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rbutera

harness-bridge

by rbutera

Ask Codex

codex_query

Use this MCP bridge to send a prompt to OpenAI Codex for a second opinion, unfamiliar code exploration, or implementation delegation. Pass a thread ID to continue prior Codex conversations.

Instructions

Ask OpenAI Codex a question or give it a task. Use for getting a second opinion, exploring unfamiliar code, or tasks that benefit from a different model's perspective.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOverride the Codex model. OMIT this unless explicitly told otherwise: when omitted, the Codex CLI uses the default from ~/.codex/config.toml, which is kept current. This enum may lag behind newly released models.
promptYesThe question or task for Codex
threadIdNoContinue a previous Codex conversation: pass the threadId returned by an earlier call. Omit to start a fresh thread.
workingDirectoryNoWorking directory (defaults to server cwd)

Schema Changelog

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

  1. Changed2 schema fields changedv1.0.0
    • addedInput schema / properties / threadId
      Added value: +{
      +  "description": "Continue a previous Codex conversation: pass the threadId returned by an earlier call. Omit to start a fresh thread.",
      +  "type": "string"
      +}
    • removedInput schema / properties / threadKey
      Removed value: -{
      -  "description": "Optional key enabling multi-turn continuity. Calls sharing a key use the same harness conversation and are serialized. Omit it for independent one-shot calls, including parallel reviews.",
      -  "type": "string"
      -}
  2. First observedv0.4.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'ask a question or give it a task,' which is ambiguous about whether Codex may execute code, modify files, or have side effects. It also does not mention thread continuation behavior or what the response contains, leaving significant behavioral gaps.

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 two sentences with no filler. The core purpose is front-loaded, and the usage guidance is placed efficiently after the action. Every sentence earns its place.

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

Completeness3/5

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

The description covers the high-level purpose and typical use cases, but because there is no output schema and no annotations, it misses important context: what a call returns, whether threadId allows continuing conversations, and whether 'give it a task' can lead to file mutations. It is adequate for a general query tool but not fully complete.

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 schema already documents all parameters, including the model override caveat and threadId semantics. The description adds little beyond saying the prompt is 'the question or task,' which is consistent with the schema; baseline 3 is appropriate.

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 clearly states a specific action ('Ask OpenAI Codex a question or give it a task') and identifies the resource. It suggests general use cases like 'second opinion' and 'exploring unfamiliar code,' but does not explicitly distinguish itself from siblings such as codex_explain_code or codex_review_code, so it falls short of a 5.

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

Usage Guidelines4/5

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

The description provides clear contexts for use: 'second opinion,' 'unfamiliar code,' and 'different model's perspective.' It does not, however, state when not to use it or explicitly name alternatives, so the guidance is present but lacks exclusions.

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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