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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__load_vectors_from_json

Bulk-load vector embeddings from a JSON Lines file into a TigerGraph vertex attribute by creating and running a GSQL loading job.

Instructions

Bulk-load vectors from a JSON Lines (.jsonl) file into a vertex type's vector attribute. Creates a GSQL loading job with JSON_FILE="true", runs it with the file, then drops the job.

File format: Each line is a JSON object with an ID field and a vector field. The vector is stored as a comma-separated string (not a JSON array).

Example file (id_key='id', vector_key='embedding'):

{"id": "vertex1", "embedding": "0.1,0.2,0.3"}
{"id": "vertex2", "embedding": "0.4,0.5,0.6"}

Prerequisites:

  1. Vertex type must already exist

  2. Vector attribute must already be added (use 'add_vector_attribute')

  3. File must exist on the local machine (it is uploaded to TigerGraph via REST)

Related Tools: add_vector_attribute, load_vectors_from_csv (CSV alternative), upsert_vectors (REST API for in-memory data), get_vector_index_status (check indexing after load)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
id_keyNoJSON key for the vertex ID. Default: 'id'.id
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
file_pathYesAbsolute path to the JSON Lines (.jsonl) file on the local machine (uploaded to TigerGraph via REST). Each line is a JSON object with an ID field and a vector field.
graph_nameNoName of the graph. If not provided, uses default connection.
vector_keyNoJSON key for the vector data (stored as a comma-separated string). Default: 'vector'.vector
vertex_typeYesTarget vertex type that has the vector attribute.
vector_attributeYesName of the vector attribute to load into.
element_separatorNoSeparator between vector elements within the vector string value. Default: ','.,

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.2

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description transparently states the side effects: it creates a GSQL loading job, runs it, and drops the job. It also notes the file is uploaded via REST, which is important behavioral context. No annotations are present, so the description carries this responsibility well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with concise sections: overview, file format, example, prerequisites, and related tools. It is informative without being verbose or redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the operation's complexity, the description covers the process, prerequisites, and related tools well. It does not describe the return value, but since no output schema is provided, the missing detail is not critical. The existing information is sufficient for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 8 parameters are described in the schema with clear semantics, defaults, and examples. The description additionally clarifies the file format and vector representation (comma-separated string, not array), which complements the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: bulk-load vectors from a JSON Lines file into a vertex type's vector attribute. It specifies the action, resource, and mechanism (GSQL loading job with JSON_FILE='true').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains prerequisites and lists related tools such as load_vectors_from_csv as a CSV alternative and upsert_vectors for in-memory data, providing useful context for when to choose this tool. It could be more explicit about 'use this when' but is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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