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AWS MCP Server

AWS MCP Server

by marcelobrake
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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aws_glue_get_tables

Retrieve tables from an AWS Glue Data Catalog database to manage data assets. Use filters to find specific tables by name and control result volume.

Instructions

List tables in a Glue Data Catalog database.

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": {
      "database_name": "<database_name>"
    },
    "name": "aws_glue_get_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault
profileNoAWS profile name from ~/.aws/config (e.g., 'default', 'production')
regionNoAWS region override (e.g., 'us-east-1', 'sa-east-1')
database_nameYesGlue database name
expressionNoRegular expression filter on table names
max_resultsNoMaximum tables to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden but fails to disclose behavioral traits: it does not confirm the read-only nature (though implied by 'List'), describe the return structure or metadata fields, mention pagination behavior, or note AWS API rate limiting considerations.

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 a single 7-word sentence that is front-loaded with the action. There is no redundant or wasteful content; every word serves the purpose of identifying the operation.

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 the 5 parameters including regex filtering and pagination controls, plus AWS service complexity with no output schema, the description is minimal but functional. It identifies the core operation but omits mention of the regex filtering capability and what table metadata is returned.

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?

With 100% schema description coverage (all 5 parameters documented), the baseline is 3. The description mentions 'database' which aligns with the database_name parameter, but adds no additional semantic context for expression (regex filtering) or max_results pagination that isn't already in the schema.

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?

The description 'List tables in a Glue Data Catalog database' provides a clear verb (List), resource (tables), and scope (Glue Data Catalog). It distinguishes from siblings like aws_glue_get_databases (which returns databases, not tables) and aws_athena_list_table_metadata (by specifying Glue). However, it lacks specificity on scope (e.g., whether filtering is supported) which is present in the parameters.

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 offers no guidance on when to use this tool versus alternatives (e.g., aws_athena_list_table_metadata for Athena queryable tables), nor does it mention prerequisites like AWS authentication or required Glue permissions. It assumes the user knows the 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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