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Kinetica MCP Server

Kinetica MCP Server

Official
by kineticadb
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

describe_table

Retrieve a mapping of column names to their data types for any specified table in the Kinetica database.

Instructions

Return a dictionary of column name to column type.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_name": "<table_name>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description must disclose all behavioral traits. It only states the return type, omitting side effects (likely none), read-only nature, or error behavior. This is insufficient for safe agent invocation.

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 a single, clear sentence that efficiently conveys core functionality. While brief, it avoids unnecessary words, though it could incorporate more detail without becoming verbose.

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

Completeness2/5

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

Given the existence of an output schema, the description partly covers return values. However, it lacks critical context about parameter semantics and behavioral constraints, making it inadequate for a tool that likely interacts with an external database.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must clarify the single parameter table_name. It does not specify expected format (e.g., schema-qualified, case sensitivity) or provide any additional context beyond the parameter name.

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 returns a dictionary of column name to column type, which precisely conveys its purpose. It naturally distinguishes from sibling tools like get_records (data retrieval) and list_tables (listing table names) by focusing on schema introspection.

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 provides no guidance on when to use this tool versus alternatives such as get_records or query_sql. It does not mention prerequisites (e.g., table must exist) or conditions that affect its use.

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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