Incident Triage MCP

Incident Triage MCP

Incident Triage MCP is a Model Context Protocol (MCP) server for incident triage. It provides safe, auditable tools for evidence retrieval, deterministic summaries, ticket workflows, and notifications.

Category
访问服务器

README

Incident Triage MCP

<!-- mcp-name: io.github.felixkwasisarpong/incident-triage-mcp -->

Python MCP Transport Docker Kubernetes License

Incident Triage MCP is a Model Context Protocol (MCP) server for incident triage. It provides safe, auditable tools for evidence retrieval, deterministic summaries, ticket workflows, and notifications.

What This Project Is

  • MCP control plane for incident triage tools.
  • Compatible with local (stdio) and networked (streamable-http) MCP clients.
  • Designed for standalone mode, Docker Compose, and Kubernetes.

What This Project Is Not

  • Not a standalone LLM agent platform.
  • Not a provider credentials vault.
  • Not a replacement for your evidence pipeline; it consumes normalized evidence bundles.

Architecture Snapshot

  • MCP server stays thin and policy-focused.
  • Evidence collection runs in Airflow (optional) and writes EvidenceBundle artifacts.
  • Agents call MCP tools only.
  • Contract stability is defined under spec/.

For full details, see docs/ARCHITECTURE.md.

Core Tools

Tool Purpose Mutating
evidence_get_bundle Fetch normalized EvidenceBundle for an incident No
evidence_wait_for_bundle Poll until bundle is available No
incident_triage_summary Build deterministic triage summary from bundle No
jira_draft_ticket Build non-mutating ticket draft No
jira_create_ticket Create ticket with safety gates Yes

Mutating actions are guarded by RBAC, dry_run, confirm_token, audit logging, and idempotency.

Provider Matrix

Area Supported providers
Alerts mock, datadog, cloudwatch, prometheus, pagerduty, opsgenie
Metrics mock, datadog, cloudwatch, prometheus
Logs mock, datadog, cloudwatch, elk, none
Traces mock, datadog, cloudwatch, xray, otel, none
Ticketing (JIRA_PROVIDER) mock, cloud, servicenow
Notify (NOTIFY_PROVIDER) slack, teams

Quick Start

Local (stdio)

python -m venv .venv
source .venv/bin/activate
pip install -e .

MCP_TRANSPORT=stdio \
WORKFLOW_BACKEND=none \
EVIDENCE_BACKEND=fs \
EVIDENCE_DIR=./evidence \
incident-triage-mcp

Local agent run (single incident)

incident-triage-agent \
  --incident-id INC-123 \
  --service payments-api \
  --artifact-store fs \
  --artifact-dir ./evidence \
  --compact

Docker (streamable-http)

docker run --rm -p 3333:3333 \
  -e MCP_TRANSPORT=streamable-http \
  -e WORKFLOW_BACKEND=none \
  -e EVIDENCE_BACKEND=fs \
  ghcr.io/felixkwasisarpong/incident-triage-mcp:latest

Optional local stack (Airflow + Postgres + MinIO + MCP):

docker compose up --build

Kubernetes: One Agent Job Per Trigger

This is the recommended runtime pattern:

  1. Incoming trigger (webhook/manual) arrives.
  2. Dispatcher (or operator) creates one Kubernetes Job per incident.
  3. Job runs incident-triage-agent once and exits.
  4. Agent calls MCP tools over HTTP.
  5. MCP optionally triggers Airflow DAG (incident_evidence_v1) and consumes bundle from fs/s3.

Deploy MCP server (Helm)

helm upgrade --install incident-triage-mcp ./charts/incident-triage-mcp \
  --namespace incident-triage --create-namespace \
  --set image.repository=ghcr.io/felixkwasisarpong/incident-triage-mcp \
  --set image.tag=0.2.8 \
  --set env.MCP_TRANSPORT=streamable-http \
  --set env.MCP_HTTP_AUTH_MODE=api_key \
  --set secretEnv.MCP_HTTP_API_KEY=change-me

Trigger one incident with a single-run agent Job

kubectl -n incident-triage create job triage-inc-123 \
  --image=ghcr.io/felixkwasisarpong/incident-triage-mcp:0.2.8 \
  -- incident-triage-agent \
  --incident-id INC-123 \
  --service payments-api \
  --mcp-url http://incident-triage-mcp/mcp \
  --mcp-api-key change-me \
  --compact

Ensure single-run behavior

  • Use deterministic job names per incident (triage-inc-<incident_id>).
  • Reject duplicates at dispatcher level if job already exists.
  • Keep ticket creates idempotent with idempotency_key.
  • Configure Job lifecycle controls (backoffLimit, activeDeadlineSeconds, ttlSecondsAfterFinished).

Configuration Essentials

Variable Meaning
MCP_TRANSPORT stdio or streamable-http
WORKFLOW_BACKEND none or airflow
EVIDENCE_BACKEND none, fs, s3, airflow
EVIDENCE_DIR Local bundle directory when using fs
AIRFLOW_BASE_URL Required for Airflow trigger/read tools
MCP_HTTP_AUTH_MODE none, api_key, jwt_hs256
AUDIT_MODE stdout (recommended in k8s) or file
DEPLOYMENT_PROFILE local, staging, prod

Profile templates live in deploy/profiles/:

  • local.env.example
  • staging.env.example
  • prod.env.example

Testing

Run full tests:

pytest -q

Run contract checks only:

pytest -q tests/test_contract_evidence_bundle.py tests/test_contract_mcp_tools.py
python scripts/validate_contrib.py

Releases

Install from PyPI

pip install incident-triage-mcp==X.Y.Z

Pull container image

docker pull ghcr.io/felixkwasisarpong/incident-triage-mcp:X.Y.Z

Supported image tags:

  • X.Y.Z (exact)
  • X.Y (minor stream)
  • latest

For release workflow details, see docs/RELEASING.md.

Project Layout

incident-triage-mcp/
  src/incident_triage_mcp/      # MCP server + tools + adapters
  spec/                         # versioned contracts
  airflow/dags/                 # evidence pipeline
  charts/incident-triage-mcp/   # Helm chart
  k8s/                          # Kubernetes manifests
  contrib/                      # polyglot contribution area
  docs/                         # architecture, release, governance docs

Support And Triage

  • Discussions: https://github.com/felixkwasisarpong/incident-triage-mcp/discussions
  • Issues: https://github.com/felixkwasisarpong/incident-triage-mcp/issues
  • Security reports: SECURITY.md

Documentation Index

Contributing

Read CONTRIBUTING.md before opening a PR.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选