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sap_stream_table_data

Retrieve large SAP table data in manageable chunks using offset-based pagination. Increment the offset to page through millions of rows efficiently.

Instructions

Streams large table data using offset-based pagination. Call repeatedly with increasing offset to retrieve all data. Ideal for tables with millions of rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNoStarting row offset
table_nameYesTable to stream
package_sizeNoRows per chunk (recommended max: 10000)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.2

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden. It implies read-only behavior and notes the large-data context, but does not explicitly state that it avoids modifications, nor does it disclose potential performance impacts or rate limits. It is not misleading, but lacks depth.

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 with no filler. It front-loads the primary purpose and immediately provides usage guidance, keeping it efficient and focused.

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

Completeness4/5

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

Given the tool's simplicity and absence of an output schema, the description conveys the essential context: it is a streaming/pagination mechanism for large tables. It does not mention error conditions or consistency concerns, but these are not critical for this straightforward operation.

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 schema already fully describes all three parameters with clear descriptions. The tool description adds no extra parameter-level insight or examples beyond what is in the schema, so it meets the baseline exactly.

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 action ('Streams'), the resource ('large table data'), and the method ('offset-based pagination'). It differentiates itself from siblings like sap_read_table_data and sap_read_table_paginated by emphasizing suitability for very large tables.

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

Usage Guidelines4/5

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

It explicitly suggests when to use the tool ('Ideal for tables with millions of rows') and instructs how to use it ('Call repeatedly with increasing offset'). However, it does not compare against specific alternatives or state when NOT to use it.

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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