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SynAgent MCP - Cross-Agent CLI Bridge

codex_debug_error

Diagnose errors, exceptions, stack traces, and unexpected test or build failures by consulting OpenAI Codex to identify root causes and suggest fixes.

Instructions

Consult OpenAI Codex to diagnose errors, exceptions, stack traces, or unexpected test/build failures and suggest solutions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel to use (default: "gpt-6.1-sol", options: "gpt-6.1-sol", "gpt-6.0-sol", "luna", "astra").
contextNoAdditional context, what command was run, expected behavior, or relevant logs.
file_pathsNoOptional list of file paths relevant to the failure.
error_messageYesThe exact error message or stack trace to diagnose.
user_confirmedNoMandatory true confirmation if using the top-tier "astra" model.
workspace_pathNoOptional absolute path to workspace root.
reasoning_effortNoReasoning effort depth (default: "xhigh" for debugging).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It never states that this is a read-only external consultation that returns suggestions rather than applying fixes, nor does it disclose that data is sent to OpenAI Codex, nor any confirmation/auth requirement (the astra-tier confirmation lives only in the schema).

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?

A single front-loaded sentence with no filler; the purpose and outcome land immediately. It is slightly under-specified rather than bloated, which is not a conciseness failure.

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

Completeness2/5

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

For a 7-parameter tool with no annotations and no output schema, one sentence is thin: the description says nothing about the returned diagnosis format, latency/cost implications of the model tiers, or when the user_confirmed gate must be set. The schema covers parameters, but behavioral context is largely absent.

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 the model tiers, reasoning_effort default ("xhigh"), file_paths, context, and user_confirmed gate are all already documented in the schema. The description adds no parameter meaning beyond that, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb ("diagnose") plus the resource class (errors, exceptions, stack traces, unexpected test/build failures) and the outcome (suggest solutions). It is clearly more than a restatement of the name, but it never distinguishes itself from overlapping siblings such as codex_analyze or codex_consult.

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

Usage Guidelines2/5

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

Usage is only implied by the noun list ("when you have an error"). There is no explicit when-to-use, no exclusion, and no routing to alternatives even though siblings codex_analyze and codex_consult plausibly overlap with debugging-by-consultation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.