"Connecting external data sources for contextual LLM calls" matching MCP servers:

A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.

Enables coding agents to evaluate a single model over a golden set with trap cases, reporting accuracy overall and on traps, mean latency, cost per request, and failing cases. Also compares a baseline against a candidate model and returns a pass/fail gate verdict with failure reasons, so a model swap can be checked before it ships.

Enables running SQL warehouse validation checks through natural-language questions in MCP clients, while reusing the same logic for automated pipeline and CI/CD runs.

Enables developers to manage Genesys Cloud Function Data Actions through natural language in Cursor, including validating, building, planning, and applying function deployments while enforcing safety controls like plan-before-apply and read-only mode.

Enables interaction with Azure DevOps through natural language in Cursor IDE. Supports work item management, pull requests, builds, releases, test management, and guided workflows for development teams, QA testers, and release management.

MCP aggregator with many existing NCP templates and allows running external MCP servers too

A Model Context Protocol server implementation that allows AI models to interact with and manage Spinnaker deployments, pipelines, and applications through a standardized interface.

Checks the outcome after an agent handoff, when a request, 200 or ack only shows the handoff. Reads one field of one exact public HTTPS JSON record, with a same-site known-good control, for 20-120 s. Returns PROVED, DISPROVED or INCONCLUSIVE with a signed receipt you can verify. 5 free checks, then USD 1 per completed check, by card (Stripe Checkout) or existing Link wallet. INCONCLUSIVE is free.

Integrates Cline with Azure DevOps services, enabling access to work items, repositories, and pull requests through configurable MCP tools.

Provides AI assistants with comprehensive access to Azure DevOps services including work items, repositories, pull requests, wikis, builds, pipelines, and test plans through 25+ MCP tools with multi-project support.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

Provides ChatGPT read-only access to inspect repositories, code, merge requests, pipelines, and CI logs on self-managed GitLab instances, including those reachable only from a private network or VPN.

Enables comprehensive GitHub workflow automation including Actions monitoring, PR management, code search, file operations, and repository management through a code-first architecture that reduces token usage by 98%.

Enables AI assistants to interact with Argo CD applications, managing clusters, applications, and resources through natural language.

Provides integration with Apache Airflow's REST API, allowing AI assistants to programmatically interact with Airflow workflows, monitor DAG runs, and manage tasks.

MCP server that enables AI assistants to manage Azure DevOps resources including Work Items, Git repositories, pipelines, and user identities.

A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.

Enables AI agents to deploy static files and get live HTTPS URLs instantly, with support for custom domains and data residency.

Checks npm and PyPI packages for outdated versions, deprecation status, and breaking changes with cited sources, enabling AI agents to verify dependency freshness.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.