Deploy a solution FROM its GitHub repo. Reads .ateam/export.json + connector source from the repo and feeds it into the deploy pipeline. Use this to restore a previous version or deploy from GitHub as the source of truth.
| Name | Required | Description | Default |
|---|---|---|---|
| solution_id | Yes | The solution ID to pull and deploy from GitHub |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses what files are read and that data feeds into the deploy pipeline, which is useful. However, it doesn't mention potential side effects (e.g., overwriting existing deployment), required permissions, or reversibility—leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary purpose, followed by a succinct use-case statement. No filler or redundant content—every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description adequately covers its function, inputs, and typical usage. It could mention prerequisites like the need for a .ateam/export.json file, but overall it is sufficiently complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the only parameter (solution_id) is clearly described as 'The solution ID to pull and deploy from GitHub.' The tool description adds no new parameter information beyond what the schema already states, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear, specific action: 'Deploy a solution FROM its GitHub repo.' It further clarifies the mechanism (reads .ateam/export.json + connector source) and distinguishes this tool from siblings like ateam_github_push and ateam_redeploy by explaining its role as pulling from GitHub to deploy.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: 'Use this to restore a previous version or deploy from GitHub as the source of truth.' This gives clear context for when to choose this tool, though it doesn't name alternative tools or state when not to use it.
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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Most tools have clearly distinct purposes, with detailed descriptions that differentiate similar functions like chain polling vs. chain inspection. However, there is slight overlap between ateam_design_advisor, ateam_get_spec, and ateam_spec_search, which all serve design guidance, potentially causing confusion if descriptions are not read carefully.
The naming mostly follows a consistent verb_noun pattern with the 'ateam_' prefix (e.g., ateam_get_solution, ateam_create_connector, ateam_test_skill). Minor deviations include ateam_patch (missing object) and ateam_redeploy (verb only), but overall the pattern is predictable and clear.
With 47 tools, the count is high and exceeds the typical 15-tool threshold for a well-scoped set. However, the tools cover a broad and complex platform (auth, deployment, testing, GitHub integration, scaffolding), and each tool appears to have a distinct role, making the count borderline acceptable rather than excessive.
The tool set covers the full lifecycle of building, deploying, testing, and managing A-Team solutions, including design, GitHub integration, and verification. Minor gaps exist, such as no explicit tool for deleting individual files (though patching can overwrite) and no standalone skill listing, but these are not critical dead ends for an agent.
