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jkanselaar

Python Code Validator

by jkanselaar
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Validate Python

validate_python
Read-onlyIdempotent

Check Python code before writing it to disk: parse, lint, type-check, and scan for security issues and credentials without running untrusted source.

Instructions

Check Python source without running it: parse, lint (ruff), type-check (mypy), AST security policy, credential scan. Safe on code you do not trust. Use it on every Python file you generated or edited, before writing it to disk. Alternatives: repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs. Auth: a key is required. A free key covers this call, 25 per day, then HTTP 429; get one with POST /v1/keys. Credits are bought without an account, 1 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 ($0.01 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. Of options only transpile_to (e.g. 'javascript', which returns a translated copy in transpiled) acts here; timeout_s, max_iterations, optimize, examples and expected_output need a pass that rewrites or runs the code, so send code alone. Ignored options are not refused, so a call that sets them looks like it worked; and code that does not parse is answered rather than refused: valid=false with the syntax error located, which is the point. 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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv1.17.1
    • addedInput schema / $defs / Options / properties / examples
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "What the code is supposed to do, as doctest lines ('>>> f(2)' on one line, '4' on the next) or as plain assertions ('assert f(2) == 4'). In execute mode they are run in the sandbox: an example that does not hold is a 'python:example-mismatch' error and makes the response invalid, and repair searches for a single-token change that makes every one of them pass. This is the only way the service can tell code that runs from code that is right, so send it whenever you know what you asked for. Examples already written in the code ('>>> ' in any string) are used the same way without this option. Ignored in the other modes, which run nothing.",
+  "title": "Examples"
+}
  2. Addedv1.6.4

TDQS

A5/5.0
Behavior5/5

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

Even though annotations already mark readOnlyHint=true and destructiveHint=false, the description goes far beyond them: it promises safety on untrusted code ('Safe on code you do not trust'), discloses that it never runs code, and highlights silent no-op options ('Ignored options are not refused, so a call that sets them looks like it worked'). It also reveals rate limits and auth behavior (HTTP 429, key requirement, x402 payment flow) and the fact that submitted code is retained to improve the service. 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.

Conciseness5/5

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

The description is long but every sentence earns its place. It is clearly structured with labeled sections (Auth, Arguments, Returns) and front-loaded with the core purpose before diving into details. No filler or redundant repetition.

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?

With an output schema present, the description does not need to enumerate return fields in depth; it still gives a high-level list. It covers input constraints, authentication requirements, call-limits, error behaviors, sibling tool relations, and retention. An agent has everything necessary to call the tool safely and 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 though the schema already covers parameters 100%, the description adds critical meaning: it notes empty/large code fails with HTTP 400/413, line/column numbers count from the submitted fragment, the language must be exactly 'python' despite the broad schema enum, and that only transpile_to among options actual effects in this static tool. It explains that other options are ignored rather than rejected, which is not communicated by the schema alone.

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 opens with a precise verb and resource: 'Check Python source without running it' followed by an explicit checklist (parse, lint/ruff, type-check/mypy, AST security policy, credential scan). It explicitly distinguishes the tool from its siblings: 'repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs.' No ambiguity remains about what this tool does.

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 states exactly when to use this tool ('Use it on every Python file you generated or edited, before writing it to disk') and clearly names alternatives and their purpose. The when-not-to-use is implicit but clear: if you need a corrected file, use repair_python; if you need runtime proof, use execute_python. This is explicit enough to route an agent correctly.

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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