CI Investigator MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GITHUB_TOKEN | Yes | GitHub token with actions:read, checks:read, and contents:read permissions |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_failed_runsB | List recent failed CI runs for a GitHub repo |
| summarize_failureB | Fetch and summarize the logs of a failed CI run |
| compare_with_last_successA | Compare a failed run with the last successful run on the same branch |
| detect_flaky_testsB | Analyze run history to detect flaky jobs on a branch |
| explain_failure_root_causeB | Classify the likely root cause category for a failed CI run |
| suggest_fix_for_failureB | Suggest remediation steps and validation checks for a failed CI run |
| list_failure_trendsC | Summarize recurring failed jobs over a time window |
| find_regression_pr_or_commitA | Find likely regression commit and related pull request for a failed run |
| ci_health_scoreB | Compute a CI health score for a branch based on pass/fail and flakiness |
| failure_notifications_digestC | Build a deduplicated digest of recent failures for alerting and triage |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 10 tools
Each tool has a clearly distinct purpose, covering different aspects of CI failure analysis without overlap. For example, ci_health_score computes a health metric, while detect_flaky_tests analyzes flakiness, and explain_failure_root_cause classifies root causes.
Tool names follow a consistent snake_case pattern and mostly use verb_noun or verb_phrase structure (e.g., detect_flaky_tests, list_failure_trends). One minor deviation is ci_health_score, which is a noun phrase, but it still fits the pattern.
With 10 tools, the server is well-scoped for its purpose of CI failure investigation. This count allows comprehensive coverage without being overwhelming.
The tool set covers the full lifecycle of CI failure analysis: identification (get_failed_runs), summarization (summarize_failure), root cause analysis (explain_failure_root_cause, find_regression_pr_or_commit), trend detection (list_failure_trends, detect_flaky_tests), and remediation (suggest_fix_for_failure, ci_health_score). No obvious gaps.