"Apache Spark" matching MCP servers:

An MCP server that optimizes Apache Spark code using Claude AI, providing intelligent code optimization suggestions and performance analysis.

Enables lookups across Apache Iceberg Javadocs, local source evidence, and Iceberg REST Catalog operations through MCP tools and prompts.

An MCP server that wraps multiple vendors' Coding Plan services (iFlytek Spark, Volcengine) to provide code review, plan review, and multi-turn dialogue for AI coding tools like Claude Code.

Open-source scanner that finds authorization logic embedded in application code. Precision-first static analysis with per-language rule packs, externalization percentage and per-finding confidence; generates OPA/Rego policy stubs. Apache 2.0, no telemetry.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

Enables coding agents to run deterministic, zero-dependency AST static analysis over source code, building call graphs and dependency metrics to find orphaned functions, unreachable methods, breaking API surface diffs, and token-bloat clusters, then return a structured refactoring plan. It plugs into MCP clients like Claude Desktop, Cursor, and Windsurf via a single Python entrypoint.

Enables coding agents and developers to parse Python source into ASTs and compute complexity metrics that pinpoint refactoring hotspots, dead code, and breaking API diffs. It returns decomposition plans and dependency-graph telemetry over MCP, keeping token usage low with zero external dependencies.

Enables automated analysis of test suite traces to identify timing races and state pollution, helping developers mitigate flaky tests through MCP-compliant clients.

Enables coding agents and MCP-compatible clients to parse multi-file AST dependency graphs and extract minimum context through symbol call-tree slicing. It provides deterministic, zero-dependency execution over JSON-RPC stdio.

Enables AI-powered code review and analysis of asynchronous JavaScript/TypeScript control flow, identifying unawaited promises and race conditions through AST taint-flow tracing and ESLint-style audits within MCP-compliant clients.

Enables whole-program callgraph reachability analysis to locate dead functions and stale flags, and prune unreachable AST branches. It integrates with MCP-compliant clients for deterministic, zero-dependency code review and semantic analysis.

Enables coding agents to analyze Python source with AST-based static analysis, detect circular dependencies across monorepo packages via Tarjan SCC, and produce a topological build order with Kahn's algorithm along with refactoring plans and graph metrics. Runs with zero external dependencies over MCP from Claude Desktop, Cursor, or any compliant client.

Enables AI agents and MCP clients to compute real-time, in-memory unified diffs between versions of live artifacts along with structured change telemetry. It runs as a deterministic, zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, or Windsurf for diff and change-tracking workflows.

Enables coding agents and developers to detect SemVer 2.0 breaking changes by statically diffing Python AST public API surfaces, catching removed exports, parameter reordering, and other incompatible signature shifts. It returns deterministic refactoring plans and dependency graph metrics without any external pip dependencies.

Enables developers and coding agents to refactor Python codebases by re-aliasing imports and migrating dependencies, safely rewriting module paths and callsites through deterministic AST analysis instead of error-prone regex. It also surfaces dependency graphs and refactoring plans that minimize LLM token usage.

Enables automated semantic analysis of Git pull request diffs to detect breaking REST/GraphQL API schema changes, integrating with MCP-compatible clients for AI-driven code review.

Enables automated Solidity smart contract auditing by analyzing callgraphs and control flow to detect reentrancy vulnerabilities. Integrates with MCP-compatible clients to provide deterministic, zero-dependency security assessments.

Enables static AST-based taint flow tracing from untrusted sources to sensitive sinks, supporting AI code review and security analysis via MCP.