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tigergraph__load_vectors_from_csv

Bulk-load vector data from a CSV file into a vertex type's vector attribute by creating and running a GSQL loading job.

Instructions

Bulk-load vectors from a CSV/delimited file into a vertex type's vector attribute. Creates a GSQL loading job, runs it with the file, then drops the job.

File format: Each row has a vertex ID and a vector. Fields are separated by field_separator (default |). Vector elements are separated by element_separator (default ,).

Example file (field_separator='|', element_separator=','):

vertex1|0.1,0.2,0.3
vertex2|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_json (JSON Lines alternative), upsert_vectors (REST API for in-memory data), get_vector_index_status (check indexing after load)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headerNoWhether the file has a header row. Default: false.
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
file_pathYesAbsolute path to the CSV/delimited data file on the local machine (uploaded to TigerGraph via REST). Each row has a vertex ID and a vector column.
id_columnNoColumn for vertex ID: integer index (0-based) or header name. Default: 0 (first column).
graph_nameNoName of the graph. If not provided, uses default connection.
vertex_typeYesTarget vertex type that has the vector attribute.
vector_columnNoColumn containing the vector data: integer index (0-based) or header name. Default: 1 (second column).
field_separatorNoSeparator between fields (columns) in the file. Default: '|'.|
vector_attributeYesName of the vector attribute to load into.
element_separatorNoSeparator between vector elements within the vector column. Default: ','.,

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.6/5.0
Behavior4/5

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

The description reveals key behavioral details beyond the annotations: it creates and then drops a GSQL loading job, uploads the local file via REST, and requires the vertex type and vector attribute to pre-exist. Annotations only indicate non-read-only/non-destructive behavior, so this additional context meaningfully helps the agent anticipate side effects.

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

Conciseness4/5

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

The description is well-organized with clear sections for purpose, file format, example, prerequisites, and related tools. It is somewhat long but each section serves a purpose for a 10-parameter tool, and the critical details are front-loaded in the first sentence.

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

Completeness5/5

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

Given the tool's complexity, the description provides all necessary context: prerequisites, file format with an example, the upload mechanism, and related follow-up tools. There is no output schema, but the description covers the operational behavior well enough 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.

Parameters4/5

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

The input schema already documents all parameters with 100% coverage, establishing a baseline of 3. The description adds value by giving a concrete file-format example, explaining the default separators, and clarifying that each row contains a vertex ID plus a vector column. This goes beyond the schema's field-level 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 states a specific verb and resource: 'Bulk-load vectors from a CSV/delimited file into a vertex type's vector attribute.' It also clearly distinguishes this from related tools by naming load_vectors_from_json and upsert_vectors as alternatives. The scope is unambiguous and the lifecycle (create, run, drop loading job) is explicit.

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

Usage Guidelines5/5

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

The description explicitly lists prerequisites and related tools, telling agents to use add_vector_attribute first, load_vectors_from_json for JSON Lines input, upsert_vectors for in-memory data, and get_vector_index_status after loading. This gives clear when-to-use and when-not-to-use guidance without relying on inference.

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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