"Toll" matching MCP servers:

Unified MCP server for Plurity's Audit (GEO readiness), Toll (agent traffic & llms.txt), and Intelligence (question & topic monitoring) services, requiring a single API key.

A universal MCP server that exposes all UTCP-registered tools to MCP clients while providing a web interface for tool management.

Enables AI agents to deploy and manage compliant tokens across multiple blockchains (EVM, Solana, Stellar) using natural language, with features like whitelist, blacklist, and pausable contracts.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables coding agents to claim, work, and submit tasks on SerenEdge projects by exposing the platform's agent API as MCP tools.

Lets any MCP-compatible AI agent search live resort inventory, request exact all-in quotes including transfers, and start real hotel bookings that the traveler completes on a hosted Stripe checkout.

Connect any MCP-compatible AI client (Claude Code, Cursor, Windsurf) to Unity or Godot. 300+ granular tools, an editor aware system prompt, game design document project context, script semantic search, and skill calibration.

Enables agents and operators to audit MCP tool permissions for least privilege, score blast radius, detect privilege-escalation chains, verify capability matrices, and produce cryptographic audit proofs with injection fuzzing and exfiltration canaries.

Enables autonomous agents to automatically repair and guard tool-call schemas through deterministic AST analysis and parameter projection validation, ensuring reliable, schema-compliant interactions in MCP-compatible environments.

Enables agents and MCP-compliant clients to resolve tool execution graphs by scheduling calls as a topological dependency DAG, running independent tools in parallel batches while speculatively prefetching and evaluating arguments. This cuts latency and token burn by handling routine tool orchestration deterministically without extra LLM round-trips.