mcpField
P
panini-connector-mcp

panini-connector-mcp

Local
github.com/prak16Developer Tools
Repository

About panini-connector-mcp

Install and verify the panini-connector SDK on self-hosted sites, driven by AI agents

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "adithyavardan1-panini-connector": {
      "command": "npx",
      "args": [
        "-y",
        "panini-connector-mcp"
      ]
    }
  }
}
Run commandnpx -y panini-connector-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmpanini-connector-mcp0.1.3

Tools

6

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
storageenumYesWhere posts land. supabase = Supabase via PostgREST. postgres = direct Postgres DSN. custom_db = MySQL/Mongo/MSSQL/CosmosDB/CMS — SDK generates a StorageAdapter class scaffold. memory = ephemeral in-memory (testing only, posts vanish on restart).supabase | postgres | custom_db | memory
languageenumYesThe customer site's runtime language.node | python
frameworkenumYesThe customer's web framework. next_app = Next.js App Router, next_pages = Next.js Pages Router. Must match `language`: node → next_app|next_pages|express; python → fastapi|django|flask.next_app | next_pages | express | fastapi | django | flask
url_templatestringNoURL where published posts appear on the customer site. Use {slug} and/or {id} placeholders. Example: "https://mysite.com/blog/{slug}". Defaults to "https://yoursite.com/blog/{slug}".
existing_tablestringNoName of the customer's existing blog/posts table (e.g. "blog_posts"). For supabase/postgres, seeds the toRow mapping. For custom_db, seeds the SQL. Defaults to "blog_posts".

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.