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AxonityAI

Axonity Flow MCP Server

Official
by AxonityAI

execute_tool

Run tool code directly for testing before creation. Returns stdout, stderr, result, and typed error on failure.

Instructions

Run tool code directly, WITHOUT saving it — the way to test a tool you are authoring, before create_tool/update_tool. Returns stdout/stderr, the result, and a typed errorType (timeout/memory/import/runtime/validation) on failure. Pass toolId to run against an already-saved tool's context.

This does NOT satisfy the publish gate, however cleanly it runs. It executes the functions YOU supply, which need not be the tool's stored code, so a pass here proves nothing about what would actually ship. To clear the dry_run_required blocker on a SAVED tool, use dry_run_tool — that runs the tool's own stored implementation and is the only run that counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolIdNoAn existing tool id to run in context of.
classesNoOptional classes (max 20).
importsNoThe import block. Defaults to empty.
timeoutNoSeconds. Default 30.
functionsYesThe functions to run (max 20).
inputParamsNoParameters passed into the entry-point function.
Behavior5/5

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

No annotations provided, but the description fully discloses that it runs user-supplied functions, returns stdout/stderr, result, and error types on failure, and does not prove anything for publishing.

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 paragraphs with clear front-loading of purpose, limitations, and contrast with sibling tools. Every sentence provides value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, 1 required, no output schema, the description explains return values, behavioral limitations, and differences from related tools, making it highly complete for an agent to decide and invoke correctly.

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 baseline is 3. The description adds minimal extra context beyond the schema definitions, e.g., mentioning toolId for saved tool context, but does not elaborate on other parameters.

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 runs tool code directly without saving, for testing before create/update, distinguishing it from siblings like dry_run_tool and create_tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says to use for testing a tool being authored before saving, and specifies it does not satisfy the publish gate, contrasting with dry_run_tool for the dry_run_required blocker.

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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