"Understanding Boss Mode" matching MCP servers:

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Turns any OpenAPI spec into two sandboxed tools, search and execute, so an AI agent can discover endpoints and run small JavaScript programs against an entire API using under 1,000 tokens of context. Requests are read-only by default and the API key never enters the sandbox.

Code Mode MCP server for the OVH API. Two tools (search + execute) give LLMs access to all OVH endpoints via sandboxed JavaScript.

Enables AI agents to discover and read Typeship API documentation and execute API operations through schema-validated MCP tools, with optional read-only mode and configurable result limits.

MCP server that fetches API responses and generates ready-to-use model/POJO classes in your configured language, with built-in login/refresh token handling.

A server that interfaces with the Hedera Testnet Mirror Node API, converting its OpenAPI-defined endpoints into MCP-compatible tools that can be accessed over Server-Sent Events (SSE).

Enables AI assistants to interact with the Roark REST API, allowing them to explore endpoints, make test requests, and use documentation to help integrate this SDK into your application.

Lets an AI assistant run locally over stdio to inspect and test the Klap Core API — looking up charges and timelines, listing webhooks and deliveries, checking accepted token/network pairs, querying metrics, and, in test mode, creating charges, re-checking them on-chain, retrying deliveries and driving sandbox payments. Live environments are read-only unless writes are explicitly opted into, and money-moving or endpoint-management operations are never exposed.

Enables MCP clients to invoke every typeship API operation as a tool, with spec-derived input schemas, argument validation, read-only mode, and selective tool exposure.

A proxy server that bridges AI agents and external APIs by dynamically translating OpenAPI specifications into standardized MCP tools, enabling seamless interaction without custom integration code.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

Runs 19 simulated form-mode and URL-mode elicitation scenarios against a mock MCP server and client, exposing the raw JSON-RPC of every message, rendering the dialog a compliant client must show, and flagging MUST/SHOULD violations. Everything stays in-process and offline, so users can study consent, secret handling, retries, phishing, and broken-server behaviour without touching a real network.

A server that implements the Model Context Protocol for Schwab API, allowing access to account information, positions, stock quotes, and order/transaction history designed for integration with Large Language Models.

Enables reading and searching API documentation from YApi instances, allowing AI models to access interface definitions, project API lists, and search through API endpoints using YApi URLs or project IDs.

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

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.

A Python implementation of the MCP server that enables AI models to connect with external tools and data sources through a standardized protocol, supporting tool invocation and resource access via JSON-RPC.

Enables AI agents to securely list, retrieve, and search Freshdesk support tickets through typed read-only MCP tools. It supports API-key authentication, rate-limit handling, safe logging, and an offline fictional demo mode.

Enables AI assistants to interact with the Scorecard REST API for exploring endpoints, making test requests, and integrating the SDK into applications.

Turns AI coding agents into senior QA engineers by generating black-box test matrices, manual test plans, UI/UX checklists, Page Object Model automation, API contract tests, and STLC strategies, plus sandboxed local test execution and defect triage from raw logs. Eight tools cover exploratory charters, WCAG audits, and Jira-ready bug reports, all with injection-proof command handling.