ci-watch-mcp
It waits for GitHub Actions CI to finish for a commit in a local GitHub repository and returns the result plus failure logs in a single MCP call.
watch_ciaccepts a required local repo path (cwd) and optionalref,workflow,timeout_ms, andpoll_interval_ms.Polls
gh run listuntil the relevant CI run completes, fails, or times out.On success: returns
ok: true, conclusion, URL, SHA, and workflow name.On failure: returns
ok: false, conclusion, URL, SHA, workflow name, and the tail ofgh run view --log-failed(truncated to 8000 chars).On timeout: returns
ok: false,timedOut: true, status, URL, and message.Throws an error immediately if
refdoes not resolve to a real commit.Lets you filter by workflow name when a repo has multiple workflows.
Timeout is configurable up to 20 minutes, polling interval configurable.
Cross-platform Node.js server; requires the
ghCLI installed and authenticated.
Monitors GitHub repository CI runs by waiting for workflows to complete for a given commit, and retrieves failure logs via the GitHub CLI.
Provides a tool to wait for GitHub Actions workflows to finish and return the conclusion (success/failure) along with truncated error logs if a workflow fails.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ci-watch-mcpWatch CI for my current commit and tell me if it passes, with logs on failure."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ci-watch-mcp
An MCP server with a single watch_ci tool — it waits for GitHub Actions CI to complete for a commit in a local git repository and, in one call, returns a summary along with the error log if something failed.
Why
Without it, checking whether CI passed after a push is done manually:
push one → sleep → gh run list → if the status is "in_progress", sleep again → gh run list again → if conclusion: failure, work through a separate
gh run view <id> --log-failed call to see what broke.
watch_ci does all this in a single call and immediately returns the failure log,
without a manual wait loop.
Related MCP server: claude-beacon
Tool
watch_ci
Parameter | Type | Default name | Description |
| string | — (required) | Path to the local clone of the repository with |
| string |
| SHA (full or short) or any git-ref |
| string | — | Filter by workflow name if the repo has several |
| number |
| Ceiling: |
| number |
| Polling interval for |
Returns:
{ ok: true, conclusion: "success", url, sha, workflowName }— CI passed;{ ok: false, conclusion: "failure", url, sha, workflowName, failedLogs }— CI failed;failedLogscontains the tail ofgh run view --log-failed(truncated to 8000 chars, from the end — that is the most informative part);{ ok: false, timedOut: true, status, url, message }— did not finish/start within the allotted time.
If ref does not resolve to an actual commit (i.e. a typo in the SHA), the tool immediately throws an error instead of silently disposing until the timeout.
Installation
cd ci-watch-mcp
npm installConnect to Claude Code:
claude mcp add ci-watch -s user -- node /шлях/до/ci-watch-mcp/src/server.jsRequires an installed and authenticated gh CLI (gh auth status).
Tests
npm testThe tests run against already finished real runs in the secretscan repository (successful, failed, and a commit with none of the CI runs) — no need to wait for a live push.
License
MIT — Faneraiy14.
Available Tools
1 toolwatch_ciДочекатись завершення GitHub Actions CIA
Чекає, поки GitHub Actions CI завершиться для поточного (або заданого) коміту в локальному git-репозиторії, і повертає підсумок ОДНИМ викликом: ok/conclusion/url, а за провалу - одразу хвіст логу невдалих кроків (--log-failed), без окремого запиту. Заміняє ручний цикл "push -> sleep -> gh run list -> (якщо впало) gh run view --log-failed".
| Name | Required | Description | Default |
|---|---|---|---|
| cwd | Yes | Абсолютний шлях до локального клону репозиторію (з origin на GitHub) | |
| ref | No | SHA коміту для перевірки; за замовчуванням поточний HEAD у cwd | |
| workflow | No | Назва workflow для фільтрації (напр. "CI"), якщо в репо їх декілька | |
| timeout_ms | No | Максимальний час очікування в мс, типово 300000 (5 хв), стеля 1200000 (20 хв) | |
| poll_interval_ms | No | Інтервал опитування в мс, типово 5000 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses key behaviors: single-call summary return, and immediate failed-step log tail on failure. It does not mention timeout behavior or authentication requirements, but the essential behavioral traits are covered well beyond what the input schema conveys.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, efficient and front-loaded. The first sentence states the core function, the second details the return behavior, and the third shows the manual cycle it replaces. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While there is no output schema and no annotations, the description provides a strong overview of inputs, behavior, and outputs, including failure handling. It does not specify timeout outcomes, but the timeout_ms parameter in the schema partially addresses that. Overall, it gives an agent sufficient context to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters with detailed descriptions, so baseline is 3. The description adds minimal extra parameter context (e.g., 'current or given commit' aligns with ref, 'local git repository' aligns with cwd), but it does not meaningfully enhance the schema's already-complete parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool waits for GitHub Actions CI completion and returns a summary (ok/conclusion/url), with failed-step logs on failure. It uses a specific verb ('Чекає' – waits) and identifies the resource and scope (current/given commit in local git repo), making the purpose unambiguous even without sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly frames the tool as a replacement for the manual 'push -> sleep -> gh run list -> gh run view --log-failed' cycle, clearly indicating when to use it. It provides a concrete alternative scenario and positions this tool as the streamlined single-call solution.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
watch_ci
TDQS
Scored across 1 tool
Only one tool exists, so there is zero ambiguity in tool selection. The tool's purpose is clearly defined and distinct.
With a single tool, naming consistency is trivially maintained. The name 'watch_ci' is descriptive and follows a verb_noun pattern, though no comparison set exists to evaluate full consistency.
A single-purpose server with one tool is well-scoped. The tool encapsulates a complete workflow (waiting for CI and returning results), avoiding unnecessary fragmentation.
The tool covers the core CI-watching workflow effectively, including failure log retrieval. Minor gaps exist (e.g., no explicit cancel or list operations), but they are outside the tool's stated purpose and do not hinder its primary function.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Create, deploy, and operate MCP servers directly from your GitHub repositories.
MCP server for Appcircle mobile CI/CD platform.
The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.
Related MCP Servers
- AlicenseAqualityCmaintenanceMCP server for GitHub Actions — view workflow runs, read logs, re-run failed jobs, and manage CI/CD.9504MIT
- AlicenseNot gradedqualityCmaintenanceMCP server that pushes GitHub Actions CI/CD results and PR events into Claude Code sessions, enabling automatic investigation and remediation.131MIT
- AlicenseAqualityCmaintenanceMCP server for GitHub Actions CI failure triage. It wraps the gh CLI to fetch PR checks, failed jobs, and tail/grep failed job logs with ANSI/timestamp stripping and root-cause extraction.5MIT
- AlicenseAqualityBmaintenanceMCP server that lets Claude diagnose failed GitHub Actions runs by fetching and parsing CI logs into structured failure signals, enabling natural-language questions like 'Why did the last run fail?'.6MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Faneraiy14/ci-watch-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server