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One Shared Skill Set, Four MCP Configs: Claude Code, Codex, Cursor and Hermes

A shared skill set and canonical MCP inventory can reduce duplication, but Claude Code, Codex, Cursor and Hermes do not share one documented, zero-config MCP file.
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You can share skill content and keep one canonical list of the MCP servers your team wants, but the available documentation does not show one MCP configuration file that Claude Code, Codex, Cursor and Hermes Agent all use unchanged. Plan on portable skills plus agent-specific MCP configuration—or generate each client’s file from a shared inventory. That is a more reliable goal than “zero config.”

Why one shared MCP file is not a safe assumption

MCP is the connection layer between an agent and external tools; the client still determines how you declare servers, credentials, and transport. Cursor documents JSON configuration in .cursor/mcp.json for a project or ~/.cursor/mcp.json for a user. Hermes instead documents YAML in ~/.hermes/config.yaml, under mcp_servers, and supports stdio and HTTP servers. OpenAI provides separate Codex MCP documentation rather than asserting that Codex reads another agent’s config. The formats and locations are therefore not interchangeable by default.

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Hermes describes MCP as a way to connect to external tools such as GitHub, databases, file systems, browser stacks and internal APIs. The exact tools available still depend on configuring the relevant server in the client.

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See the Hermes Agent MCP documentation, Cursor MCP documentation and OpenAI Codex MCP documentation for their respective conventions.

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What can be shared across the four agents

Skill content

Skills are usually the more portable part because their instructions can live in Markdown files. Cursor documents project skills in .agents/skills/ and compatibility discovery of skill directories associated with Claude and Codex. This supports sharing skill content, but it does not prove that every agent discovers every directory or interprets every instruction identically. Check the discovery rules for the specific client and version you use.

Cursor also distinguishes project and user skill locations. A skill present on one developer’s machine is not automatically a project-level resource, and personal skills need syncing for Cursor Cloud Agents. See Cursor’s skills documentation and OpenAI’s Codex skills guide.

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Server inventory and intent

A team can maintain one version-controlled inventory of intended MCP servers: names, purposes, transports, endpoints, and which environments should use them. Treat that inventory as a source for the clients’ actual configurations, not as a file they are all guaranteed to consume. Keep secrets out of the shared inventory; provide credentials through each client’s supported mechanism.

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Rules, skills, and MCP settings are distinct. A skill describes reusable instructions; MCP configuration connects tools; rules may set broader behavior. Cursor documents rules separately from MCP and skills, so a shared skill set does not itself configure a server or synchronize policy. See Cursor’s rules documentation.

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How the four clients differ

Agent Documented sharing or configuration detail Practical implication
Claude Code Hermes documents importing a Claude Code mcpServers block and migrating associated skills and instructions. See Hermes MCP documentation. This is a route into Hermes, not evidence of universal or bidirectional synchronization.
Codex OpenAI publishes separate documentation for MCP configuration and skills. Do not assume Codex reads Cursor, Hermes, or Claude Code settings files unchanged.
Cursor MCP uses project or user mcp.json locations. Skills can be discovered from .agents/skills/ and compatible Claude and Codex skill directories. See MCP and skills. Project, user, and Cloud Agent scope differ; verify which skills and settings reach the environment where the agent runs.
Hermes Agent MCP servers are configured under mcp_servers in ~/.hermes/config.yaml; stdio and HTTP transports are documented. It also documents a Claude Code import. See Hermes MCP documentation. Its YAML structure differs from Cursor JSON and the Claude Code block it can import.

A practical shared setup

For a team using multiple agents, the maintainable pattern is one canonical source of intent with small client-specific outputs. You can write the adapters by hand or generate them, but the reviewed vendor documentation does not establish a universal generator for all four agents. Treat generation as your own workflow and validate its output rather than assuming compatibility.

  1. Keep portable skills in version control. Use Markdown instructions and a predictable project directory. Confirm each client’s discovery paths and whether the skill is available in local, user, and remote-agent contexts.
  2. Maintain a canonical MCP inventory. Record each server’s purpose, transport, endpoint or command, required environment, and authentication needs. Do not put tokens or passwords in shared files.
  3. Map the inventory to each client. Produce the specific JSON, YAML, or other format expected by the target agent, in its documented project or user location. Review the generated files for differences in transport and authentication fields.
  4. Validate in the actual runtime. Check that the agent discovers the skill, starts or reaches each server, receives the needed authorization, and can use it in the environment where work happens—including cloud or remote agents.
  5. Track changes per client. When a server endpoint, credential flow, or agent version changes, update the relevant outputs and retest them. The shared inventory reduces drift; it does not remove client-specific maintenance.

When Hermes’ Claude Code importer helps

Hermes documents hermes import-agent claude-code as a migration route that brings over Claude Code MCP settings, skills, and instructions. It is useful when moving that setup into Hermes. The documentation does not make it a general importer for Codex and Cursor or a live sync mechanism, so treat the result as a migration to inspect and maintain.

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What “zero config” would need to mean

A setup is not truly zero-config merely because skills are shared or a server inventory exists. Each client still has to find the right configuration, connect using a supported transport, obtain credentials, and expose the skill in the relevant environment. Before promising a no-touch workflow, verify these points for your exact agent versions:

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  • Which project and user paths the client reads.
  • Which skill directories it discovers, and whether remote agents receive them.
  • Whether it supports the server’s transport and authentication method.
  • How secrets are supplied without committing them.
  • Whether changes to the canonical inventory update every client-specific file.

The available documentation supports a shared skill strategy and a single source of truth for MCP intent—not a guaranteed, unchanged config shared by all four products. For volatile configuration details, consult the current OpenAI Docs MCP resource, Codex MCP guide, Cursor MCP guide, Cursor skills guide, and Hermes MCP guide.

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