INDIVIDUAL MCP TOOL

extract_entities

Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.

agishub.comnone authenticationAvailability not checked

LIVE ENDPOINT

https://api.agishub.com/mcp/ai

No auth detected

Connect to this endpoint to inspect the live schema for extract_entities and invoke it with your own arguments.

{}

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.ai-toolkit]
url = "https://api.agishub.com/mcp/ai"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "ai-toolkit": {
      "type": "http",
      "url": "https://api.agishub.com/mcp/ai"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: ai-toolkit
Remote MCP URL: https://api.agishub.com/mcp/ai

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "ai-toolkit": {
      "url": "https://api.agishub.com/mcp/ai"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "ai-toolkit": {
      "type": "http",
      "url": "https://api.agishub.com/mcp/ai"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "ai-toolkit",
  "transport": "streamable-http",
  "url": "https://api.agishub.com/mcp/ai"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

summarize — Summarize a block of text into a short abstract, with an optional target length.
classify — Classify a text into exactly one of the candidate labels you provide (e.
transcribe — Transcribe an audio file (given by public URL) to text using Whisper.
embed — Turn text into a numeric embedding vector for semantic search, RAG and similarity.
chat — Ask a general-purpose LLM a question or give it an instruction, with an optional system prompt.