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post_debug_code

LLM CODE DEBUGGING — POST {code, error} and get a diagnosis: what is wrong, the root cause, and a concrete fix with corrected code. Paste the failing snippet plus the error message or stack trace; any language, up to 20,000 chars combined. Optional {language} and {context} ('happens only on the second call'). Fast cheap LLM under the hood. Want deterministic no-AI lint instead? POST /api/lint/:language ($0.002). ($0.01 per call, paid via x402)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe failing code snippet (required)
errorNoError message, stack trace, or a description of the wrong behavior
contextNoOptional extra context: what you expected, when it happens, what you tried
languageNoOptional language hint, e.g. python, javascript, elixir

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fixNothe concrete change to make
modelNo
usageNo
diagnosisNowhat is wrong, in plain language
confidenceNohigh, medium, or low
fixed_codeNocorrected version of the snippet, when applicable
root_causeNo

Schema Changelog

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

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations indicate non-readonly, non-idempotent, non-destructive. Description adds that it uses a fast cheap LLM, costs $0.01, and processes any language. No contradictions.

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

Conciseness4/5

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

Description is detailed and well-structured but slightly verbose. Front-loaded with purpose, then usage instructions. Could be more concise but effective.

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 output schema exists, description explains return format (diagnosis, root cause, fix). Also covers pricing, language support, and character limit. Fully complete.

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?

Schema covers all parameters with good descriptions. Description adds context: 'Paste failing snippet plus error' and explains optional parameters, slightly enhancing semantics.

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 debugs code by posting code and error, returning diagnosis, root cause, and fix. It distinguishes from sibling tools like post_lint_* by offering an alternative deterministic lint.

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 when to use (debugging with error message) and when not (deterministic lint via /api/lint/:language). Also specifies constraints like 20k char limit and any language.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but the SEO-related tools (head_check, full_audit, site_audit, etc.) overlap in scope, potentially causing confusion despite clear descriptions.

Naming Consistency5/5

Tool names consistently follow a get_/post_/delete_ verb pattern with descriptive noun phrases (e.g., get_seo_head_check, post_store_collection), with no mixing of naming conventions.

Tool Count2/5

With 46 tools covering a wide breadth of domains (SEO, accessibility, music, crypto, linting, etc.), the count is excessive for a single server, feeling unfocused and heavy.

Completeness4/5

The tool set covers most core operations for each sub-domain, but minor gaps exist (e.g., missing update for datastore, limited music operations).