"Resources or information related to coding or programming" matching MCP servers:

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

Enables Claude to interact with R package development workflows, including CRAN metadata lookup, dependency checking, testing, documentation validation, and release automation.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

Enables AI assistants to interact with Bitbucket repositories, pull requests, and related resources through the Bitbucket Cloud and Server APIs. It supports operations such as listing repositories, retrieving repository details, and fetching or creating pull requests.

Mneme exposes a governed Decision Index so MCP clients can propose candidate architectural decisions and query decision state, applicability, and traceability. Proposals remain non-authoritative: accepting or activating protections stays under explicit human control.

Fail-closed merge gate for AI coding agents: verify_repo runs the tests that existed before the change against a git working tree and answers MERGE, BLOCK or INCONCLUSIVE. The agent names what to judge; the operator pins the baseline and the floors.

Enables AI coding agents and CI to vet npm dependencies before they reach the lockfile, flagging hallucinated, slopsquatted, or otherwise risky packages with evidence-backed verdicts.

Enables coding agents to read Squally CI data, including test results, flakiness, and failure diagnostics.

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

Generates a daily standup note from GitLab merge request or GitHub pull request state: what is ready to merge, what is blocked, what is stale, and what is waiting on your review. When CI is red it opens the failed job's log and reports the error lines, so the note says why the pipeline failed rather than just that it did.

Enables AI coding tools to scaffold, build, and deploy Node.js or Python apps to Varity with a single command, automatically provisioning databases and backend services.

MCP server for publishing HTML or Markdown to a live hosted URL via htmldrop. Provides tools to publish, list, and delete hosted sites, with remote OAuth and API token authentication.

Provides MCP tools to get a single-call snapshot of git and GitHub Actions CI status across all repositories in a folder, flag missing or stale architecture docs, write doc files, and detect release drift between source and release repositories.

SSWP MCP — Deterministic software attestation for AI-augmented development. Witness any repo through a 5-gate pipeline, adversarially probe dependencies, and produce self-verifying .sswp.json attestations. Fleet registry across 131 nodes with tamper-proof audit ledger and FTS5 search. Agent-native — one tool call from Hermes, Claude, or Cline.

MCP server for GitLab CI/CD — pipelines, jobs, schedules, branches, tags, merge requests and repository files. Works with any GitLab (SaaS gitlab.com or self-hosted) via python-gitlab.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

An MCP server that enables AI assistants to discover and execute Nox sessions for project automation tasks like testing, linting, and building. It provides tools to list available sessions and run them using specific Python versions, tags, or keyword expressions.

Diffgate MCP server acts as a code review engine, enabling AI coding agents to analyze and validate code diffs before application. It enhances AI workflows by providing self-checking capabilities to optimise and secure code changes.