"Providing Mathematical and Scientific Calculation Functions for a Single-Modal LLM" matching MCP servers:

A stateless MCP server that exposes arithmetic operations (add, subtract, multiply, divide) as tools over HTTP, enabling AI assistants to perform basic calculations conversationally.

Local, read-only Modal context for coding agents. Enables agents to inspect Modal apps, deployments, logs, containers, volumes, and sandboxes without granting mutation tools.

Connects AI agents to Modal.com infrastructure, allowing management of deployments, apps, containers, volumes, secrets, and environments via MCP tools.

An MCP server that allows users to deploy Python scripts to Modal directly from Claude, providing a link to the deployed application that can be shared with others.

A local MCP gateway that unifies multiple LLM providers, API keys, routing, and multi-model discussions into a single tool surface.

Lets Claude Code send prompts to a locally running Qwen model on Apple Silicon and get answers back, with optional thinking mode for harder problems. It also lists the registered models and reports whether each one is running.

Centralized encrypted gateway that routes requests to 11+ LLM providers (API keys and CLI subscriptions) through a single OpenAI-compatible endpoint, with MCP tools for vault operations, code search, and shared state.

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

Enables coding agents to query the current free LLM model lineup mid-task, check what free models were added or removed since the last scan, and look up curated provider free tiers with limits and caveats, so they stop hard-coding model IDs that silently break.

This MCP server enables MCP clients such as Claude Desktop to run LLM-generated code in a secure, isolated sandbox across multiple container backends and programming languages.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

Share code context with LLMs via MCP or clipboard

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables mathematical and scientific computations including linear algebra operations (matrices, eigenvalues, decompositions), vector calculus (gradients, curl, divergence), and visualization of vector fields and functions.

MCP server for managing a project backlog as Markdown files in Git, enabling AI agents to read, create, and update tasks programmatically.

A quickstart template to build and deploy a remote MCP server on Azure Functions using Python, secured with keys and HTTPS.

Token cost math for LLM API calls: current per-million-token rates for 69 models across 17 providers, with local arithmetic for estimates, comparisons and monthly budgets. Rates are verified and date-stamped.

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and fetching weather data to demonstrate basic tool implementation and API integration patterns.

Provides real-time LLM pricing and availability data as an MCP server, enabling AI agents to make optimal model routing decisions at inference time with cited pricing sources.