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sourav-spd

AWS S3 Connector MCP Server

by sourav-spd

Query S3 Tables Database

query_database

Run read-only SQL queries on AWS S3 Tables via Iceberg catalog. Use this tool to explore and analyze data in S3 Tables without making changes.

Instructions

Execute SQL queries against S3 Tables using PyIceberg/Daft engine. This tool provides a secure interface to run read-only SQL queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYesREST URI for Iceberg catalog
queryNoOptional SQL query. If not provided, will execute SELECT * FROM table.
regionYesAWS region for S3Tables/Iceberg REST endpoint
namespaceYesThe namespace. Must be 1-255 characters long.
warehouseYesWarehouse string for Iceberg catalog
catalog_nameNoCatalog name
rest_signing_nameNoREST signing name
rest_sigv4_enabledNoEnable SigV4 signing
Behavior3/5

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

The description discloses that queries are read-only and secure, and mentions a default behavior (SELECT * FROM table when no query given). With no annotations provided, the description carries the burden, but it lacks details on error handling, performance, or parameter effects.

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 two sentences, front-loading the core purpose and then adding the read-only and secure aspect. Every word is meaningful and no redundancy.

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 tool's complexity (8 parameters, no output schema), the description is too brief. It omits any mention of return format, limits, or parameter relationships, which is needed 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 input schema has 100% description coverage for all parameters, so the description does not need to add much. It does not provide extra context beyond the schema, which meets the baseline but does not excel.

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 identifies the verb (Execute SQL queries), resource (S3 Tables), and scope (read-only). This differentiates it from sibling write tools like create_table or import_csv_to_table.

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

Usage Guidelines3/5

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

The description states it is 'read-only SQL queries', implying it should be used for querying rather than modifying data. However, it does not explicitly mention when not to use it or suggest alternatives among the many sibling tools.

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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