Cassandra MCP

by Sandeep-BA

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Python

Local

describe_table

Read-onlyIdempotent

Read column types, partition keys, and clustering keys for an authorized Cassandra table to understand its schema.

Instructions

Read column types and partition/clustering keys for an authorized table.

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": {
      "keyspace": "<keyspace>",
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

Name	Required	Description	Default
`table`	Yes
`keyspace`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 17, 2026

TDQS

B3.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds the 'authorized' qualifier, signaling a permission requirement, but does not say what happens on an unauthorized or nonexistent table, nor describe the return shape.

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?

A single tight sentence with zero filler, front-loading the verb and the specific metadata returned. Every clause earns its place.

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?

For a two-parameter read-only introspection tool with no output schema, the description adequately names what is returned (types, partition/clustering keys), which partly substitutes for an output schema. However, with no output schema and 0% parameter coverage, it leaves the keyspace argument and error/permission behavior unexplained.

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 carries the full burden for the two required parameters. It mentions 'table' only implicitly by naming the resource and says nothing about 'keyspace' or the identifier format/pattern required by the schema, leaving half the parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Read') and resource ('table'), and specifies exactly what is retrieved: column types and partition/clustering keys. This is clearly distinguishable in substance from list_tables and read_partition, but the description never names those siblings to make the routing explicit.

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 only contextual hint is 'for an authorized table,' which implies a permission precondition but gives no when-to-use guidance and no contrast with list_tables or read_partition. An agent must infer whether to call this before reading data or partitions.

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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