"Statistics of China's Population" matching MCP servers:

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r

Provides LLMs with deterministic math tools for sandboxed expression evaluation, descriptive statistics, primality testing, prime factorization, GCD/LCM, quadratic solving, and base conversion. Every operation runs real code rather than relying on the model's arithmetic, with expression parsing restricted to a whitelist of operators, constants, and functions.

econ-r-mcp is a local MCP server plus companion skill for reproducible applied econometrics and statistics workflows powered by headless R execution.

Enables easy gRPC requests and Protocol Buffer file information retrieval through natural language commands. Supports unary RPCs with SSL, timeout configuration, and response time statistics.

Provides secure mathematical computation capabilities including expression evaluation, symbolic math (derivatives, simplification), matrix operations, statistics, and unit conversion, with multi-tier acceleration through WebAssembly and WebWorkers for high-performance calculations.

Enables LLM agents to parse, validate, and solve MiniZinc constraint models directly from chat sessions, returning solutions and solver statistics.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of tools (calculator, greeting) and resources (server info).

Provides a suite of deterministic math tools powered by SymPy to handle algebra, calculus, linear algebra, and statistics via the Model Context Protocol. It enables smaller language models to delegate complex computations to a verified symbolic backend for accurate and reliable results.

A simple implementation of a Model Context Protocol server that demonstrates core functionality including mathematical tools (add, subtract) and personalized greeting resources.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of calculator and greeting tools plus resource management capabilities.

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

MCP server exposing SLAC (Simple Logic & Arithmetic Compiler) as tools for LLM agents, enabling evaluation of expressions and retrieval of syntax reference.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with examples of tools (calculator, greeting) and resources (server info).

Provides AI agents with tools to retrieve live system statistics (CPU, memory) and safely evaluate basic arithmetic expressions.

A boilerplate project for quickly developing Model Context Protocol servers using TypeScript SDK, with example implementations of calculator tools, greeting functionality, and resource management.

A sample implementation of Model Context Protocol server demonstrating core functionality with simple arithmetic tools and greeting resources.

A Python-based implementation of the Model Context Protocol that enables communication between a model context management server and client through a request-response architecture.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.