"GitNexus - Git Repository Management Tool or Platform" matching MCP servers:

MCP server for operating and testing Android devices with AI agents. Provides observation and action tools for interactive control and spreadsheet-driven test runs.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Enables Claude Code to read and write issues on the Testing Platform via natural language, including listing projects, filtering issues, creating and updating issues, and adding comments.

Enables AI clients to query and operate Git for a Cocos Creator 3.8 project and its submodules, drive editor asset/scene/prefab tools, and inspect the running preview's hierarchy, properties and page events through a scoped, token-gated bridge that previews every write as a one-time plan and never executes arbitrary code.

Enables AI agents to inspect Git repositories for changes across committed, staged, unstaged, and untracked scopes, and run allowlisted validation commands with structured results.

Connects an LLM to specialized MCP servers via stdio, enabling authenticated GitHub repository, issue, file, and code operations alongside TF-IDF retrieval over employee PDF records and QA test-case generation and result validation. It exposes tools, a read-only resource, and reusable prompt templates so a client can discover and orchestrate multiple capabilities in one workflow.

Enables AI agents to interact with 1C: Enterprise development environment, including running tests, managing launch profiles, building configurations, and performing database operations through the 1C: Platform Tools extension.

Enables verification of deployment-scoped model-visible DSH tool surface conformance by checking recorded tool identities and schemas against approved locks, reporting only drift categories and hashes.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.

A throwaway HTTP MCP server that empirically tests whether Cursor invokes tools by exact name or munges them, to inform case normalization decisions.

A lightweight multi-provider MCP testing fixture with two providers (eva_air and uk_railway), each exposing 5 tools using in-memory fake data, designed for verifying MCP client tool discovery, tool calls, and multi-provider routing.

This MCP server verifies that an agent’s claimed tool output matches the actual response returned by a tool to prevent invented or distorted results. It flags mismatches, omissions, and invented fields to ensure downstream logic only processes verified tool data.

Enables deterministic detection of pytest config-leakage that corrupts agent-benchmark grading, returning CLEAN/CORRUPTED verdicts and corrected pytest commands.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

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.

An LLM-driven API testing platform exposed over MCP, enabling declarative test definition and DAG-based execution.

Comprehensive MCP server for iOS Simulator automation, enabling AI agents to control simulators via tap, swipe, type, accessibility inspection, app debugging, and device management.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

Enables autonomous agents to automatically repair and guard tool-call schemas through deterministic AST analysis and parameter projection validation, ensuring reliable, schema-compliant interactions in MCP-compatible environments.

Exposes a fictional smart-home store's REST API to agents as schema-validated MCP tools, resources, and prompts, letting them search products, check inventory, place atomic orders, look up customers and orders, and open support tickets. It also adds auth injection, rate limiting, REST-to-MCP error mapping with actionable hints, per-call JSONL tracing, and a golden-task eval harness for tool selection and argument correctness.

An MCP server that exposes a document store for evaluating agent tool-use accuracy and measuring resistance to indirect prompt injection, with built-in conformance to the 2026-07-28 MCP protocol.

Enables researchers to present 12 mock file, memory, schedule, and database tools over stdio and collect LLM pre-execution judgments (EXECUTE/CONFIRM/REFUSE) without executing real operations.

An interactive tool that enables users to benchmark vLLM endpoints through MCP, allowing performance testing of LLM models with customizable parameters.

Audits local package folders to generate publish-readiness reports and previews of NPM tarball contents. It helps developers identify and resolve configuration issues like missing types or broken exports before releasing code.

Provides browser automation tools for coding agents—goto, browse, goal, page_state, and summary—so agents can drive real pages through auditable, gated decisions and record traces for offline replay.

An MCP-style stdio server for evaluating AI agent outputs, enabling CI-friendly quality gates, regression comparisons, and canary promotion decisions.

Enables agents and operators to audit MCP tool permissions for least privilege, score blast radius, detect privilege-escalation chains, verify capability matrices, and produce cryptographic audit proofs with injection fuzzing and exfiltration canaries.

Enables agents to classify every claim into evidence tiers (FACT/INFERENCE/SPECULATION/UNVERIFIED) with attached evidence, forcing deterministic, evidence-gated reporting without LLM or network calls.

Runs deterministic local environment checks against a repository and returns a JSON pass/fail report covering Node version, git and npm availability, lockfiles, and env-file key completeness without executing repository code or exposing secret values.

Enables AI agents to drive a local asset review loop: list and fetch paired original/output items (including image content for visual comparison), mark pass/reject/unsure with problem tags and region annotations, batch-review scoped selections, and export results as JSON or CSV. Also supports round archiving, detecting stale results after source re-renders, and listing previous-round rejects.