mcp server
MetaEngine
Spec-first code generation: OpenAPI, GraphQL, Protobuf, and SQL to typed clients in 11 languages.
Description as published by the maintainer. Source
- version 1.4.1
- active
- code generation
active — Most recent push to the repository was 2026-07-07. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-07-07 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.4.1 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-07 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-07-07 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
Avrotize
— last commit 2026-08-04, shares protobuf
Schema conversion and schema-driven code generation MCP server.
-
io.github.archradhq/deterministic
— last commit 2026-05-21, shares codegen, openapi
Deterministic IR/IR-LINT validation, policy packs, drift vs exports (archrad). Apache-2.0.
-
com.knitli/codeweaver
— archived, last commit 2026-06-16, shares codegen
Semantic code search built for AI agents. Hybrid, AST-aware, context for 166 languages.
-
Unraid RMCP
— last commit 2026-08-05, shares graphql
Rust MCP server and CLI for Unraid GraphQL operations across NAS, Docker, VM, and storage workflows.
-
Unraid MCP
— last commit 2026-08-05, shares graphql
MCP server for Unraid API — provides tools to interact with an Unraid server's GraphQL API.
-
com.medplum/mcp
— last commit 2026-08-06, shares graphql
Securely access and manage FHIR healthcare data stored in Medplum.
-
Linear MCP (multi-workspace, PAT auth)
— last commit 2026-08-06, shares graphql
Multi-workspace Linear MCP — PAT auth, 57 tools + 3 prompts, substrate enforcement, drop-in.
-
Apollo MCP Server
— last commit 2026-08-06, shares graphql
MCP server that exposes GraphQL operations as tools for AI models.
-
io.github.Docat0209/graphql
— last commit 2026-03-23, shares graphql
Turn any GraphQL API into MCP tools. Zero config, zero code.
-
Soma
— last commit 2026-08-06, shares openapi
RMCP runtime for provider-backed agents with CLI, REST, HTTP MCP, plugins, and scaffold support.
These share tags the maintainers applied themselves, such as protobuf, codegen, openapi, graphql. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: claude, codegen, graphql, mcp, mcp-server, metaengine, model-context-protocol, openapi, protobuf.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab