"A server for finding a Jira integration" matching MCP servers:

A local MCP server for Jira Server/Data Center that enables Claude and Codex to manage issues, comments, transitions, worklogs, and links through the Jira REST API v2 using Basic Auth.

Enables read-only Jira Data Center insights by default, with opt-in audited write tools, sprint and release health summaries, and structured issue search via MCP.

A production-ready MCP server enabling AI assistants to interact with Jira projects, issues, sprints, users, attachments, and worklogs via natural language, with support for both local stdio and remote HTTP transports, dual authentication methods, and enterprise-grade security controls.

Enables Claude to read Jira CSV/JSON backlog exports, audit tickets for test readiness, and generate validated Gherkin acceptance scenarios tied to ticket criteria. It also tracks acceptance-criteria coverage and flags ambiguities or missing information for Product Owner follow-up.

MCP server that connects to Jira Data Center, enabling issue search, retrieval, creation, comments, workflow transitions, project listing, arbitrary JQL, and turning meeting notes into structured Jira stories, tasks, and bugs.

Enables PM radar analysis of Jira projects, providing daily, sprint, and backlog reports with issue context and search.

Enables conversion of business briefs or authenticated saved projects into structured handoffs for the Lovable plugin, with both anonymous basic mode and authenticated full mode including knowledge base, SEO, WCAG, security, and roadmap guidance.

Enforces a marketing pre-flight: plan the campaign with kill criteria, score each offer on an explainable 0-100 rubric, and require every tactic to cite a scored offer plus a success metric before any spend.

It enables solo builders to automatically log product changes to PostHog and later get statistically honest verdicts on whether those changes moved key metrics, without needing enough traffic for A/B tests.

Provides a comprehensive creative thinking tool with methods like structured process, perspective shift, and constraint-based innovation to enhance divergent thinking.

Pre-build reality check for AI coding agents. Scans GitHub, Hacker News, npm, PyPI & Product Hunt in parallel — returns a 0-100 reality signal with real competitor data.

Enables discovery of a company's public feature board (Canny, Featurebase, Frill, UserVoice, Productboard) from its domain and retrieval of its most-voted requests with votes, status, comments, and the integrations customers ask for. Supports monitoring runs that surface only new requests, status changes, and vote jumps since a prior run using a saved state key.

A Model Context Protocol server that provides Monte Carlo forecasting tools for project duration, cost, completion dates, and sensitivity analysis using three-point estimates and historical throughput.

Enables triaging product feedback over a local dataset, including search, theme clustering, severity tagging, and drafting PRD-ready problem statements.

Enables users to describe a product and receive a ranked, live-checked list of directories, launch sites, newsletters, communities, and other submission destinations, with fit scores, pricing, link attributes, and popularity ranks for planning launches.

Enables agents to evaluate whether a product-adoption message is worth sending by applying a fixed, evidence-based rubric to a draft, recipient context, evidence, and the agent's own assessment, returning a send, revise, or hold recommendation with a score and reasons.

A local MCP server that gives AI agents read access to Roblox experience analytics via the Open Cloud API, exposing 168 metrics and analysis tools for retention, engagement, monetization, and more.

A Python MCP server that gives Claude access to PM workflow data such as sprints, roadmap, blockers, and workload, using synthetic fixture data (no API keys required) and can be forked for real Linear/Jira integration.

A Jira-lite MCP server that enables planning-first workflows for AI-assisted development. It manages tickets as markdown files with YAML frontmatter, guiding agents through epics, stories, tasks, and architecture decision records.

Validates startup ideas with a deterministic scorecard, evidence brief, and verdict before code is written, integrating with MCP-aware build agents to avoid building dead-on-arrival products.

Enables AI assistants to search new software launches, read product pages, find alternatives, and follow daily rankings from inside a chat using public read-only data.

Enables AI assistants to validate drafted user stories against INVEST and acceptance-criteria rules, suggest splits for oversized stories, and export finished backlogs to Markdown, CSV, or Jira CSV.

Deterministic marketing math for AI agents: 62 engines that compute LTV:CAC, CAC payback, pipeline coverage, A/B test significance, budget allocation, pricing and retention from your own numbers. Runs locally with Node 18+, makes no network calls and stores no data.

Turns product feedback pinned to a live UI into an actionable backlog for Claude Code — list comments, open one with its target element's HTML, computed styles and screenshot, and update its status.

Lets an AI agent read a Pyxis dashboard's analytics — overviews, funnels, devices, acquisition, backend requests, visits and project settings — using a read-only access token created by an admin and revocable at any time. It runs locally over stdio, holds no state, and exposes only GET-style reads of the live Pyxis API.

Provides a transparent, deterministic multi-criteria decision analysis engine that ranks options against weighted criteria with exact, explainable results.

A Model Context Protocol server for FeaturePulse feedback management, enabling AI assistants to query feature requests, analyze MRR impact, and manage product roadmaps through natural language.

A deterministic what-if scenario simulation MCP server that projects business metrics over time with exact decimal arithmetic, offering sensitivity analysis and break-even solving.

Enables a coding agent to keep one JSON feature specification per feature, governed by a progressive schema, and to render it as editable Markdown documents for a product owner. Answers and direct Markdown edits are reconciled back into the validated model through stdio MCP tools, applying coherent changes and reporting those that widen scope or contradict the model.

Prioritizes tasks by eliminating distractions, returning a ranked shortlist based on a given goal.