Turn any OpenAPI/Swagger spec into an agent-ready MCP server. Point it at a spec; every operation becomes a tool. (YC RFS: Software for Agents)
claude mcp add agentify -- npx -y agentify-openapi{
"mcpServers": {
"agentify": {
"command": "npx",
"args": ["-y", "agentify-openapi"]
}
}
}MCP Servers overview
# agentify
**Turn any OpenAPI / Swagger spec into an agent-ready MCP server.**
[](https://www.npmjs.com/package/agentify-openapi)
Listed on the [official MCP Registry](https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.sani-savaliya/agentify) (`io.github.sani-savaliya/agentify`) and [Smithery](https://smithery.ai/servers/sanisavaliya12/agentify).
Point it at a spec — a URL, a file, OpenAPI 3.x or Swagger 2.0 — and every operation
becomes a tool an AI agent can call. No code generation, no per-API boilerplate, no
hosting. One command.
```bash
npx agentify-openapi https://petstore3.swagger.io/api/v3/openapi.json --list
```
```
Swagger Petstore - OpenAPI 3.0 v1.0.27
Base URL: https://petstore3.swagger.io/api/v3
Tools: 19
getPetById
Find pet by ID.
findPetsByStatus
Finds Pets by status.
...
```
## Why
> *"Today, agents have to operate software designed for humans. The interfaces of
> the future will be built for agents — APIs, MCPs, CLIs — with agents as
> first-class citizens."* — [YC RFS: Software for Agents](https://www.ycombinator.com/rfs)
There are tens of thousands of APIs that already describe themselves with an OpenAPI
document. `agentify` makes every one of them agent-native, instantly, without anyone
hand-writing an integration.
## Use it with Claude / any MCP client
Add to your MCP client config (e.g. Claude Desktop / Claude Code):
```json
{
"mcpServers": {
"petstore": {
"command": "npx",
"args": ["-y", "agentify-openapi", "https://petstore3.swagger.io/api/v3/openapi.json"]
}
}
}
```
The agent now has one tool per API operation. Calling a tool builds the HTTP request
(path params, query string, headers, JSON body) and returns the live response.
## Auth
Provide credentials via environment variables — `agentify` reads the spec's declared
security scheme to find the right header name when it can:
| Variable | Effect |
|---|---|
| `AGENTIFY_BEARER_TOKEN` | `Authorization: Bearer <token>` |
| `AGENTIFY_BASIC_USER` / `AGENTIFY_BASIC_PASS` | HTTP basic auth |
| `AGENTIFY_API_KEY` | API key (sent as a header by default) |
| `AGENTIFY_API_KEY_HEADER` | Override the api-key header name |
| `AGENTIFY_API_KEY_QUERY` | Send the api key as a query param instead |
You can also inject raw headers from the CLI: `--header "X-Org-Id: 42"` (repeatable).
## CLI
```
agentify <spec-url-or-file> [options]
--base-url <url> Override the API base URL from the spec
--header "K: V" Add a raw header to every request (repeatable)
--name <name> Override the MCP server name
--list Print the discovered tools and exit (no server)
-h, --help Show help
```
## How it works
A small, pure pipeline — each stage is independently unit-tested:
```
spec ──▶ operations ──▶ tool defs ──▶ http request ──▶ response
│ │ │ │ │
load & one tool JSON Schema path/query/ fetch + surface
deref per op for inputs header/body status & body
$refs + auth
```
Only the HTTP execution and MCP transport touch the outside world; everything else is
deterministic and tested.
## Programmatic use
```ts
import { loadSpec, extractOperations, resolveBaseUrl, createServer } from "agentify-openapi";
const spec = await loadSpec("./openapi.yaml");
const tools = extractOperations(spec);
const baseUrl = resolveBaseUrl(spec);
// ...build your own MCP server, or just inspect the generated tool defs
```
## Limitations (v0.1)
- JSON request/response bodies are first-class; `multipart`/form bodies are passed
through best-effort.
- `cookie` parameters and OAuth2 flows are not yet handled (use `--header` for now).
- One server per spec. Multi-spec aggregation is on the roadmap.
## Development
```bash
npm install
npm test # vitest, 80%+ coverage enforced
npm run build # tsc -> dist/
node scripts/smoke.mjs # end-to-end MCP client smoke test (network)
```
## License
MIT
What people ask about agentify
What is sani-savaliya/agentify?
+
sani-savaliya/agentify is mcp servers for the Claude AI ecosystem. Turn any OpenAPI/Swagger spec into an agent-ready MCP server. Point it at a spec; every operation becomes a tool. (YC RFS: Software for Agents) It has 0 GitHub stars and was last updated today.
How do I install agentify?
+
You can install agentify by cloning the repository (https://github.com/sani-savaliya/agentify) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is sani-savaliya/agentify safe to use?
+
sani-savaliya/agentify has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.
Who maintains sani-savaliya/agentify?
+
sani-savaliya/agentify is maintained by sani-savaliya. The last recorded GitHub activity is from today, with 0 open issues.
Are there alternatives to agentify?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
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