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Connect AI coding assistants to OpenTelemetry docs, examples, and instrumentation guidance.
This MCP tool does not declare any required secrets or fixed remote endpoints, and it is open source under Apache 2.0, with no clear high-risk red flags in the provided material. The main concerns are its local code-execution capability, limited clarity on data-access scope, and weak adoption/unknown maintenance, so it is best reviewed and used in an isolated environment.
The material explicitly states that no keys or environment variables are required, and there is no indication that users must provide API tokens, cloud credentials, or other sensitive secrets; based on the provided facts, credential exposure or abuse risk appears low.
No remote host is declared in the material, and the README is absent, so there is no clear factual indication that user data is sent to third-party services. The description references the OpenTelemetry ecosystem, but specific network targets and transmitted data are not disclosed; based on current evidence, there is no clear high-risk egress signal.
The objective checks already mark this tool as executes-code, indicating it can execute code or spawn processes locally. This is a standard sensitive capability for MCP tools and warrants running it with restricted permissions or in a sandbox, but this alone is not enough to classify it as high risk.
The description says it provides AI coding assistants with real-time access to repositories, documentation, examples, and scoring, which implies potential access to code repositories or related local/workspace data; however, the material does not specify exact read/write scope, disk writes, or access beyond project directories. This is better classified as caution rather than over-privileged high risk.
Positive factors are that it is open source under Apache 2.0 and the source is auditable, which materially lowers supply-chain risk; however, it comes from a third-party registry, has only 0 stars, and has unknown maintenance status, so maturity and ongoing-maintenance signals are weak. Code and dependency review is recommended before production use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "otel-instrumentation-mcp" yet — see the docs or source repo.
Based on OpenTelemetry semantic conventions, explain which span attributes, events, and status codes should be used to add request tracing and error logging to a Node.js HTTP service, and provide sample code.
Returns standards-aligned instrumentation guidance, key field lists, and reusable sample code.
Find official OpenTelemetry documentation, repository examples, and recommended practices for adding tracing and metrics to a Python Flask app, then summarize the differences.
Outputs relevant documentation links, example sources, and a concise comparison of recommended practices.
Review this service instrumentation plan for completeness, identify missing traces, metrics, and log fields based on OpenTelemetry best practices, and provide improvement suggestions with scoring rationale.
Provides an instrumentation quality score, a list of gaps, and actionable optimization recommendations.
Let AI query and analyze OpenTelemetry traces to debug apps faster.
Query and analyze telemetry data in natural language to find performance issues.
Connect AI to OpenCode for autonomous coding, debugging, and refactoring across projects.
Convert OpenAPI specs into MCP tools for fast LLM API integration.
Let AI assistants use OpenCode CLI and multiple models through one interface.
Helps AI map unfamiliar TypeScript SaaS repos, risks, and critical flows.