Community MCP server that exposes Coroot incidents and root-cause context as tools for AI-assisted analysis
Coroot MCP demonstrates solid definition quality with all 13 tools properly named with action verbs (get_, investigate_), clear descriptions explaining purpose and context, and complete JSON Schema input definitions with type constraints and parameter descriptions. Tools are read-only and composable. However, descriptions could be more LLM-optimized (some exceed 200 chars), output schemas are not documented in the provided code, and error handling patterns are not visible. Tool names are well-chosen but some parameters lack granular detail (e.g., 'query' parameters in logs/traces tools lack format guidance). No tool annotations (readOnlyHint, etc.) are present. The avg tool name length is 22 chars (within baseline p10=10, p90=27), and descriptions average 168 chars (within acceptable range). All tools have descriptions, and all required parameters are present. Schema completeness is high, every tool exposes its input schema explicitly with property types and descriptions.
Returns recent log entries for a single Coroot application, summarised for AI-assisted analysis.
Returns recent tracing spans for a single Coroot application, summarised for AI-assisted analysis.
Returns an overview of application health for a Coroot project, aligned with the Application Health Summary view.
Returns a cost overview for a Coroot project, aligned with the Costs overview in Coroot.
Returns an overview of recent deployments and their status for a Coroot project.
Returns detailed Coroot incident context, including suspected root cause, affected services, metrics, and timeline.
Output schemas not documented in tool definitions. LLMs cannot predict what fields to expect from tool responses, forcing them to reason about downstream field mappings.
Tool annotations missing (readOnlyHint, destructiveHint, idempotentHint). All tools are read-only, which should be explicitly declared to help agents reason about safety and composition.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 66 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Returns a logs overview for a Coroot project, aligned with the Logs overview in Coroot.
Returns an overview of node health for a Coroot project, aligned with the Nodes overview in Coroot.
Returns a summary of risks detected in a Coroot project, including exposure and availability information.
Returns a compact health snapshot for a Coroot service, aligned with the Application Health Summary view.
Returns an overview of SLO objectives for a Coroot project (availability & latency thresholds per project and app).
Returns a tracing overview for a Coroot project, aligned with the Tracing overview in Coroot.
Performs a structured incident investigation using Coroot data and returns an RCA summary plus a machine-readable JSON payload.
Query parameters (in get_logs_overview and get_traces_overview) lack format guidance. Descriptions say 'Optional log query string as you would enter it in the Coroot logs overview' but do not specify query syntax, operators, or examples. LLMs cannot construct valid queries without this detail.
No error handling guidance visible. Tools do not document what errors might occur (e.g., invalid projectId, missing incident, API timeout) or how agents should recover. Error responses are inferred to be JSON but recovery paths are not specified.
No pagination or result-limiting mechanism visible in overview tools (get_applications_overview, get_costs_overview, etc.). If a project has many applications, deployments, or risks, responses could bloat the context window. No documentation of result caps or pagination patterns.