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IBM watsonx.data MCP Server

Official
by IBM
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

create_ingestion_job

Creates a data ingestion job to load data from cloud storage into watsonx.data tables, supporting multiple file formats and write modes.

Instructions

Create a data ingestion job to load data into watsonx.data.

Args:
    job_id: Unique job identifier (e.g., "ingestion-1234567890")
    catalog: Target catalog name
    schema: Target schema name
    table: Target table name
    file_paths: Source file path (e.g., "s3://bucket-name/file.csv")
    file_type: Source file type - "csv", "parquet", "json", "orc", "avro" (default: "csv")
    bucket_name: S3 bucket name (extracted from file_paths if not provided)
    bucket_type: Bucket type - "amazon_s3", "aws_s3", "minio", "ibm_cos", "ibm_ceph",
                 "adls_gen1", "adls_gen2", "google_cs", "ibm_storage_scale", "ozone" (default: "ibm_cos")
    write_mode: Write mode - "append", "overwrite" (default: "append")
    engine_id: Spark engine ID to use for ingestion
    field_delimiter: CSV field delimiter (default: ",")
    line_delimiter: CSV line delimiter (default: "

") escape_character: CSV escape character (default: "") header: Whether CSV has header row (default: true) encoding: File encoding (default: "UTF-8") driver_memory: Spark driver memory (default: "2G") driver_cores: Spark driver cores (default: 1) executor_memory: Spark executor memory (default: "2G") executor_cores: Spark executor cores (default: 1) num_executors: Number of Spark executors (default: 1)

Returns:
    Dict with job_id, status, and creation details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
catalogYes
schemaYes
tableYes
file_pathsYes
file_typeNocsv
bucket_nameNo
bucket_typeNoibm_cos
write_modeNoappend
engine_idNo
field_delimiterNo,
line_delimiterNo
escape_characterNo\
headerNo
encodingNoUTF-8
driver_memoryNo2G
driver_coresNo
executor_memoryNo2G
executor_coresNo
num_executorsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must cover behavioral aspects. However, it only lists parameters and a brief return type. It does not disclose important behaviors like whether the job starts immediately, is idempotent, requires permissions, or has 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 front-loaded with a purpose statement and then an organized Args block. While it is lengthy due to 20 parameters, it is well-structured and all information is relevant. Minor deduction for verbosity.

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

Completeness3/5

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

Given 20 parameters and no annotations, the description covers parameter details and return type. However, it lacks explanation of the overall process, error conditions, or output schema details. It is moderately complete.

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?

Schema description coverage is 0%, so the description fully documents parameters. Each parameter includes explanation, default value, and format examples (e.g., 'e.g., s3://bucket-name/file.csv'). This significantly adds meaning beyond the raw schema.

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 'Create a data ingestion job to load data into watsonx.data.' It uses a specific verb (create) and resource (data ingestion job) with context (watsonx.data). This clearly distinguishes from sibling tools like cancel_ingestion_job or get_ingestion_job.

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 any guidance on when to use this tool versus alternatives, such as other data loading or spark submission tools. It lacks context about prerequisites, ideal scenarios, or exclusions.

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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