Enables agents to forecast dynamic airline ticket fare volatility and produce optimal buy-now versus wait-and-freeze recommendations through a zero-dependency MCP STDIO interface.

Enables agents to generate optimized multi-day travel schedules and decision payloads via MCP, applying TSP graph heuristics with fatigue decay and opening-hour constraints.

Enables agents to plan multi-day travel schedules by solving itinerary graphs with TSP heuristics, fatigue decay, and opening-hour constraints, exposed over MCP STDIO JSON-RPC for agent runtime integration.

An MCP server that lets AI agents automate Microsoft Edge, organizing sessions into labelled tab-group workspaces or separate profiles and providing tools for browser control, screenshots, extraction, and secure sign-in through Edge's password manager.

Open-source HCM system for managing HR processes with AI agents. Full data ownership, MCP/A2A-native, no vendor lock-in.

A self-hosted MCP memory server that gives every MCP-compatible agent a shared, persistent memory layer, so preferences, project conventions, and past decisions carry across sessions and across different agents and vendors. It combines hybrid keyword-plus-vector retrieval with automatic context injection, zero-config sync, and selective injection of memories by agent and priority.

Human-in-the-loop (HITL) for AI agents: ask a person for approvals or answers by push notification. One command connects Claude Code, Codex, Cursor, Gemini CLI and more.

Native Model Context Protocol (MCP) server turning AI coding agents into .NET runtime diagnosticians, memory forensic analysts, and CIL binary architects.

Deterministic Unit-Record Memory Engine for AI coding agents. Eliminates context-window amnesia and compaction bloat via zero-turn workspace hydration, byte-exact registers, and live context telemetry.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

Enables AI agents and LLMs to persist facts, preferences, and observations in a local SQLite store and to build and traverse subject-predicate-object knowledge graphs across sessions. It provides fuzzy keyword search and graph queries so assistants retain context and recall prior project history without repeating prompts.

Enables AI agents to drive a real Chromium browser by receiving compact, token-efficient page maps and only the changes after each action. It guards against hidden prompt injections, blocks payments, sends and deletions until the user approves, and lets agents learn site-specific notes over time.

Enables agents to bring claims, situations, and Scripture passages to five doors (Check, Find, Walk, Word, Keep) and receive a verdict, the worked reasoning trail, cited sources, and a re-checkable content-addressed seal. It covers 77 verifier domains and 880k+ cards fully offline, so answers are found, verified, and sealed rather than generated.

Exposes the full Plexim PLECS Standalone JSON-RPC interface as MCP tools, letting agents auto-start and connect to PLECS to load and edit models, run simulations and analyses, manipulate scopes and webserver data, and perform code generation or FMU export with structured, size-aware results. Includes write guardrails, concurrency handling for long simulations, and batch/raw passthrough escape hatches.

Enables AI agents to look up Chinese laws and statutory articles fully offline from a local SQLite database, resolving a law name plus article number to verbatim text, searching articles by keyword, and retrieving a law's chapter structure and version history. It also verifies citations in bulk, checking whether each quoted provision exists word-for-word and is currently in force.

Zero-custody DeFi intelligence and control MCP server for AI agents, exposing Trail Heat, wallet and risk intelligence, swap and futures workflows, x402 access, and policy-gated execution. FarmDash never receives customer private keys.

Enables agents to query real-time GPU rental prices across 26 platforms, including current lowest quotes, 30-day price history, market movers, platform reliability, and task/budget recommendations. Access is read-only and metered per call via x402 with USDC on Base, requiring no signup or API key.

A hosted remote MCP server that gives AI agents live hotel, Airbnb/Vrbo and flight pricing tools — nightly rate calendars up to 330 nights, every booking site's price for a stay compared against the hotel's own site, guest reviews, and Google Flights search. Clients connect over Streamable HTTP with browser OAuth or an API key, so nothing needs to be installed or run locally.

Enables agents to retrieve permission-filtered document knowledge across organizations and knowledge bases, and to identify and expand business terms and typed object relationships through a unified MCP interface.

Provides Model Context Protocol access to the SEAF ArchTool REST API, enabling agents to inspect sections and entities, navigate relations, query datasets, and run JSONata expressions against a SEAF portal.

Enables VS Code Copilot Chat and custom agents to perform MongoDB queries, writes, transactions, schema discovery, index and collection management, and administrative commands across multiple secure connection profiles without using mongosh or PowerShell.

Gives browser agents a reviewed map of 79 real sites — entry URLs, access verdicts, and role/accessible-name button maps for 104 screens — so an agent can find where things are and know whether it may act at all. Safety rules in code halt irreversible actions such as pay, order, submit and delete pending explicit human approval, while per-site memory recalls which routes worked last time.

Exposes a governed dbt MetricFlow semantic layer to AI agents through three tools — list metrics, list dimensions and query metrics — so questions about federal contract spend are answered from defined metrics rather than raw tables. Guardrails reject undefined metrics, raw columns, SQL in filters and bulk exports, and no tool can execute SQL.