AgentPatch vs Toolhouse: Comparing MCP Tool Providers
AI agents need tools. Web search, email, image generation, data lookups. The Model Context Protocol (MCP) gives agents a standard way to call those tools. But someone still has to host them.
Two services take different approaches to that problem. Both let you skip the Docker containers, the API key juggling, and the version pinning. Both give your agent access to external capabilities through a single connection. The similarities end there.
Toolhouse: SDK-first MCP integration
Toolhouse positions itself as an integration platform for MCP tools. You install their SDK, connect it to your agent framework, and get access to a managed set of tools.
The core value proposition is smooth connection management. Toolhouse handles the MCP transport layer, tool discovery, and execution. You write a few lines of SDK code, and your agent can call tools without knowing anything about the underlying APIs.
Toolhouse provides SDKs for Python and TypeScript. You initialize a client, pass it your agent’s messages, and the SDK handles tool calls and responses. The focus is on making the MCP handshake invisible to your application code.
Tool hosting is managed. You pick tools from their catalog, and Toolhouse runs the MCP servers on their infrastructure. No containers to manage, no endpoints to monitor.
The pricing model is usage-based with a free tier. You pay for tool executions beyond the free allowance.
AgentPatch: marketplace with protocol flexibility
AgentPatch takes a different angle. It is a tool marketplace where every tool is available through three access methods: MCP, REST API, and CLI.
The MCP connection works like you would expect. Point your agent at the MCP endpoint, authenticate with an API key, and every tool in your account appears as an MCP tool. But the same tools are also callable through plain HTTP requests and through command-line invocation. If your agent framework does not support MCP, or if you are building a shell script that needs to call a tool, the REST and CLI paths exist as first-class options.
Pricing uses a credit system. One credit equals $0.0001 USD, so 10,000 credits cost one dollar. Each tool lists its price upfront. A Google search costs a few credits. Image generation costs more. Failed calls get refunded. There are no monthly minimums and no seat-based pricing.
API responses are optimized for LLM consumption. Tool outputs are structured and compressed to minimize token usage when fed back into a model’s context window. This matters when your agent chains multiple tool calls in a single conversation.
Multi-agent support is built in. Multiple agents can share a single API key with separate usage tracking, or each agent can have its own key under one account.
Where they overlap
Both services solve the same root problem: you should not have to run MCP servers yourself. Cloning GitHub repos, configuring environment variables, keeping containers alive, and handling version updates is maintenance work that does not make your agent smarter.
Both give you a catalog of pre-built tools. Both handle authentication to upstream APIs (Google, Bing, email providers) so your agent only needs one credential. Both support the MCP protocol.
If your only requirement is “I want hosted MCP tools that work,” either service will get you there.
Key differences
Protocol flexibility. Toolhouse is MCP-native. That is its strength and its constraint. AgentPatch supports MCP, REST, and CLI access to every tool. If you are building with Claude Code, a REST-based agent, or a bash automation pipeline, the non-MCP paths matter.
Pricing model. Toolhouse uses a tiered plan with a free allowance. AgentPatch uses per-call micropayments with no monthly commitment. For sporadic usage, credits avoid paying for capacity you do not use. For heavy, predictable usage, a flat tier might be cheaper.
Agent breadth. Toolhouse focuses on SDK integration with popular agent frameworks. AgentPatch is agent-agnostic. It works with any agent that can make HTTP calls, run CLI commands, or speak MCP. The target is broad: Claude Code, OpenAI Codex, custom agents, scripts, anything.
Response optimization. AgentPatch compresses and structures tool outputs for LLM context windows. This reduces token costs when your agent processes tool results. Toolhouse returns standard MCP responses.
How to choose
Pick Toolhouse if you are building with a supported SDK framework and want the tightest possible integration. The SDK approach means less boilerplate in your agent code, and the managed hosting is solid.
Pick AgentPatch if you need protocol flexibility, if you run multiple agents on different frameworks, or if you want pay-per-call pricing without a subscription. The REST and CLI fallbacks mean you are never locked into one protocol.
You can also use both. There is nothing stopping you from connecting Toolhouse for some tools and AgentPatch for others. MCP clients can connect to multiple servers.
Wrapping up
Toolhouse and AgentPatch both eliminate the pain of self-hosting MCP servers. Toolhouse does it through a polished SDK experience focused on MCP. AgentPatch does it through a marketplace model with multiple access protocols and micropayment pricing.
The right choice depends on your agent architecture. Single framework, single agent, SDK-friendly? Toolhouse fits well. Multiple agents, multiple protocols, variable usage? AgentPatch is the better match.
Try both. The setup cost for either is about five minutes and an API key.