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analyze_ssis_package

Analyze SSIS packages to generate detailed reports on complexity, effort estimates, component inventory, and migration gaps for Azure Data Factory conversion.

Instructions

Analyze a single SSIS package (.dtsx file) and return a detailed report including: complexity score (0-100), effort estimate (Low/Medium/High/Very High), component inventory, gap analysis (items needing manual work), cross-database/linked server references, CDM pattern detection, ESI reuse candidates, and dependency execution order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
package_pathYesAbsolute path to the .dtsx file.
esi_tables_pathNoOptional path to a JSON file mapping source_system → table list for ESI reuse detection. Format: {"PHINEOS": ["TocPartyAddress", "TLBenefit"]}.
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It describes the output content but lacks behavioral details such as whether it's a read-only analysis, if it modifies the package, performance characteristics, error handling, or authentication needs. This is inadequate for a tool with no annotation coverage.

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, dense sentence that efficiently lists all key output components without waste. It is front-loaded with the core action and resource, making it easy to scan and understand.

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 no annotations and no output schema, the description partially compensates by detailing the report contents. However, it lacks information on behavioral traits, error cases, or output structure, leaving gaps for a tool with two parameters and no structured output documentation.

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 description coverage is 100%, so the schema already documents both parameters fully. The description does not add any meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate when schema does the heavy lifting.

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 verb ('analyze') and resource ('a single SSIS package (.dtsx file)'), and distinguishes it from siblings by specifying it analyzes a single package rather than scanning multiple packages (scan_ssis_packages) or converting/deploying (convert_ssis_package, deploy_to_adf).

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 implies usage by listing what the tool returns, but does not explicitly state when to use it versus alternatives like scan_ssis_packages (for multiple packages) or validate_adf_artifacts (for ADF validation). No exclusions or prerequisites are mentioned.

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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