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configure_coding_agent_runtime

Configure the coding agent runtime for Lightbulb by selecting Codex or Claude Code as the backend, then specify model provider, model ID, and optional connection settings.

Instructions

Configure Codex or Claude Code as the Lightbulb coding agent runtime.

Args: runtime_backend: codex_app_server, codex, claude_agent_sdk, or claude_code. model_provider: Optional provider such as openai or anthropic. model_id: Optional model id. Defaults are chosen by the platform. provider_connection_id: Optional existing AI provider connection UUID. use_codex_account: Must remain false (the default). Connected Codex account execution is disabled until account-synced managed requirements and hooks can be isolated from the agent-worker host. Use a governed Lightbulb provider connection instead. scope: USER, COMPANY, or TENANT. USER is the normal personal setting. request_overrides: Optional JSON object with additional safe runtime hints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoUSER
model_idNo
model_providerNo
runtime_backendYes
request_overridesNo{}
use_codex_accountNo
provider_connection_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Addedv0.1.1

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It does disclose a critical constraint: use_codex_account 'Must remain false' with a rationale about account-synced managed requirements and hooks. However, for a mutation/configuration tool it omits behavioral traits such as whether configuration persists, applies immediately, overwrites existing settings, or requires testing afterward. Some disclosure, but meaningful gaps remain.

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?

A one-sentence summary is front-loaded, followed by a clean Args block where each parameter gets a single informative line. The longer use_codex_account entry earns its length by conveying a safety-critical constraint. No filler or repetition of schema defaults.

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

Completeness4/5

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

For a 7-parameter configuration tool with zero annotation coverage, the description covers parameter semantics, allowed values, defaults, and a dangerous combination to avoid. The output schema exists, so return values need no explanation. The remaining gap is post-configuration behavior (persistence, immediate effect, whether to verify with test_agent_runtime_config), which would make it fully complete.

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?

Schema description coverage is 0%, so the description fully compensates. Every one of the 7 parameters is documented in prose: runtime_backend lists all allowed values, scope enumerates USER/COMPANY/TENANT with a default recommendation, use_codex_account carries a warning plus guidance to use a governed Lightbulb provider connection, and request_overrides is described as a JSON object of safe runtime hints. This adds substantial meaning beyond the bare schema titles.

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 specific verb-resource pair: 'Configure Codex or Claude Code as the Lightbulb coding agent runtime.' It names the exact backends (codex_app_server, codex, claude_agent_sdk, claude_code) and distinguishes this configuration concern from sibling tools like configure_backbone_agent_surface and get_agent_runtime_config by scoping it to the coding agent runtime.

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 gives clear context on how to use the tool correctly: which runtime_backend values are valid, that USER is the normal scope, and that provider connections should be used instead of use_codex_account. However, it never explicitly says when to choose this tool over the closely related siblings (get_agent_runtime_config, list_agent_runtime_options, test_agent_runtime_config), so usage context is strong but exclusion guidance is absent.

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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