"How to create tasks or issues in Jira" matching MCP servers:

Enables interaction with Jira Service Management Operations alerts and on-call schedules, including searching alerts, reading notes and activity logs, acknowledging, closing, annotating, adding responders, and querying current and next on-call rotations.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables AI agents to check the health of internet infrastructure and specific services, helping diagnose whether issues are local or external.

Enables agents to interact with a GlitchTip instance for issues, events, projects, releases, alerts, uptime monitors, performance, and more via stdio or HTTP.

Connect AI assistants to Sentry for error tracking, issues, releases, events, and project management through the Model Context Protocol. Provides tools to list organizations, projects, issues, events, releases, teams, and members, as well as get details and update issue status.

Enables AI agents to inspect a live Celery deployment over RabbitMQ or Redis — checking which workers are responding, their pools, concurrency and consumed queues, and how many messages are backed up in each queue — so agents can diagnose stuck or unrouted tasks. Read-only by default, with broker credentials masked in all output.

Enables MCP clients to create and list error-tracking projects and investigate grouped exception issues using captured OTLP error data. It provides admin-token-secured tools for project setup and issue investigation.

Connects AI coding assistants to Shipbook for investigating issues using real application logs with error classification, log search, and session tracing.

Runs, tests, and finds issues in your Python services with zero code changes, then helps your AI agent fix what breaks and proves it with acceptance tests.

Enables Pi to connect to Sentry's MCP endpoint, allowing an agent to search issues, events, and traces after OAuth sign-in.

A local stdio MCP server with recording and tracking tools that lets Codex and Claude Code agents log chat activity, tasks, and delegations onto a loopback-only visual Kanban board with animated mascots. It enables observing work in progress while leaving conversation and execution in the originating client, without hosting models or running AI itself.

Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.

Enables AI assistants to retrieve and analyze Sentry issues, including error reports, stacktraces, and debugging information from Sentry.io.

Enables assistants to monitor cron jobs, investigate failures via runs and incidents, and create or manage monitors through the Tickwatch API.

Connects AI assistants to a self-hosted workflow layer so they can create jobs, browse available work, claim tasks, update status, retrieve job details, share structured context, and record review or approval events through exposed tools and prompts.

Enables interaction with the Bigeye Data Observability platform to query data quality issues, analyze data lineage and dependencies, track AI agent data access patterns, and perform root cause analysis for data issues.

Read-only MCP server exposing Flowsery analytics and AI-detected issues via 17 GET tools, enabling natural language queries in compatible clients.

Enables querying and managing Sentry projects, issues, and events through natural language.