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tigergraph-mcp

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by tigergraph

tigergraph__load_vectors_from_csv

Bulk-load vectors from a CSV/delimited file into a vertex type's vector attribute by creating, running, and dropping 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. Dates show when Glama detected each change.

  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 transparently explains that the tool creates a GSQL loading job, runs it, then drops the job, and that the file is uploaded via REST. It also lists prerequisites, giving a clear picture of side effects and dependencies. It does not mention whether existing vector values are overwritten or how errors are reported, but the main behavioral flow is well disclosed.

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-organized with sections for file format, example, prerequisites, and related tools. It is detailed but each section serves a purpose, and the example directly clarifies the separator semantics. No redundant or filler content is present.

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 (10 parameters, file upload, loading job lifecycle, and relation to other vector tools), the description covers all necessary context: prerequisites, file structure, defaults, alternative tools, and post-load indexing. It is sufficiently complete for an agent to use correctly without external documentation.

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?

Every parameter in the schema has a meaningful description, including defaults and accepted types. The description also provides an example file and explains column defaults, separators, and profile/graph override behavior, making parameter usage highly clear.

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-loading vectors from a CSV/delimited file into a specific vertex type's vector attribute. It also distinguishes itself from related tools like load_vectors_from_json and upsert_vectors, making its role unambiguous.

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 provides prerequisites, file format requirements, and related tool alternatives, which effectively guide when to use this tool. It does not explicitly state 'use this when...' or 'use this instead of...' in a separate instruction block, but the related tools and prerequisites make the intended usage clear.

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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