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Glama

Ultra MCP

index-vectors

Index project files for semantic search by generating vector embeddings, supporting providers like OpenAI, Azure, and Gemini, with options to specify paths and force re-indexing.

Instructions

Index project files for semantic search using vector embeddings

Input Schema

NameRequiredDescriptionDefault
forceNoForce re-indexing of all files
pathNoProject path to index (defaults to current directory)
providerNoEmbedding provider to use (defaults to configured provider)

Input Schema (JSON Schema)

{ "$schema": "http://json-schema.org/draft-07/schema#", "additionalProperties": false, "properties": { "force": { "description": "Force re-indexing of all files", "type": "boolean" }, "path": { "description": "Project path to index (defaults to current directory)", "type": "string" }, "provider": { "description": "Embedding provider to use (defaults to configured provider)", "enum": [ "openai", "azure", "gemini" ], "type": "string" } }, "type": "object" }

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/RealMikeChong/ultra-mcp'

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