"Information about Oracle Cloud services" matching MCP servers:

A thin MCP server that wraps provenance-receipts and quality-gate services as tools, enabling AI agents to certify content origin and score quality via Ed25519-signed receipts.

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.

Enables read-only querying, evaluation, challenge, and judging of an Oracle project's rules and evidence without modifying the filesystem, plus task tracking for agents.

An MCP server that connects AI assistants to Xray Cloud for Jira test management, enabling natural language interaction with tests, test plans, executions, and CI result imports.

Enables access to Xray Cloud test management on Jira Cloud through its REST v2 and GraphQL APIs, with configurable read/write toolsets, single-tool selection, and read-only or per-request credential modes.

Enables automated access to LoadRunner Cloud APIs for retrieving performance test data, managing projects, and collecting test results. Supports integration with AI clients for building performance engineering workflows and dashboards.

Enables AI assistants to interact with Zephyr Scale Cloud for test management, including test cases, cycles, plans, folders, priorities, and statuses via the MCP protocol.

A paid remote MCP server for OpenAI Codex harness, offering structured JSON tools for coding task planning, safe command suggestions, test verdict recording, and run history.

MCP server for Xray Cloud test management that enables importing automation results and managing tests, test executions, and test sets in Jira.

An MCP Server that provides access to Google's Cloud Tool Results API, enabling applications to programmatically interact with test results from Firebase Test Lab and mobile app testing through natural language.

Evaluates AI agent and MCP server competency across 6 quality dimensions using challenge-response tests, returning quality scores and W3C Verifiable Credentials.

A remote MCP server that exposes mock application security testing data (applications, scans, issues) for LLM clients to query security posture using natural language.

Enables Cursor AI to query SonarQube for pull request metrics, issues, file issues, and duplication reports directly, without manual data export.

Enables code agents to run risk-based Jev quality reviews on focused diffs through a read-only MCP tool, returning structured scores and weakness hints without modifying code.

Gives any agent eyes and ears by indexing video, audio, and screen activity into searchable, timestamped frames, transcripts, and OCR text that answers can cite. It also evaluates frozen verification contracts over file digests, JSON values, SQL results, HTTP responses, and DOM state to return a deterministic verdict from a separate process.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

A read-only MCP server that exposes Zephyr Scale Cloud test data to review a Jira story's test coverage, answering whether tests are adequate and passing.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp

MCP server that gives Claude deep awareness of your frontend codebase by scanning components, hooks, services, routes, and data flow for React or Vue projects.