tigergraph-mcp

Model Context Protocol (MCP) server for TigerGraph — lets AI agents interact with TigerGraph through the MCP standard. All tools use pyTigerGraph's async APIs for optimal performance.

Table of Contents

• Requirements
• Installation
• Getting Started
• Usage
  • Running the MCP Server
  • Configuration
  • Multiple Connection Profiles
    • Helping the agent pick the right environment
  • Multi-user Deployments (HTTP/SSE)
  • Using with Existing Connection
• Client Examples
  • LangChain / LangGraph over stdio
  • LangChain / LangGraph over HTTP
  • MCP SDK over stdio
  • MCP SDK over HTTP
• Available Tools
• Loading from a Data Warehouse
• LLM-Friendly Features
  • Structured Responses
  • Rich Tool Descriptions
  • Token Optimization
  • Tool Discovery
• Notes

Requirements

• Python 3.10 through 3.14
• MCP SDK 1.x or 2.x — either generation of the mcp package works.
• TigerGraph 4.1 or later — Install from the TigerGraph Downloads page or use TigerGraph Savanna for a managed cloud instance.

Recommended: TigerGraph 4.2+ to enable TigerVector and advanced hybrid retrieval features.

Installation

Install with pip:

pip install tigergraph-mcp

Or with conda (from the tigergraph channel):

conda install -c tigergraph tigergraph-mcp

This installs:

• pyTigerGraph>=2.0.4 — the TigerGraph Python SDK
• mcp>=1.0.0 — the MCP SDK
• pydantic>=2.0.0 — for data validation
• click — for the CLI entry point
• python-dotenv>=1.0.0 — for loading .env files

To serve over HTTP (--transport streamable-http or sse), also install a web stack:

pip install uvicorn starlette

To enable the tigergraph__generate_gsql and tigergraph__generate_cypher tools (LLM-powered query generation), install the optional [llm] extras (pip only):

pip install "tigergraph-mcp[llm]"

Getting Started

TigerGraph-MCP supports multiple AI agent frameworks. Choose the one that fits your workflow:

LangGraph (Recommended)

LangGraph is ideal for building stateful, agent-based workflows with complex tool chaining. Setup guide and full chatbot example:

• LangGraph Setup
• Chatbot example code
• Example transcripts

CrewAI

CrewAI provides a simpler starting point for basic agentic workflows with a web-based UI:

• CrewAI Setup
• Chatbot example code

GitHub Copilot Chat (VS Code)

For quick tasks or straightforward tool invocations directly in your editor:

• Copilot Chat Setup

Usage

Running the MCP Server

stdio (default — single user, one IDE/agent)

tigergraph-mcp

The server talks MCP over its own stdin and stdout: it reads JSON-RPC messages from standard input and writes replies to standard output, then exits when standard input closes. Run it in a terminal and it simply waits for messages — there is no prompt and no human-facing console. You normally never start it this way; the MCP client (Claude Code, Cursor, GitHub Copilot Chat, a LangChain agent) spawns it as a subprocess and owns the pipes. Running it by hand is mainly useful for checking that it starts:

echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"manual","version":"1"}}}' \
  | tigergraph-mcp

Because the client owns the process, credentials must reach it as environment variables — from a .env file, or from the client's own env mapping. This is the right mode for any single-user IDE integration.

With a custom .env file:

tigergraph-mcp --env-file /path/to/.env

With verbose logging:

tigergraph-mcp -v    # INFO level
tigergraph-mcp -vv   # DEBUG level

Or programmatically:

from tigergraph_mcp import serve
import asyncio

asyncio.run(serve())

Streamable HTTP / SSE (multi-user, shared server)

tigergraph-mcp --transport streamable-http --host 0.0.0.0 --port 8000
# legacy SSE shape:
tigergraph-mcp --transport sse --host 0.0.0.0 --port 8000

Here the server binds the chosen port and serves MCP over HTTP, staying up until you stop it — a long-lived service you start once (under systemd, a container, or whatever supervises it), not a process a client spawns. Many clients connect to it concurrently, and it does not read standard input at all.

Each MCP session gets its own connection pool, so concurrent users never share state. Clients connect at http://<host>:<port>/mcp/ — note the trailing slash; /mcp returns a 307 redirect that some clients will not follow on POST. The mount path is configurable with --mount-path.

The server reads the same .env file and TG_* / <PROFILE>_TG_* variables as stdio, which supply each profile's topology and, optionally, its credentials. A client selects a profile with X-TG-Profile and may override any value with the other X-TG-* headers — see Server-side profiles and header overrides. With no headers at all, the default profile is used exactly as configured.

Authenticate the HTTP endpoint itself with a reverse proxy / API gateway when exposing it beyond localhost. The MCP server checks TigerGraph credentials, not who may reach the URL.

Configuration

The MCP server reads connection configuration from environment variables. You can set these either directly or in a .env file.

Using a .env File (Recommended)

Create a .env file in your project directory:

# .env — Username/Password authentication
TG_HOST=http://localhost
TG_GRAPHNAME=MyGraph  # Optional — can be omitted if the database has multiple graphs
TG_USERNAME=tigergraph
TG_PASSWORD=tigergraph
TG_RESTPP_PORT=9000
TG_GS_PORT=14240

Or use an API token instead of username/password:

# .env — API Token authentication
TG_HOST=http://localhost
TG_GRAPHNAME=MyGraph
TG_API_TOKEN=your_api_token_here

When TG_API_TOKEN (or TG_JWT_TOKEN) is set, the server uses token-based authentication (Authorization: Bearer <token>) and ignores username/password. You can obtain a token via pyTigerGraph's getToken() method or by directly calling TigerGraph's token generation endpoint.

When only username/password are provided and the TigerGraph instance requires a token for RESTPP endpoints, pyTigerGraph auto-mints one on the first 401 response and transparently retries the request — no manual token setup needed.

The server loads the .env file automatically. Environment variables take precedence over .env values.

Environment Variables

Variable	Default	Description
TG_HOST	http://127.0.0.1	TigerGraph host
TG_GRAPHNAME	(empty)	Graph name (optional)
TG_USERNAME	tigergraph	Username
TG_PASSWORD	tigergraph	Password
TG_SECRET	(empty)	GSQL secret (optional)
TG_API_TOKEN	(empty)	API token (optional)
TG_JWT_TOKEN	(empty)	JWT token (optional)
TG_RESTPP_PORT	9000	REST++ port
TG_GS_PORT	14240	GSQL port
TG_SSL_PORT	443	SSL port
TG_TGCLOUD	false	Whether using TigerGraph Cloud
TG_CERT_PATH	(empty)	Path to certificate (optional)

Multiple Connection Profiles

Define named profiles in your .env to work with multiple TigerGraph environments without changing any code.

Defining profiles

Each named profile uses a <PROFILE>_ prefix on the standard TG_* variables. Only variables that differ from the default need to be set.

# .env

# Default profile (no prefix) — password auth
TG_HOST=http://localhost
TG_USERNAME=tigergraph
TG_PASSWORD=tigergraph
TG_GRAPHNAME=MyGraph

# Staging profile — token auth
STAGING_TG_HOST=https://staging.example.com
STAGING_TG_API_TOKEN=staging_token_here
STAGING_TG_TGCLOUD=true

# Production profile — password auth
PROD_TG_HOST=https://prod.example.com
PROD_TG_USERNAME=admin
PROD_TG_PASSWORD=prod_secret
PROD_TG_GRAPHNAME=ProdGraph
PROD_TG_TGCLOUD=true

Profiles are discovered automatically at startup. Any variable matching <PROFILE>_TG_HOST registers a new profile. Values not set for a named profile fall back to the default profile's values.

Selecting the default profile

# Switch to staging for this run
TG_DEFAULT_PROFILE=staging tigergraph-mcp

# Or set permanently in .env
TG_DEFAULT_PROFILE=prod

TG_DEFAULT_PROFILE names the profile used when a call does not specify one. If it is not set, the unprefixed TG_* variables are the default profile. TG_PROFILE is accepted as an alias.

Omitting the profile argument and passing profile="default" mean the same thing — the default profile — in both stdio and HTTP mode.

Switching profiles per call

Every tool accepts an optional profile argument, so an agent can route individual calls to different environments without restarting the server. Connections are pooled per profile and reused across calls. list_connections reports the configured profiles, which one is the default, and which are currently connected — in HTTP mode scoped to the calling session.

User: Compare the vertex count of MyGraph between staging and prod.

Agent:
  → get_vertex_count(profile="staging", graph_name="MyGraph")
  → get_vertex_count(profile="prod",    graph_name="MyGraph")

User: Show me the schema on staging, then run this GSQL on prod:
      SHOW VERTEX Person

Agent:
  → get_graph_schema(profile="staging", graph_name="MyGraph")
  → gsql(profile="prod", command="SHOW VERTEX Person")

Helping the agent pick the right environment

Users normally name an environment the way it is configured — "staging", "the prod cluster". list_connections reports each profile's name and its host, so the agent can also resolve the occasional bare hostname or URL to the profile that reaches it:

{
  "default_profile": "dev",
  "profiles": [
    {"profile": "dev",     "host": "http://localhost",                "username": "tigergraph", "is_default": true,  "connected": true},
    {"profile": "prod",    "host": "https://mycompany.i.tgcloud.io",  "username": "analyst",    "is_default": false, "connected": false},
    {"profile": "staging", "host": "https://tg-staging.example.com",  "username": "analyst",    "is_default": false, "connected": false}
  ]
}

Note that prod's host carries no hint of the profile name, so a user who names that host cannot be served by guessing from profile names alone.

A system prompt that puts that to work:

You are a TigerGraph assistant. The tigergraph-mcp server may be configured
with several environments, each identified by a profile name.

Discovering profiles
- Call `list_connections` before your first data access, and again whenever
  the user mentions an environment you have not seen.
- Each profile reports its name, host, and username, which one is the
  default, and which are already connected.
- Never invent or hardcode a profile name.

Choosing one
- Users normally name an environment, not a machine. If the user names a
  profile ("use staging", "on the prod cluster"), use that profile.
- If the user names a host or URL instead, match it against the `host`
  field. Several profiles may share one host, differing only in the user
  they connect as. In that case run the request against every matching
  profile and report the results per profile, rather than asking which
  one was meant.
- If nothing matches what the user named, say so and list the configured
  profiles with their hosts. Do not guess.
- If the user says nothing about an environment, use the default profile
  and mention which one you used.

Using one
- Pass `profile="<name>"` on every tool call meant for that environment.
- A single turn may use different profiles when the user compares
  environments.

Reporting
- Answer about the environments the user asked about. Do not list the
  profiles you considered and skipped, and do not narrate the lookup.
- Name the environment alongside each answer, so the user knows which
  one it came from — especially when reporting more than one.

With that prompt, a site named in plain language resolves to a profile:

User: How many vertices does MyGraph have on staging?

Agent:
  → get_vertex_count(profile="staging", graph_name="MyGraph")
  "On staging: 1,204 vertices."

User: And on mycompany.i.tgcloud.io?          # a host, not an environment

Agent:                                        # tool calls, not shown to the user
  → list_connections()                        # prod and prod_ro share that host
  → get_vertex_count(profile="prod",    graph_name="MyGraph")
  → get_vertex_count(profile="prod_ro", graph_name="MyGraph")

Agent replies:
  "Two profiles reach that host:
     prod (as analyst):     1,204 vertices
     prod_ro (as readonly): 1,204 vertices"

The reply names the environment behind each number and says nothing about dev or staging, which the user did not ask about.

Omitting profile, or passing "default", uses the default profile — TG_DEFAULT_PROFILE if set (or its alias TG_PROFILE), otherwise the unprefixed TG_* variables.

Multi-user Deployments (HTTP/SSE)

Run one shared tigergraph-mcp HTTP server and let each logged-in user supply their own TigerGraph credentials. Sessions are isolated: every user's connection pool, profile state, and tool list lives in its own SessionConnectionManager.

Option 1 — Frontend backend owns the MCP client (recommended)

Your web backend authenticates the user, opens one MCP session per login carrying that user's TigerGraph credentials as X-TG-* headers, and routes all of their agent traffic through that session. The LLM never sees credentials.

[user logs in]         backend validates the user (any IdP, or passthrough
                       TigerGraph auth) and mints a short-lived TG token.
[per-user session]     backend opens one MCP session with that user's
                       X-TG-* headers; the server validates them once and
                       builds that session's connection.
[agent traffic]        every tool call reuses the same session, and so the
                       same pooled TigerGraph connection.
[user logs out]        backend closes its client; the session's pool is
                       reclaimed after TG_HTTP_SESSION_IDLE_TIMEOUT.

Hold the session open for the user's lifetime rather than calling get_tools() per request — see Client Examples for why.

A working reference lives in examples/multi_user_backend/: a FastAPI service that does this with subprocess-per-user over stdio. To use the shared HTTP server instead, point each user's MultiServerMCPClient at streamable_http with your server's URL and put that user's credentials in headers.

The authenticate tool remains available for re-pointing a live session mid-conversation: omit profile to replace the session's default connection, or name one to replace just that profile. Credentials are checked before the swap, so a bad one is reported at once and the existing connection keeps working. It is not needed when credentials arrive as headers.

Option 2 — Cursor / Copilot pointing at a remote MCP server

Each developer's mcp.json carries their own TigerGraph credentials as headers. The MCP server validates them against TigerGraph on every request and 401s on failure, so a bad credential surfaces as a connection error rather than a tool failure:

{
  "servers": {
    "tigergraph-mcp-server": {
      "type": "http",
      "url": "https://my-tg-mcp.internal/mcp",
      "headers": {
        "X-TG-Host": "https://acme.tgcloud.io",
        "X-TG-Api-Token": "${env:TG_API_TOKEN}"
      }
    }
  }
}

Server-side profiles and header overrides

The HTTP server reads the same .env file and TG_* / <PROFILE>_TG_* variables as stdio (--env-file, or a .env discovered from the working directory). Profiles define topology — host, ports, graph, TLS — and may optionally carry credentials. A request selects one with X-TG-Profile, and any X-TG-* header overrides that profile's value for that session only:

Headers sent	Connection used
X-TG-Profile only	that profile's topology and its configured credentials
X-TG-Profile + credential headers	that profile's topology, the caller's identity
Credential headers only	the default profile's topology, the caller's identity
No headers	the default profile exactly as configured

TG_DEFAULT_PROFILE (alias TG_PROFILE) names which profile acts as the default for requests that don't send X-TG-Profile; unset, the unprefixed TG_* variables are the default profile. Naming a profile the server does not define is an error rather than a silent fallback. TG_HTTP_ALLOWED_PROFILES=demo,staging narrows which profiles clients may name.

A named profile uses its own credentials where it defines them, and the unprefixed TG_* ones where it does not — the same inheritance stdio uses. If no credentials are configured anywhere, every caller must supply their own.

Whether profiles carry credentials at all is the operator's decision when writing the env file. Credentials in the env file are a shared identity usable by anyone who can reach the server, which suits a demo or single-user deployment; an env file with topology only forces every caller to identify itself, which is what you want for multi-user deployments.

Within a session, a tool call may name any configured profile with its profile argument. That connection is opened in that session's pool and is never shared with other sessions — the same shape as a stdio process holding several profiles. Omitting profile, or passing "default", resolves to the server's default profile, exactly as it does in stdio. A client normally sends no X-TG-Profile at all, which establishes the session on that default profile.

Failures are reported by kind, so a caller can tell a wrong address from a wrong login:

Status	Meaning	Examples
400	Topology — the request does not describe a reachable server	unknown profile, no host resolvable
401	Credentials — missing, or rejected by TigerGraph	no identity supplied, wrong password
502	Connection — the server could not be reached	unroutable host, wrong port, timeout

The reachability probe is bounded by TG_HTTP_VALIDATE_TIMEOUT (default 10 seconds) so an unroutable host fails promptly instead of hanging the request.

Idle sessions are reclaimed after TG_HTTP_SESSION_IDLE_TIMEOUT seconds (default 900; 0 keeps them for the life of the process), so pools do not accumulate when clients open many short-lived sessions. A reclaimed session is not broken — a later request rebuilds its pool.

Credentials are checked against TigerGraph when the session's connection is established, not on every request — a client's headers are fixed for the life of its session, so re-checking proves nothing new. A rejected credential never establishes a session. Requests that cannot be resolved at all (unknown profile, no host) are refused without contacting TigerGraph.

Access control to the endpoint itself remains the deployment's responsibility — put a reverse proxy or API gateway in front.

Recognised headers (they mirror the TG_* env vars used in stdio mode):

Header	Env-var equivalent
X-TG-Profile	selects a server-side profile (<PROFILE>_TG_*)
X-TG-Host	TG_HOST
X-TG-Graphname	TG_GRAPHNAME
X-TG-Username + X-TG-Password	TG_USERNAME + TG_PASSWORD
X-TG-Secret	TG_SECRET
X-TG-Api-Token	TG_API_TOKEN
X-TG-Jwt-Token	TG_JWT_TOKEN
X-TG-Restpp-Port, X-TG-Gs-Port, X-TG-Ssl-Port	TG_RESTPP_PORT, TG_GS_PORT, TG_SSL_PORT
X-TG-Tgcloud (true/false)	TG_TGCLOUD
X-TG-Cert-Path	TG_CERT_PATH

A host and an identity must be resolvable from the selected profile, the headers, or a combination of the two; every header is otherwise optional.

For per-call credential routing inside the agent's conversation, have the agent call authenticate once at session start:

User: Connect to my TigerGraph at https://acme.tgcloud.io with token eyJ...
Agent:
  → authenticate(host="https://acme.tgcloud.io", api_token="eyJ...")
  → get_graph_schema(graph_name="MyGraph")

Using with Existing Connection

from pyTigerGraph import AsyncTigerGraphConnection
from tigergraph_mcp import ConnectionManager

async with AsyncTigerGraphConnection(
    host="http://localhost",
    graphname="MyGraph",
    username="tigergraph",
    password="tigergraph",
) as conn:
    ConnectionManager.set_default_connection(conn)
    # ... run MCP tools ...
# HTTP connection pool is released on exit

Client Examples

Hold one session for the run. MultiServerMCPClient(...) connects nothing, and await client.get_tools() opens a session only to list the tools, then closes it — the returned tools carry a connection config, so each tool call opens a new session. Over stdio that spawns a tigergraph-mcp process per call; over HTTP it creates a session, a connection, and a credential check per call. Binding tools to a session held open by client.session(...) reuses one process (or one session and its pooled connection) for the whole run — in a measured 8-call agent run, 1 session instead of 9, and roughly 4× faster. Use get_tools() only for one-shot scripts.

LangChain / LangGraph over stdio

The client starts tigergraph-mcp as a subprocess and passes credentials as env vars.

import asyncio
from pathlib import Path

from dotenv import dotenv_values
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools

env_dict = dotenv_values(dotenv_path=Path(".env").expanduser().resolve())

client = MultiServerMCPClient(
    {
        "tigergraph-mcp-server": {
            "transport": "stdio",
            "command": "tigergraph-mcp",
            "args": ["-vv"],
            "env": env_dict,
        },
    }
)


async def main():
    async with client.session("tigergraph-mcp-server") as session:
        tools = await load_mcp_tools(session)
        # ... run your agent here; every tool call reuses this session
        print([t.name for t in tools])


asyncio.run(main())

Note: Instead of loading a .env file, you can pass credentials directly in the env mapping:

    "env": {
      "TG_HOST": "http://localhost",
      "TG_USERNAME": "tigergraph",
      "TG_PASSWORD": "tigergraph",
      "TG_GRAPHNAME": "MyGraph"
    }

Either way the credentials must be in env: the subprocess does not inherit your shell environment.

LangChain / LangGraph over HTTP

The server is already running elsewhere; the client only connects. Credentials travel as headers, so nothing about TigerGraph needs to be configured on this side.

import asyncio

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools

client = MultiServerMCPClient(
    {
        "tigergraph-mcp-server": {
            "transport": "streamable_http",
            "url": "http://localhost:8000/mcp/",   # trailing slash required
            "headers": {
                # Omit these entirely to use the server's default profile.
                "X-TG-Profile": "staging",
                "X-TG-Username": "my_user",
                "X-TG-Password": "my_password",
            },
        },
    }
)


async def main():
    async with client.session("tigergraph-mcp-server") as session:
        tools = await load_mcp_tools(session)
        print([t.name for t in tools])


asyncio.run(main())

MCP SDK over stdio

stdio_client does not pass your environment to the subprocess — it forwards only a minimal safe set (HOME, PATH, SHELL, …). Credentials must be supplied explicitly via env, or the server will fall back to its defaults and try http://127.0.0.1.

import asyncio
from pathlib import Path

from dotenv import dotenv_values
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import get_default_environment, stdio_client

env_dict = dotenv_values(dotenv_path=Path(".env").expanduser().resolve())


async def main():
    server_params = StdioServerParameters(
        command="tigergraph-mcp",
        args=["-vv"],
        env={**get_default_environment(), **env_dict},
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

            result = await session.call_tool("tigergraph__list_graphs", arguments={})
            for content in result.content:
                print(content.text)


asyncio.run(main())

MCP SDK over HTTP

import asyncio

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

URL = "http://localhost:8000/mcp/"          # trailing slash required
HEADERS = {                                  # omit to use the default profile
    "X-TG-Host": "http://my-tigergraph:14240",
    "X-TG-Username": "my_user",
    "X-TG-Password": "my_password",
}


async def main():
    async with streamablehttp_client(URL, headers=HEADERS) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

            # Every call reuses this session's pooled connection.
            result = await session.call_tool("tigergraph__list_graphs", arguments={})
            for content in result.content:
                print(content.text)

            # Route one call to another configured profile.
            result = await session.call_tool(
                "tigergraph__list_graphs", arguments={"profile": "prod"}
            )


asyncio.run(main())

Available Tools

Global Schema Operations

• tigergraph__get_global_schema — Get the complete global schema via GSQL LS

Graph Operations

• tigergraph__list_graphs — List all graph names in the database
• tigergraph__create_graph — Create a new graph with schema
• tigergraph__drop_graph — Drop a graph and its schema
• tigergraph__clear_graph_data — Clear all data from a graph (keeps schema)

Schema Operations

• tigergraph__get_graph_schema — Get schema as structured JSON
• tigergraph__show_graph_details — Show schema, queries, loading jobs, and data sources

Node Operations

• tigergraph__add_node / tigergraph__add_nodes
• tigergraph__get_node / tigergraph__get_nodes
• tigergraph__delete_node / tigergraph__delete_nodes
• tigergraph__has_node
• tigergraph__get_node_edges

Edge Operations

• tigergraph__add_edge / tigergraph__add_edges
• tigergraph__get_edge / tigergraph__get_edges
• tigergraph__delete_edge / tigergraph__delete_edges
• tigergraph__has_edge

Query Operations

• tigergraph__run_query — Run an interpreted query
• tigergraph__run_installed_query — Run an installed query
• tigergraph__install_query / tigergraph__drop_query
• tigergraph__show_query / tigergraph__get_query_metadata / tigergraph__is_query_installed
• tigergraph__update_query_description / tigergraph__get_query_description — Set or read query and per-parameter descriptions (TigerGraph 4.0+)
• tigergraph__get_neighbors

Loading Job Operations

• tigergraph__create_loading_job — from files, or from a data_source + query pair to load the result of a SQL query against a warehouse
• tigergraph__run_loading_job_with_file / tigergraph__run_loading_job_with_data
• tigergraph__get_loading_jobs / tigergraph__get_loading_job_status
• tigergraph__drop_loading_job

Statistics Operations

• tigergraph__get_vertex_count / tigergraph__get_edge_count
• tigergraph__get_node_degree

GSQL Operations

• tigergraph__gsql — Execute raw GSQL
• tigergraph__generate_gsql — Generate GSQL from natural language (requires [llm])
• tigergraph__generate_cypher — Generate openCypher from natural language (requires [llm])

Vector Schema Operations

• tigergraph__add_vector_attribute / tigergraph__drop_vector_attribute
• tigergraph__list_vector_attributes / tigergraph__get_vector_index_status

Vector Data Operations

• tigergraph__upsert_vectors
• tigergraph__load_vectors_from_csv / tigergraph__load_vectors_from_json
• tigergraph__search_top_k_similarity / tigergraph__fetch_vector

Data Source Operations

• tigergraph__create_data_source / tigergraph__update_data_source
• tigergraph__get_data_source / tigergraph__drop_data_source
• tigergraph__get_all_data_sources / tigergraph__drop_all_data_sources
• tigergraph__get_data_source_types — List supported types and their configuration keys
• tigergraph__preview_sample_data

Supported data source types:

Family	Types
Object storage	s3, gcs, abs (alias: azure_blob)
Data warehouse	snowflake, bigquery, postgresql
Lakehouse	iceberg
Streaming	kafka, kafka_v2, mirrormaker

Each type takes different configuration keys. Call tigergraph__get_data_source_types for the required keys and a worked example, or see Loading from a data warehouse.

Credentials in config are sent to TigerGraph but masked in tool responses, so they do not appear in a conversation transcript.

Connection / Session

• tigergraph__list_connections / tigergraph__show_connection — Inspect configured profiles
• tigergraph__authenticate — Register per-session TigerGraph credentials (HTTP/SSE mode)

Discovery & Navigation

• tigergraph__discover_tools — Search for tools by description or keywords
• tigergraph__get_workflow — Get step-by-step workflow templates
• tigergraph__get_tool_info — Get detailed information about a specific tool

LLM-Friendly Features

Structured Responses

Every tool returns a consistent JSON structure:

{
  "success": true,
  "operation": "get_node",
  "summary": "Found vertex 'p123' of type 'Person'",
  "data": { ... },
  "suggestions": ["View connected edges: get_node_edges(...)"],
  "metadata": { "graph_name": "MyGraph" }
}

Error responses include actionable recovery hints:

{
  "success": false,
  "operation": "get_node",
  "error": "Vertex not found",
  "suggestions": ["Verify the vertex_id is correct"]
}

Rich Tool Descriptions

Each tool includes detailed descriptions with use cases, common workflows, tips, warnings, and related tools.

Token Optimization

Responses are designed for efficient LLM token usage — no echoing of input parameters, only new information (results, counts, boolean answers).

Tool Discovery

# Find the right tool
result = await session.call_tool("tigergraph__discover_tools",
    arguments={"query": "how to add data to the graph"})

# Get a workflow template
result = await session.call_tool("tigergraph__get_workflow",
    arguments={"workflow_type": "data_loading"})

# Get detailed tool info
result = await session.call_tool("tigergraph__get_tool_info",
    arguments={"tool_name": "tigergraph__add_node"})

Notes

• Transport: stdio by default
• Error Detection: GSQL operations include error detection for syntax and semantic errors
• Connection Management: Connections are pooled by profile and reused across requests; pool is released at server shutdown
• Performance: Persistent HTTP connection pool per profile; async non-blocking I/O; v.outdegree() for O(1) degree counting; batch operations for multiple vertices/edges