py-ast-mcp

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get_function_body

Extract the full source of any Python function or method with line numbers, using a file path and function or dotted method name.

Instructions

Extract the full source of a function or method, with line numbers.

Args: path: Path to a .py file. name: Function name, Class.method, or a dotted nested path.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>",
      "name": "<name>"
    },
    "name": "get_function_body"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes
`path`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Aug 2, 2026

TDQS

A3.8/5.0

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 discloses that the result includes line numbers, but is silent on error handling, behavior when the function is not found, or whether the full source includes the function signature. It is minimal but not misleading.

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-loaded with the purpose, and includes just enough parameter detail without superfluous text. Every sentence earns its place.

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, the description covers purpose and parameters adequately. The output schema exists, so return values need not be explained in depth. However, it lacks explicit differentiation from the many sibling tools, which would improve completeness.

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?

The Args section adds meaningful detail beyond the bare schema: path is a .py file, and name can be a function name, Class.method, or dotted nested path. This compensates for the 0% schema description coverage, though it could clarify dotted path resolution further.

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 tool extracts the full source of a function or method with line numbers. This specific verb-resource pairing distinguishes it from siblings like list_declarations (which just lists) and get_doc (which returns documentation).

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or contrast with sibling tools that might also inspect functions. The description only states the core action.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.