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sap_infer_joins

Detect join keys between two SAP tables by comparing field domains, returning suggested keys with confidence levels.

Instructions

Automatically detect join keys between two tables based on domain matching. Compares field domains to suggest join keys with confidence levels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table1YesFirst table name
table2YesSecond table name

Schema Changelog

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

  1. Addedv0.1.2

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It clearly discloses the core behavior: comparing field domains and suggesting join keys with confidence levels. It does not explicitly state whether this is a read-only analysis or what side effects, if any, may occur, nor does it describe the output format beyond 'confidence levels'. This is adequate but incomplete.

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 two sentences with no fluff, and the primary purpose is front-loaded. There is slight redundancy between 'based on domain matching' and 'compares field domains', but overall it is concise and scannable.

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 tool with no output schema, the description should explain what the caller receives beyond 'join keys with confidence levels'. It does not state whether multiple candidate keys are returned, how confidence is represented, or whether the tool reads table metadata vs. actual data. Given the tool's moderate complexity and missing output schema, this is a noticeable gap, but the basic invocation parameters are clear.

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?

Schema coverage is 100% and both parameters have descriptions and examples ('EKKO', 'EKPO'). The description adds no parameter-specific meaning beyond the schema, so the baseline of 3 applies. It does not clarify naming conventions or accepted formats, but the schema already handles the basics.

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 states a specific verb ('detect') and resource ('join keys between two tables'), and clarifies the method ('based on domain matching'), which distinguishes it from siblings like sap_read_foreign_keys that read actual foreign keys. However, it does not explicitly name or differentiate itself from any sibling tool, so it stops short of a 5.

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 implies the tool should be used when join keys between two tables need to be inferred, but it provides no explicit when-to-use, when-not-to-use, or alternative tool guidance. Siblings like sap_read_foreign_keys or sap_analyze_query could serve related purposes, and no routing information is given.

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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