"Tool to analyze backend code, connect with Jira, and automate coding tasks" matching MCP servers:

Enables coding agents to load backend anti-slop rules and deterministically audit TypeScript/backend files for violations like SELECT *, SQL interpolation, N+1 calls, silent catches, stack leaks, type escapes, and TODOs, plus verify TypeScript compilation. Returns PASS/FAIL findings so agents can fix issues before shipping.

A Model Context Protocol server for integrating JIRA with Claude, enabling AI assistants to create, search, update, and link JIRA tickets, as well as manage Zephyr test steps through natural language.

MCP server for Jira Cloud tailored for ALM projects with Zephyr Scale integration, enabling issue management, sprint tracking, and test step operations.

MCP server for Jama Connect that enables AI assistants to interact with Jama requirements and test management through MCP tools, a REST API, and a built-in viewer.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Enables MCP clients to work with self-hosted Jira Server / Data Center 8.x through entity-based core tools, JSON-declared plugin tools, and bundled Zephyr Scale. It supports issue, comment, worklog, attachment, project, user, link, watcher, agile, and test management operations using Basic Auth or PAT with runtime field discovery.

Gives AI assistants complete, spec-driven access to Apple's App Store Connect API through 1300+ tools covering the full surface — apps and versions, TestFlight, Xcode Cloud, Game Center, App Clips, in-app purchases and subscriptions, pricing, review submissions, provisioning, analytics, sales and finance reports. It lets users automate submission workflows, manage multi-account credentials, and orchestrate builds and releases across any MCP client.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

Provides a read-only MCP bridge that lets Claude Web inspect workspace files, git state, and recorded test results while Codex remains the sole executor of changes.

Enables AI coding agents to verify code patches locally with sub-50ms neuro-symbolic analysis, detecting structural issues like dependency cycles and interface drift without consuming output tokens.

Connects to Jira with Xray Test Management to extract test cases related to tickets and list test executions.

A Model Context Protocol server that enables natural language interaction with the Coverity Connect static analysis platform, allowing users to manage projects, analyze snapshots, and generate security reports through AI-powered interfaces.

Enables coding agents to select bounded Python source context, run configured checks, and retrieve compact failure reports, diagnostic comparisons, and freshness evidence tied to the checked code.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables automated management of iOS apps, builds, TestFlight beta testing, and user access control through Apple's App Store Connect API.

Enables Garmin Connect IQ developers to inspect store versions, listings, reviews, and installs, run SDK builds and simulator workflows, and optionally access Garmin Connect data through an agent, with read-only defaults and confirmation before public actions.

Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.

An MCP server that lets an AI build hours of operation, queues, routing profiles, and real IVR flows from plain English, diagram them in chat, validate them with the platform's own flow validator, and prove them with simulated test calls. It is deliberately unable to go live on its own — no dialing, no number association, no deletes — so a human always makes the final connection.

Enables AI coding agents to submit code, files, or diffs for deterministic checks and optional re-verified LLM repair, returning cleaned code, structured findings, and a pass/fixed/reject verdict.