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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__run_loading_job_with_file

Upload a data file to TigerGraph and run a loading job to ingest it using the specified job definition and file tag.

Instructions

Execute a loading job with a data file. The file is uploaded to TigerGraph and loaded according to the specified loading job definition.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eolNoEnd-of-line character. Default is '\n'. Supports '\r\n'.
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
timeoutNoTimeout in milliseconds. Set to 0 for system-wide timeout.
file_tagYesThe name of file variable in the loading job (DEFINE FILENAME <fileTag>).
job_nameYesThe name of the loading job to run.
file_pathYesAbsolute path to the data file to load. Example: '/home/user/data/persons.csv'
separatorNoData value separator. Default is comma. For JSON data, don't specify.
graph_nameNoName of the graph. If not provided, uses default connection.
size_limitNoMaximum size for input file in bytes (default 128MB).

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing side effects. It states that the file is uploaded and loaded, implying data modification to the graph, which is a key side effect. However, it does not mention potential impacts such as overwriting existing data or failures, so transparency is partial.

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 concise, consisting of two sentences that directly convey the tool's operation without unnecessary fluff. It is well-structured and easy to parse, fitting the expected format.

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 detailed schema, the description provides sufficient context about the tool's purpose and the act of uploading and loading. It does not mention return values or error handling, but these are not critical for invoking the tool. Overall, it is complete enough for effective use.

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

Parameters3/5

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

The schema provides 100% coverage of parameter descriptions, and the tool description adds no additional meaning beyond what is already in the schema. Since the schema is comprehensive, the baseline of 3 is appropriate; the description does not enrich parameter understanding.

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 function: executing a loading job with a data file and uploading it to TigerGraph. It distinguishes itself from the sibling 'run_loading_job_with_data' by explicitly mentioning 'file' versus data, making its purpose unambiguous.

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

Usage Guidelines2/5

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

The description does not provide guidance on when to use this tool versus alternatives like 'run_loading_job_with_data' or other loading job tools. It explains the mechanism but lacks explicit usage criteria or comparisons, leaving the agent to infer the appropriate context.

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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