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AiGanak

aiganak-aiops-mcp

by AiGanak

inspect_code_ast

Parse a Python source file into an abstract syntax tree and extract class, function, and import structures to enable diagnostic code analysis and root-cause identification.

Instructions

Parses a target Python source file into an AST tree to return class, function, and import structures for diagnostic analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesPath to Python source file.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the tool parses and returns structures but doesn't disclose whether this is read-only, what happens with invalid syntax, how large files are handled, or any failure modes. For a read-only analysis tool, the lack of safety-risk concern is notable, but error behavior is undisclosed.

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 focused sentence that names the action, target, and output scope with zero wasted words. It's front-loaded with the key verb 'Parses' and efficiently conveys the purpose.

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?

With a single self-explanatory parameter, an output schema mentioned as present, and a simple one-file read operation, the description adequately covers the essentials. It could mention error-handling behavior or format limits, but given the tool's simplicity and presence of an output schema, this is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is only one parameter (file_path) with 100% schema description coverage, so the schema fully documents it. The description adds no further param details, but with a single self-explanatory parameter and baseline 4 for 0-param-equivalent simplicity, the minimal addition is acceptable.

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 action (parses as AST) on a specific resource (Python source file) with clear output intent (class/function/import structures). However, it doesn't explicitly distinguish from siblings like query_log_file or apply_code_patch, though the domain is so different that confusion is unlikely.

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 context (diagnostic analysis of Python source) but provides no explicit when-to-use guidance or exclusions. It's clear this is for inspecting code structure rather than querying logs or applying patches, but no alternatives are explicitly named.

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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