"A server for integrating with Bitbucket using MCP protocol" matching MCP servers:

A Model Context Protocol server that integrates Cursor IDE with Bitbucket Cloud, allowing developers to fetch repository information and commit data directly from their Bitbucket workspace.

MCP server with 58 tools for Bitbucket API operations. Manage repositories, pull requests, pipelines, branches, commits, deployments, webhooks, and more.

MCP server for Bitbucket Cloud that exposes 35 tools covering repositories, pull requests, branching models, and pipelines, enabling AI clients to automate Bitbucket workflows.

An integration tool that enables AI assistants like Claude to directly access and interact with Bitbucket repositories, pull requests, and code without requiring copy/paste operations.

MCP server for Bitbucket Cloud that enables reviewing, managing, and merging pull requests, assigning reviewers by name, reading files at a ref, and inspecting pipeline status from any MCP client.

Connects Claude Desktop and Claude Code to Bitbucket Cloud, enabling searching PRs, commits, diffs, and file content for AI-assisted bug triage and impact analysis.

Enables Claude and other AI agents to manage Bitbucket Cloud repositories, pull requests, branches, tags, commits, diffs, files, and pipelines through a secure local MCP server with read and write tools.

Enables AI-powered pull request review and analysis for Bitbucket Cloud, plus tools to list, create, diff, comment on, and analyze PRs. Supports stdio or HTTP transports with OAuth/bearer auth and pluggable LLM providers.

Enables cross-chain bridging, multi-chain token swaps, and real-time pricing across over 50 blockchain networks using the Relay Protocol REST API. It allows users to browse thousands of tokens, generate quotes, and monitor transaction status directly through natural language.

Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical

Conformance test harness for Model Context Protocol servers, validating JSON-RPC, transport, capability, schema, and more across multiple spec versions.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.

Enables AI assistants to interact with Bitbucket Cloud and self-hosted instances for pull request reviews, code search, repository operations, and managing PR comments and approvals.

Enables AI clients to create and configure monitors, subscribe to their evidence streams with durable cursors, and process observations with guaranteed at-least-once delivery.

Enables a language model to read Bitbucket Cloud pull requests and post line-anchored comments with a summary, while deliberately preventing merge, decline, or approval actions.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.