"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP servers:

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables AI coding agents to perform zero-token static audits, simulate refactorings in an ephemeral RAM clone, trace downstream ripple effects, and block unsafe writes with automatic rollback to prevent regressions.

Enables AI coding assistants to inspect GitHub Actions CI runs, diagnose failures with failed-step summaries and filtered job logs, read per-job coverage, and view pull request checks through read-only, allowlisted access.

Enables AI tools to operate IBM i 5250 green screens, driving and testing interactive RPG programs by seeing screens, typing into fields, and pressing keys.

Godot 4 MCP server with: Test running (GUT/GdUnit4) with structured pass/fail results API docs search with 30+ Godot 3→4 migration mappings Script analysis detecting 10 common GDScript pitfalls Scene/resource file parsing with antipattern detection Viewport screenshot capture LSP diagnostics from Godot's language server TypeScript, cross-platform (macOS/Windows/Linux), published on npm

Conformance test harness for Model Context Protocol servers, validating JSON-RPC, transport, capability, schema, and more across multiple spec versions.

Enables Python developers to define MCP tools via decorators, auto-generate schemas and tests, and package compliant MCP servers with CLI scaffolding and JSON-RPC 2.0 stdio transport.

Drives Android, iOS, smart TV (Android TV, Tizen, webOS), web and Bluetooth (BLE) devices from an agent: start a session, tap, type, swipe and assert by XPath, visible text, OCR or image, then save the steps as a replayable CSV test suite.

Enables AI assistants to generate and statically verify IEC 61131-3 Structured Text logic, mutate .csp/.cpj OLE2 project containers, trigger in-GUI Error Check compilation, manage HMI screens, and run deterministic OCS scan-cycle simulations and Modbus telemetry scaling against Cscape 10.2. All physical hardware and firmware-download paths are intercepted fail-closed, so commissioning must be performed manually by authorized field personnel.

Enables AI coding agents to run Foundry tests and builds and receive compact, structured failure reports with decoded reasons, fuzz counterexamples or invariant call sequences, logs, and short traces. It also distinguishes genuine test failures from cases where tests did not run, returning actionable errors instead of false zero-failure results.

Enables agents and security automation to adjudicate proposed defense mutations against a sealed, canonicalized attack scenario, enforcing reproduction, impact-screening, regression, and proven-improvement gates before promoting at most one candidate. Every verdict, rejected candidate, and rejection reason is sealed into a recomputable SHA-256 Merkle evidence root and hash-linked lineage that can be independently verified without trusting the server.

A server implementation of the Model Context Protocol (MCP) for managing development workflow with features like project management, task tracking, and QA review support.

An experimental MCP server for MNCS-native development and evidence control, enabling Codex to manage project authority, candidate lineage, evidence gaps, and evaluator-mode boundaries.

Enables visual authoring and testing of MCP tools, resources, and prompts across Bun, Node.js, and Python via standard MCP transports.

A local MCP server that parses Robot Framework output.xml files, scores and groups failures, and exposes structured triage data to Claude Desktop and Claude Code.

Enables MCP clients to build Logisim circuits from natural language by adding pins, gates, LEDs and wires through tools that return exact port coordinates, then saving the result as a .circ file. It also runs Logisim's own loader to read the file back and report dangling wire ends and multi-driver shorts.

Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.

Enables measuring how the same MCP tools implemented in FastMCP, the official MCP Python SDK, and fastapi-mcp differ in token overhead, schema fidelity, and call latency.

Enables MCP-compatible clients to discover API endpoints from OpenAPI/Swagger specs, send HTTP requests, validate responses, and analyze status codes for automated API testing.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.