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AxonityAI

Axonity Flow MCP Server

Official
by AxonityAI

format_tool_code

Formats Python source code using Black and returns the formatted output. Does not save input, safe for read-only tokens.

Instructions

Format Python tool code with Black and return the formatted source. Stateless and safe for read-only service tokens — it does not save anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe Python source to format.
Behavior4/5

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

The description discloses that the tool is stateless and safe for read-only service tokens ('does not save anything'). Since no annotations are provided, the description bears full responsibility for behavioral transparency. It does not detail error behavior or formatting specifics (e.g., Black version), but the primary side-effect safety is well communicated.

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?

Two sentences containing no extraneous words. The first sentence states the action and tool, the second provides safety and statelessness context. All information is front-loaded and concise.

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 (one parameter, no output schema), the description covers the core purpose and safety aspect. It does not mention the return format explicitly (though 'return the formatted source' implies a string), nor error scenarios. However, for a formatting tool, this level of detail is adequate and largely complete.

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?

With 100% schema description coverage for the single 'code' parameter, the description adds little beyond the schema. It mentions 'Python tool code' and 'formatted source', but the schema already describes the parameter as 'The Python source to format.' The description doesn't provide additional semantic details like encoding, size limits, or formatting options.

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 formats Python code using Black and returns the formatted source. It uses a specific verb ('Format') and resource ('Python tool code'), making its purpose unmistakable. Among siblings like validate_tool_code and execute_tool, this tool's formatting function is distinct.

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 when to use (when you need to format Python code) but does not explicitly contrast with alternatives like validate_tool_code or execute_tool. It mentions statelessness and safety, which suggests it can be used freely, but lacks explicit 'when to use' or 'when not to use' guidance.

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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