fleet.
revert_pipelineRoll back to a previously saved version.
add_pipeline_sourceAdd a data source node to a pipeline.

Typical edit flow: get_pipeline_config → edit YAML → validate_pipeline_config → save_pipeline_config → get_pipeline_history → deploy_pipeline (revert_pipeline to roll back).

Live-capture tools

Validating that a config is valid is not the same as confirming a change actually transformed the data. The live-capture tools sample real before/after data flowing through pipeline nodes on running agents, so an agent can verify a transformation took effect.

ToolWhat it does
start_pipeline_captureStart a capture task on a pipeline (duration, optional node list, max items).
get_pipeline_capture_statusPoll per-agent status for a capture task.
get_pipeline_capture_resultsFetch captured before/after samples per node.
get_pipeline_capture_taskShow the currently active capture task, if any.

Capture flow: start_pipeline_capture → get_pipeline_capture_status (poll until an agent reports success) → get_pipeline_capture_results (compare each node's before vs after to confirm the transformation).

Processor dry-run (test) tools

Develop and verify a processor against sample logs offline — no save, no deploy, no live traffic. The offline complement to live-capture: capture proves what did happen, this proves what will happen.

ToolWhat it does
test_pipeline_ottlDry-run OTTL statements against sample OTEL log items; returns the transformed items.
test_pipeline_nodeDry-run a full processor node of any type (log_to_metric, log_to_pattern, extract_metric, parse_json_attributes, grok, mask, …); returns the items it emits per output path.

Iterate until the output is right, then add the statements/node to the pipeline, save_pipeline_config → deploy_pipeline, and confirm on live data with the capture tools. Use test_pipeline_ottl for OTTL transforms (its ExtractPatterns also covers regex field extraction); use test_pipeline_node for non-OTTL node types such as log-to-metric and log-to-pattern.

Prerequisites

  1. Docker Engine ≥ 20.10 installed and running.
  2. Docker Buildx plug‑in available:
    • macOS / Windows – included with Docker Desktop.
    • Debian / Ubuntu
      sudo apt-get update && sudo apt-get install -y docker-buildx-plugin
      
    • Fedora / RHEL / CentOS
      sudo dnf install -y docker-buildx-plugin   # or yum install …
      
    • Other distros (manual fallback)
      mkdir -p ~/.docker/cli-plugins
      curl -sSL \
        https://github.com/docker/buildx/releases/latest/download/buildx-$(uname -s | tr '[:upper:]' '[:lower:]')-amd64 \
        -o ~/.docker/cli-plugins/docker-buildx
      chmod +x ~/.docker/cli-plugins/docker-buildx
      
  3. An Edge Delta API token with the required scope – create one here.
  4. Your Edge Delta organisation ID – find it here.

Build (container image)

First‑time setup (creates a multi‑platform builder and boots it):

docker buildx create --name edgedelta-builder --use
docker buildx inspect --bootstrap

Build the image and load it into the local Docker daemon:

docker buildx build --load -t mcp/edgedelta .

ℹ️ The --load flag streams the image back to your local Docker engine so you can run it directly with docker run mcp/edgedelta ….

Installation

Usage with Cursor
{
  "mcpServers": {
    "edgedelta": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e ED_ORG_ID",
        "-e ED_API_TOKEN",
        "ghcr.io/edgedelta/edgedelta-mcp-server:latest"
      ],
      "env": {
        "ED_API_TOKEN": "<YOUR_TOKEN>",
        "ED_ORG_ID": "<YOUR_ORG_ID>"
      }
    }
  }
}

Library Usage

The exported Go API of this module is experimental and may change without notice. If you rely on it in production, please open an issue describing your use case so we can stabilise the relevant surface.

License

Licensed under the terms of the MIT licence. See LICENSE for full details.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.