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Export Pro Context

export_pro_context

Creates a .ai-bridge/pro-context.md with repository tree, git state, selected files, and handoff context so ChatGPT can plan without live MCP calls.

Instructions

Create .ai-bridge/pro-context.md with repo tree, git state, selected files, and handoff context for high-context ChatGPT planning without live MCP tool calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoMarkdown title for the context bundle.
max_depthNoRepository tree depth. Default: 3.
max_filesNoMaximum file contents to include. Default: 24.
extra_globsNoAdditional workspace-relative glob patterns to include, for example src/**/*.ts.
include_diffNoInclude the current git diff. Default: true.
workspace_idNoWorkspace id from open_workspace. Omit to use default workspace.
max_file_bytesNoMaximum bytes per included file. Default: 60000.
selected_pathsNoSpecific workspace-relative files to include.
max_total_bytesNoMaximum bytes in the generated bundle.
include_ai_bridgeNoInclude existing .ai-bridge planning files. Default: true.
include_changed_filesNoAuto-include currently changed files from git status. Default: true.
include_important_filesNoAuto-include important root config/docs such as AGENTS.md, README.md, and package.json. Default: true.
Behavior3/5

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

Annotations cover read-only/destructive safety (both false), so the description does not need to repeat that. It adds useful context by listing the bundle's components (repo tree, git state, selected files, handoff context), which goes beyond annotations. However, it does not disclose potential overwrite behavior if the file already exists, nor mention any I/O side effects beyond file creation, leaving a minor transparency gap.

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 a single, tightly worded sentence that fronts the action and purpose. It has zero waste and directly communicates the tool's function without tangential details.

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 tool with 12 parameters and no output schema, the description succinctly covers the overall purpose, the file's contents, and the intended use case (offline ChatGPT planning). It does not explain return values or failure modes, but those are less critical for a file-generation tool. The mention of 'without live MCP tool calls' provides key context for selection. Overall, it is sufficiently complete for an agent to invoke correctly.

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 all 12 parameters are documented in the schema. The description does not add semantic detail beyond what the schema already provides (e.g., it mentions 'selected files' but that maps directly to selected_paths). With full schema coverage, baseline 3 is appropriate.

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 states a specific verb ('Create'), a specific resource ('.ai-bridge/pro-context.md'), and enumerates the contents (repo tree, git state, selected files, handoff context). It clearly distinguishes itself from generic write tools by focusing on the pro-context bundle for ChatGPT planning.

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 explicitly frames when to use it: 'for high-context ChatGPT planning without live MCP tool calls.' This gives a clear use case and implies when it's appropriate (when you need offline context). It does not name specific alternatives or exclusions, but the context is strong enough to guide an agent.

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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