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ateam_github_write

Write files to a GitHub repository for connector solutions. Use this to create or update connector code files individually on the dev branch.

Instructions

Write a file to the solution's GitHub repo. Use this to create new connector files or replace existing ones — one file per call. This is the PRIMARY way to write connector code after first deploy. Write each file individually (server.js, package.json, UI assets), then call ateam_github_promote() to ship to prod (dev→main), then ateam_build_and_run() to deploy.

DEFAULTS TO dev BRANCH.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
solution_idYesThe solution ID
pathYesFile path to write (e.g. 'connectors/my-mcp/server.js', 'connectors/my-mcp/package.json')
contentYesThe full file content
messageNoOptional commit message (default: 'Write <path>')
refNoTarget branch. Default: 'dev'.dev
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the write behavior (creates/replaces, one file per call, defaults to dev branch). However, it does not disclose details about authentication, what happens on overwrite, or commit behavior beyond the message parameter. The description is adequate but lacks some behavioral specifics.

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 three efficient sentences plus a one-line note, with no wasted words. Key information is front-loaded, and the structure flows logically from action to usage to pipeline guidance.

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 the tool's simplicity and full parameter coverage, the description is complete. It explains the tool's role in the deployment pipeline, references related tools (ateam_github_promote, ateam_build_and_run), and provides all necessary context for an AI agent to use it correctly.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the path pattern ('connectors/my-mcp/server.js') and reinforcing that content is full file content. It also clarifies the commit message default and branch default, providing context beyond the schema fields.

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 action ('Write a file'), the resource ('solution's GitHub repo'), and the purpose ('create new connector files or replace existing ones'). It distinguishes itself from siblings by specifying it's the primary way to write connector code after first deploy and mentions the subsequent pipeline steps.

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 when to use this tool ('after first deploy', 'write connector code') and the context of use (one file per call, defaults to dev branch). It also guides on what to do after writing (promote, build and run). It doesn't explicitly state when not to use, but the context implies it's for writing connector files specifically.

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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