"Actual Budget" matching MCP servers:

Provides constraint satisfaction and optimization capabilities to LLMs and AI agents for scheduling, resource allocation, routing, budget optimization, and configuration problems using Google OR-Tools CP-SAT solver.

A Model Context Protocol server that lets Claude (and any MCP client) cite actual Canadian financial-services regulations: OSFI, PIPEDA, FINTRAC, Quebec Law 25.

Enables LLMs to create, query, and manage MongoDB-compatible databases using natural language without actual data storage. Uses statistical modeling to generate realistic data on-the-fly from sample documents or descriptions.

Enables AI-powered web searches using Google's Gemini 2.5 models with Google Search Grounding. Supports multiple Gemini models (Flash, Flash-Lite, Pro) with configurable thinking budget and web search capabilities.

TinyContext provides a local, token-efficient memory layer for AI agents. It enables saving and recalling memories via MCP tools, using hybrid BM25 and dense retrieval to return only context that fits the requested token budget.

Enables AI coding agents to query repository structure, symbols, dependencies, Git history, architecture, memory, impact, and guarded edits through a stable MCP interface, returning ranked task-specific evidence within a context budget.

MCP server for semantic research: search arXiv, fetch papers, and answer questions grounded in the actual paper text via RAG tools, resources, and prompts.

Enables secure access to legal documents from Clio via Claude Desktop, with local processing and semantic search to ground AI responses in actual documents.

Searches real developer solutions from Stack Overflow, Reddit, GitHub issues, and forums to find battle-tested fixes and workarounds for specific programming problems. Bypasses AI guessing by finding actual community discussions where developers have already solved your exact issue.

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

MCP server for local semantic search over web content, enabling AI agents to ingest, index, and query pages with hybrid retrieval and token budget control.

MCP-native persistent-memory agent that remembers user preferences across sessions, forgets superseded facts, and recalls relevant memories within a tight token budget.

Filters an agent's already-retrieved memories to fit a token budget using the memory_select tool, while keeping API keys in the environment. It works locally via MCP stdio or HTTP with optional JEV gateway support.

Provides tools for document search, AWS cost estimation, checking latest AWS updates, and creating budget alerts, with a confirmation gate on the mutating tool.

A Dockerized MCP server that fetches your GitHub repositories, indexes them into a local vector database, and exposes semantic code search tools to LLM clients for tailored technical mock interviews grounded in your actual code.

Exposes read-only knowledge base articles on returns, warranty and shipping policies alongside tools that search, retrieve and create real support tickets. This lets an LLM agent ground its answers in store policy without inventing information, and act on actual ticket data rather than simulated records.

Enables AI-powered procurement policy compliance, document generation, supplier management, budget verification, and analytics through standardized MCP tools.

Turns Confluence documentation into an AI-powered knowledge base, enabling natural language questions about your systems with answers retrieved from your actual documentation through semantic search.

Show an AI coding assistant only the code that matters for a change — callers, callees, and related functions packed into a token budget, with self-grading retrieval.

Enables coding agents to retrieve only the JavaScript/TypeScript code needed for a given task within a token budget, using TypeScript compiler analysis and a decision model to judge relevance.