openrpc-mpc-server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openrpc-mpc-servercall the getBalance method on the Ethereum node at https://mainnet.infura.io"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OpenRPC MCP Server
A Model Context Protocol (MCP) server that provides JSON-RPC functionality through OpenRPC.
https://github.com/user-attachments/assets/3447175a-f921-4ded-8250-b611edb2fb67
Features
Tools
rpc_call- Call arbitrary JSON-RPC methodsSpecify server URL, method name, and parameters
Returns JSON-formatted results
rpc_discover- Discover available JSON-RPC methodsUses OpenRPC's
rpc.discoverspecificationLists all methods on a given server
Related MCP server: MCP REST API Server
Development
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchInstallation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"openrpc": {
"command": "npx",
"args": ["-y", "openrpc-mcp-server"]
}
}
}Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector:
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
Available Tools
2 toolsrpc_callC
Call any JSON-RPC method on a server with parameters. A user would prompt: Call method on with params
| Name | Required | Description | Default |
|---|---|---|---|
| server | Yes | Server URL | |
| method | Yes | JSON-RPC method name to call | |
| params | No | Stringified Parameters to pass to the method |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs a JSON-RPC call but lacks details on error handling, authentication needs, rate limits, or what the response might look like. The example prompt format adds minimal context but doesn't cover key behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences: one stating the purpose and one providing an example prompt. The structure is front-loaded with the core functionality, though the example could be slightly more informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that performs potentially complex remote procedure calls with no annotations and no output schema, the description is insufficient. It lacks details on error cases, response formats, authentication, or how to handle the stringified parameters, leaving significant gaps in understanding the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters. The description adds minimal value by mentioning parameters in the example prompt but doesn't provide additional semantics beyond what the schema provides, such as format expectations or usage examples for the 'params' string.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Call any JSON-RPC method on a server with parameters.' It specifies the verb ('Call'), resource ('JSON-RPC method'), and scope ('on a server'), though it doesn't explicitly differentiate from the sibling tool 'rpc_discover' beyond the example prompt format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'rpc_discover'. It only gives an example user prompt format without explaining the context, prerequisites, or exclusions for using this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rpc_discoverB
This uses JSON-RPC to call rpc.discover which is part of the OpenRPC Specification for discovery for JSON-RPC servers. A user would prompt: What JSON-RPC methods does this server have?
| Name | Required | Description | Default |
|---|---|---|---|
| server | Yes | Server URL |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions this is for discovery and references the OpenRPC Specification, it doesn't describe what the tool actually returns (method list, schema, etc.), error conditions, authentication requirements, or rate limits. The example prompt helps but doesn't fully explain behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence states the technical implementation, and the second provides a helpful usage example. Both sentences earn their place, though the structure could be slightly more front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description should do more to explain what the tool returns. While it mentions discovery, it doesn't describe the response format, whether it returns method names, schemas, or other metadata. The example prompt helps but doesn't fully address output expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single 'server' parameter as a URL. The description adds marginal value by implying this parameter should be a JSON-RPC server URL in the example, but doesn't provide additional format requirements or constraints beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool uses JSON-RPC to call `rpc.discover` for server method discovery, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this from its sibling tool `rpc_call`, which would handle actual RPC method calls rather than discovery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context with the example prompt 'What JSON-RPC methods does this server have? <server url>', indicating when to use this tool. It doesn't explicitly state when NOT to use it or mention the alternative `rpc_call` tool, but the context strongly implies this is for discovery rather than execution.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: rpc_call is for executing arbitrary JSON-RPC methods, while rpc_discover is specifically for server discovery to list available methods. There is no overlap in functionality, making them easy to distinguish.
Both tools follow a consistent 'rpc_' prefix pattern with descriptive suffixes (call, discover), using snake_case throughout. This provides a predictable and readable naming convention across the tool set.
With only 2 tools, the server feels minimal but appropriate for its narrow scope of JSON-RPC interaction. However, it lacks tools for common operations like server health checks or parameter validation, which could enhance usability in this domain.
The tools cover the core actions of calling methods and discovering server capabilities, which aligns with the OpenRPC domain. However, there are notable gaps such as error handling tools, batch request support, or schema validation that would provide more comprehensive coverage for JSON-RPC workflows.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
A Model Context Protocol (MCP) server for Selise Blocks Cloud integration
MCP Spec Compliance MCP — audits any MCP server.json against the official Model Context Protocol
An MCP server that provides an API to LLMs to manage their JumpCloud resources.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceA generic, modular server for implementing the Model Context Protocol (MCP).8445ISC
- FlicenseNot gradedqualityDmaintenanceA server implementation of the Model Context Protocol (MCP) that provides REST API endpoints for managing and interacting with MCP resources.
- AlicenseBqualityDmaintenanceA Model Context Protocol (MCP) server based on OpenRPC, providing JSON-RPC function invocation and method discovery services.21Apache 2.0
- AlicenseNot gradedqualityDmaintenanceModel Context Protocol server that standardizes tool discovery, execution, and context management for AI applications.MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/shanejonas/openrpc-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server