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Python Code Validator

by ai.statemind

Execute Python

execute_python
Idempotent

Everything repair does, and then RUNS the code in a throwaway container — no network, read-only filesystem, killed at options.timeout_s — reporting exit code, stdout and stderr. Any '>>>' examples in the code are run too, and one that does not print what it says is an error the other tools cannot see. This is a side effect: do not submit code you do not want executed. Use it when you need proof that the code runs, or that it does what it says. Alternatives: validate_python for the diagnosis and repair_python for the fix, neither of which runs anything. Auth: a key is required. This call needs a paid key and answers HTTP 402 without one. Credits are bought without an account, 10 per call: GET /v1/pricing says where to send the xDAI. Or pay for this one call with no key at all: call it without one and the result carries x402 payment requirements ($0.1 in USD Coin on eip155:8453); sign them and repeat the call with the payment in _meta['x402/payment']. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. options.max_iterations (1..10, default 3) caps the fix/verify rounds: raise it for a file with several independent faults, leave it for a snippet. options.optimize (default false) additionally folds constants and drops dead code, and is only worth setting when you asked for a rewrite anyway. options.transpile_to (e.g. 'javascript') returns a translation of the repaired source in transpiled, not of what you sent. fixed_code is null when nothing could be proven safe to change, so treat null as 'no fix', not as an error. options.timeout_s (seconds, default 5) is the wall clock for the run; the schema allows up to 60 but this deployment caps it at 30 and refuses a larger value with 400. options.expected_output compares stdout byte for byte and adds an 'expected-output' diagnostic (valid=false) when it differs, which is how you ask for 'it did the right thing' rather than 'it ran'. options.examples is the same question for code with no output: pass what you asked for as doctest lines ('>>> total([1, 2])' then '3') or assertions ('assert total([1, 2]) == 3'), and each is run against the code -- one that does not hold is a 'python:example-mismatch' error, and repair looks for a single-token change that makes them all pass. Send it whenever you know what you asked for: without it, code that runs but returns the wrong answer looks perfect from here. The program that runs is the repaired one, so read fixed_code before you trust runtime.stdout, and it runs exactly once however many rounds the repair took. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe source to check, as a whole file where possible: diagnostics carry the line and column of the text you send, and a fragment hides the imports and definitions the type check needs. A deployment may accept fewer bytes than the 200000 here.
optionsNoTuning knobs. Most of them only take effect in the mode that does the corresponding work; see each field.
languageNoThe language of the code. A service that does not handle it refuses the request rather than guessing; the enum is shared across services, so it lists more than any one of them accepts.python

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYes
fixesNo
scoreYes
validYes
runtimeNo
securityNo
fixed_codeNo
transpiledNo
diagnosticsNo

TDQS

A4.8/5.0
Behavior5/5

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

The description goes far beyond the boolean annotations by disclosing side effects (code execution), sandbox limitations (no network, read-only filesystem, timeout), authentication/payment requirements (HTTP 402, xDAI credits, x402 fallback), and subtle behaviors (fixed_code null semantics, repaired code runs exactly once, examples/expected_output diagnostics). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is functionally rich but overly long, repeating some schema-level parameter details and including extensive edge-case instructions. It is organized with clear labels (Auth, Arguments, Returns) and front-loads the core purpose, but would benefit from trimming redundant parameter explanations to improve readability.

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?

The description covers all essential contextual aspects: side effects, sandbox restrictions, auth, parameter nuances, error handling (402, 400, 413), output schema pointer, and data retention policy. Given the tool's complexity, the description is complete enough for an agent to select and invoke it correctly.

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

Parameters5/5

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

Even with 100% schema coverage, the description adds significant value: byte-length measured after UTF-8 encoding, 400/413 errors, line numbering starting at 1, deployment-specific timeout cap of 30 vs schema's 60, and clarifications on examples/expected_output behavior that inform parameter usage.

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's function: it performs everything repair_python does and then RUNS the code in a throwaway container, reporting exit code, stdout, and stderr. It also explicitly distinguishes itself from siblings validate_python (diagnosis only) and repair_python (fix only), noting neither runs code.

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?

It explicitly tells when to use it: 'Use it when you need proof that the code runs, or that it does what it says.' It names alternatives and their non-execution nature, and provides parameter guidance such as raising max_iterations for files with multiple faults and setting expected_output or examples to verify correctness.

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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TDQS

A4.9/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: validate only diagnoses, repair diagnoses and fixes, execute diagnoses, fixes, and runs. The descriptions explicitly state the differences and alternatives, leaving no ambiguity about which to choose.

Naming Consistency5/5

All tools follow the same verb_noun pattern: validate_python, repair_python, execute_python. The naming is perfectly consistent and predictable.

Tool Count5/5

Three tools is a well-scoped set for a Python code validator. Each tool adds a distinct level of functionality (diagnose, fix, run), and there are no redundant or unnecessary tools.

Completeness5/5

The toolset covers the full lifecycle of Python code validation: diagnose (validate), fix (repair), and verify (execute). The options within the tools (e.g., transpile, optimize, examples) further round out the surface, leaving no critical gaps.

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