Skip to main content
Glama

Convex MCP server

Official
by get-convex
schema.ts1.18 kB
import { defineSchema, defineTable } from "convex/server"; import { v } from "convex/values"; import { EMBEDDING_SIZE } from "../types"; export default defineSchema({ messages: defineTable({ channel: v.string(), timestamp: v.number(), body: v.string(), rand: v.number(), ballastArray: v.array(v.number()), }).index("by_channel_rand", ["channel", "rand"]), messages_with_search: defineTable({ channel: v.string(), timestamp: v.number(), body: v.string(), rand: v.number(), ballastArray: v.array(v.number()), }) .index("by_channel_rand", ["channel", "rand"]) .index("by_rand", ["rand"]) .searchIndex("search_body", { searchField: "body", filterFields: ["channel"], }), openclaurd: defineTable({ user: v.string(), timestamp: v.number(), text: v.string(), rand: v.number(), embedding: v.array(v.number()), }) .index("by_rand", ["rand"]) .vectorIndex("embedding", { vectorField: "embedding", dimensions: EMBEDDING_SIZE, filterFields: ["user"], }) .searchIndex("search_text", { searchField: "text", filterFields: ["user"], }), });

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/get-convex/convex-backend'

If you have feedback or need assistance with the MCP directory API, please join our Discord server