MCP Tools | Model Context Protocol Server Directory

The jira-github-mcp server bridges the gap between project management and version control by exposing Jira and GitHub APIs directly to your LLM. Instead of manually switching tabs to sync tickets with code, developers can now automate the traceability chain within their IDE. The tool enables the AI to fetch Jira issue details, create corresponding GitHub branches, and track PR status based on ticket IDs. It is particularly useful for automating repetitive administrative tasks like updating ticket statuses upon commit or generating PR descriptions from issue requirements. By unifying these two ecosystems via the Model Context Protocol, it reduces context-switching overhead and ensures that the development lifecycle remains synchronized without manual data entry.

Federated registry of 91k+ agent skills and MCP servers with one-command signed installs, plus a fleet control plane that keeps skill bundles identical across Claude Code, Codex, Hermes and OpenCode. 47 MCP tools. MPL-2.0, free tier, self-hostable.

For developers managing complex microservices, the biggest bottleneck in AI-driven development is the gap between code generation and reliable testing. Signadot’s MCP server bridges this by giving AI agents direct control over Kubernetes sandboxes. Instead of just writing code, an agent can now provision isolated, lightweight environments that fork real cluster traffic to test specific service changes. This moves beyond simple unit tests; it allows agents to validate logic against actual dependencies in a live-like state without the overhead of a full staging deployment. By integrating this into your workflow, you can automate the entire lifecycle of environment creation, traffic routing, and validation. It essentially transforms an LLM from a coding assistant into a sophisticated DevOps engineer capable of managing ephemeral infrastructure to ensure production-ready merges.

The SendGrid MCP server bridges the gap between LLM reasoning and production-grade email infrastructure. Instead of manually writing boilerplate API calls to manage communication workflows, developers can grant their AI agents direct, structured access to SendGrid’s v3 API. This toolset provides 58 specialized functions, enabling agents to handle everything from triggering transactional emails and managing dynamic templates to pulling real-time delivery analytics. For developers building automated support bots, marketing automation agents, or intelligent observability dashboards, this eliminates the friction of context-switching between a chat interface and the SendGrid dashboard. Unlike basic wrappers, this implementation allows an LLM to actually 'reason' about email performance—for instance, identifying a drop in open rates and immediately querying the API to diagnose template issues. It is an essential integration for anyone moving from simple prompt-based email generation to autonomous, data-driven communication management.

For developers building agentic workflows, the biggest bottleneck is often the 'context gap'—the difficulty of feeding high-quality, structured documentation into an LLM without massive scraping overhead. This MCP tool bridges that gap by acting as a specialized discovery and conversion layer. Instead of writing custom scrapers for every library or framework, you can point your agent to this tool to automatically locate and parse `llms.txt` files. It handles the heavy lifting: finding the most specific index, resolving links, and delivering content in clean Markdown via a single remote endpoint. Unlike generic web search tools that return noisy HTML, this tool provides high-signal, LLM-ready documentation. It’s ideal for RAG pipelines or autonomous coding agents that need to instantly understand a new codebase or API documentation without manual intervention or complex preprocessing.

For developers building security-focused AI agents, bridging the gap between LLM reasoning and standardized vulnerability taxonomies is a persistent challenge. The fetch-cwe-list MCP server solves this by exposing the fetch-cwe-list library directly as a set of executable tools within the Model Context Protocol ecosystem. Instead of relying on an LLM's potentially outdated internal training data regarding Common Weakness Enumerations, your agent can now perform real-time, programmatic lookups of the CWE database. This allows for much higher precision during automated code reviews, threat modeling, or security auditing workflows. Integration is straightforward for any MCP-compliant client like Claude Desktop, turning a generic chat interface into a specialized security assistant capable of mapping discovered patterns to official industry standards. It effectively shifts the agent from 'guessing' vulnerability types to 'verifying' them against authoritative sources.

Terminal MCP bridges the gap between LLM reasoning and local execution by providing AI assistants with a real-time, shared view of your terminal environment. Unlike standard code editors that only see static files, this tool allows an agent to observe live CLI outputs, interact with TUI-based applications, and monitor long-running processes as they happen. For developers, this transforms the debugging workflow: instead of manually copying error logs into a chat window, the model can directly observe a stack trace or a failed build command and attempt corrective actions autonomously. It is particularly effective for complex environment setups, managing local databases, or testing CLI tools where state changes are non-linear. Integration is straightforward via the Model Context Protocol, making it a high-leverage addition for anyone building autonomous developer agents or seeking a more integrated 'agentic' local development experience.

MeMesh is a lightweight AI agent collaboration layer that runs locally on your machine, giving you shared memory and messaging without sending data to the cloud. It stores everything—memories, directed messages, and improvement suggestions—in a single SQLite file, so your context stays portable and inspectable. Any model or tool that supports MCP, HTTP, or CLI can plug in, making it vendor-neutral and easy to integrate with Claude Code, Codex, Cursor, Gemini, or self-hosted models. Since it's open source under MIT and runs entirely offline, it's ideal for developers who want persistent agent state, cross-agent communication, or a simple way to track and refine prompts over time. MeMesh fills the gap between isolated model runs and full agent frameworks, offering just enough structure to enable collaboration without the overhead. It's especially useful for teams experimenting with multiple agents locally or anyone building iterative AI workflows on their own infrastructure.

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PHGuard MCP is a local-first security scanner that catches Protected Health Information (PHI) leaking into LLM prompts, application logs, analytics calls, and other outputs during development. It integrates directly with your codebase, analyzing data flows in real time without sending anything over the network. This is useful for teams building healthcare, wellness, or any app that processes medical data and needs to stay HIPAA-compliant or avoid accidental data exposure. The tool works by flagging risky patterns and data usage in code, helping developers fix issues before they reach production. It's lightweight, runs offline, and fits naturally into existing workflows like pre-commit hooks or CI pipelines. Compared to cloud-based scanners, PHI Guard gives you more control and privacy since no source code or data leaves your environment.

For developers building autonomous coding agents, state management is often the weakest link. When an LLM-driven loop crashes or hits a rate limit, the entire context of a multi-step task is usually lost. Rhizome-mcp solves this by providing a robust coordination layer via the Model Context Protocol. Unlike simple memory buffers, it implements a crash-safe task tracking system using a single Go binary and a local SQLite backend. It introduces critical primitives like expiring lease claims—preventing multiple agents from stepping on each other's toes—and resumable task attempts. This allows your agents to pick up exactly where they left off after a failure. It is particularly useful for long-running refactoring jobs or complex migrations where version-pinned reviews and state persistence are non-negotiable. If you are moving beyond simple chat-based prompting toward reliable, agentic workflows, this tool provides the necessary infrastructure to ensure task atomicity and continuity.

For developers building sports analytics engines or AI-driven betting assistants, the 5DollarFootballAPI MCP server provides a standardized bridge to real-time football data. Unlike traditional REST integrations that require manual polling and complex parsing logic, this MCP implementation allows LLMs to query live scores, fixtures, and league standings directly through a structured protocol. Beyond basic match results, the server exposes granular telemetry like corner counts, card statistics, and critical betting odds movement history. This makes it an ideal tool for testing predictive models or building conversational agents that need to reference current match dynamics. By integrating this server, you move from writing boilerplate data-fetching code to simply providing your agent with a high-fidelity, read-only window into global football markets and live match events.

For developers building LLM-driven applications, the tension between utility and data privacy is a constant friction point. Questa Privacy MCP addresses this by providing a standardized interface to intercept and sanitize sensitive data before it ever touches a model provider's API. Instead of manually writing regex patterns or custom middleware for every new integration, this tool allows you to implement automated PII redaction and anonymization directly within your MCP-enabled workflow. It is specifically engineered to help teams meet strict compliance frameworks like GDPR, HIPAA, and the EU AI Act without sacrificing the context necessary for high-quality model responses. Whether you are fine-tuning a RAG pipeline or building a customer-facing agent, Questa acts as a programmable privacy layer that sits between your local data and the cloud, ensuring that identity-revealing information is scrubbed while preserving the semantic integrity of the prompt.

reqlan MCP is a development framework that helps you build verifiable systems by explicitly modeling the components that matter and how they connect. Instead of writing code first and documenting later, reqlan lets you describe your system's structure upfront, making relationships between parts clear and testable. It's particularly useful for complex domains where correctness is critical, like financial services, healthcare, or distributed systems. The framework integrates with existing toolchains, so you can adopt it incrementally without rewriting everything. Compared to traditional modeling approaches, reqlan focuses on the essentials: what things are, what they do, and how they depend on each other. This makes it easier to catch design flaws early, generate documentation automatically, and keep your architecture consistent as your system evolves. It's aimed at teams who want to move fast but also need confidence in their system's behavior.

KD-Scout is a lightweight MCP server that brings keyword difficulty scoring, opportunity analysis, and structured content briefs directly into your development workflow. It exposes these SEO insights through a standardized protocol, so you can integrate keyword research data into static site generators, content management tools, or custom dashboards without relying on external APIs or heavy SDKs. The server runs with zero runtime dependencies, making it easy to deploy in CI pipelines or local environments. For developers building content-heavy applications, KD-Scout provides a programmable interface to evaluate search visibility and content gaps early in the development cycle. Unlike traditional SEO tools that require manual data export, KD-Scout lets you query metrics programmatically and embed them into your own tooling. It's particularly useful for teams automating content strategy, generating data-driven briefs, or building SEO-aware applications. Integration is straightforward via the MCP protocol, which is supported by various editors and agents, allowing seamless context passing between your code and SEO insights.

M00N Report brings lightweight test management directly into your MCP-compatible environment. Instead of juggling a separate QA tool, you can author test cases, execute them manually with per-case pass/fail tracking, and cut releases from the same workflow where you already work. It's designed for small to mid-sized teams who want structured testing without the overhead of enterprise suites. The server exposes actions to create cases, record results, tag versions, and link automated test scripts to specific cases, so your manual and automated coverage stays in sync. Integration is straightforward: spin up the server via the MCP host of your choice, point it at a local or Git-backed store, and start calling tools from your agent or editor. Compared to full-stack ALM platforms, M00N Report is opinionated and minimal — it won't replaceJira or TestRail for large orgs, but it covers the core needs of teams that want test visibility without leaving their dev loop.

For developers managing complex identity and access management, the mcp-keycloak server bridges the gap between LLM reasoning and administrative execution. Instead of manually navigating the Keycloak UI or writing boilerplate REST calls, you can now use an MCP-enabled agent to orchestrate multi-realm configurations directly through natural language. The tool provides deep coverage for managing users, clients, roles, and groups, but it is built with enterprise-grade safety in mind. It includes critical guardrails like dry-run modes to preview changes, delete gating to prevent accidental data loss, and comprehensive audit logging for compliance. Whether you are automating user onboarding workflows or auditing client permissions across multiple realms, this integration turns your AI assistant into a controlled, high-precision identity administrator. It moves beyond simple API wrappers by offering structured access modes and allowlists, ensuring that your LLM interactions remain within defined security boundaries.

The mcp-azure-devops server bridges the gap between LLM reasoning and your Azure DevOps ecosystem. Instead of manually context-switching between your IDE and the web portal, this tool allows your AI assistant to programmatically interface with boards, repositories, pipelines, and project metadata. It transforms your chat interface into a functional command center where you can query work item statuses, inspect pull requests, or trigger build pipelines using natural language. Unlike basic API wrappers, this implementation prioritizes enterprise-grade safety. It includes sophisticated governance features like project allowlists, protected project designations, and 'dry-run' modes to prevent accidental mutations. For developers working in regulated environments, the addition of typed confirmations and audit logging ensures that AI-driven actions remain transparent and controlled. It is an essential integration for teams looking to automate DevOps workflows without sacrificing security or oversight.

For developers working with LLM orchestration, the fragmentation of MCP server registries is a major friction point. mcp-anything solves this by acting as a unified gateway to over 75,000 public MCP servers. Instead of manually configuring individual connections for every new tool, this meta-gateway allows you to search, discover, and invoke tools on the fly through a single interface. It follows a local-first architecture, ensuring your data stays within your controlled environment rather than relying on centralized cloud proxies. Crucially, it addresses the security concerns inherent in dynamic tool calling by implementing SSRF guards and a strict stdio allowlist. Whether you are building autonomous agents or complex RAG pipelines, this tool provides a scalable way to expand your model's capabilities without the overhead of managing dozens of separate server configurations.

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The mcp-walmart-marketplace server connects your development environment directly to Walmart's Marketplace APIs, letting you query product catalogs, pricing, inventory, and order data programmatically through the standardized MCP protocol. It's aimed at developers building tools, dashboards, or automation pipelines for US-based third-party sellers on Walmart.com. Instead of manually integrating each Walmart endpoint, you get a single MCP-compatible interface that handles authentication, request formatting, and response parsing. This makes it easy to plug Walmart data into IDEs, agent workflows, or custom applications that already support MCP. Compared to scraping or raw API wrappers, it offers a cleaner, more maintainable integration path, though it's limited to the US marketplace and requires valid seller credentials. If you're already using MCP-based tooling, adding Walmart data becomes a matter of enabling the server rather than writing boilerplate HTTP clients. The project is community-maintained and hosted on GitHub, making it a practical starting point for seller-side analytics or backend sync utilities.

As LLM-integrated applications move from prototype to production, the attack surface for prompt injections and data poisoning has expanded significantly. Universal Poison Armor is an open-source MCP security layer designed to sit between your data sources and your model. Unlike traditional perimeter security, this tool functions as a specialized firewall within the Model Context Protocol framework, intercepting adversarial inputs—such as sybil attacks and poisoned RAG retrieval chunks—before they hit your context window. For developers, this means you can leverage external tools and dynamic data fetching without manually sanitizing every incoming retrieval. It integrates directly into your existing MCP ecosystem, providing a programmable defense layer that mitigates the risk of indirect prompt injection. Instead of building custom regex filters for every new integration, you can deploy this as a standardized middleware to harden your agentic workflows against evolving adversarial patterns.

The SAP ABAP MCP server connects external tools to ABAP development environments through SAP's ABAP Development Tools (ADT) and JSON RPC protocols. It enables developers to query repositories, inspect code objects, run programs, and gather system metadata without leaving their preferred editor or automation pipeline. By exposing MCP-compliant endpoints, it bridges modern LLM-powered tooling with legacy SAP systems, letting you build custom assistants for code review, refactoring, or documentation generation. Integration requires an SAP system with ADT enabled and basic auth credentials, then register the server in your MCP client configuration. Unlike generic HTTP wrappers, this server understands ABAP-specific structures like function modules, classes, tables, and DDIC objects, so responses are structured for downstream tool consumption rather than raw dumps. It's aimed at teams maintaining SAP codebases who want to automate repetitive tasks or embed AI assistance directly into ABAP workflows.

Obsify is a local, privacy-preserving PII detection and redaction tool built on MCP, designed for developers who need to handle sensitive data without exposing it to external services. Unlike cloud-based solutions, Obsify runs entirely on your machine, using Presidio and checksum-based validation to identify PII such as Australian identifiers (ABN, ACN, TFN) with deterministic accuracy and no reliance on LLMs or network calls. It operates on data shape rather than raw values, meaning your local code handles real data and returns only masked, aggregated results. This makes it ideal for compliance workflows, data anonymization pipelines, and secure testing environments where privacy is critical. Since it's MCP-compliant, Obsify integrates smoothly into existing development tools and agent ecosystems. It’s particularly useful for teams working with regulated data in finance, healthcare, or government sectors who want to avoid vendor lock-in and ensure auditability without sacrificing developer flexibility.