View commit history for a solution's GitHub repo. Shows recent commits with messages, SHAs, timestamps, and links. Default reads from main (prod). Pass ref: 'dev' to see in-progress work.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | No | Branch to read commits from. Default: 'main'. | main |
| limit | No | Max commits to return (default: 10) | |
| solution_id | Yes | The solution ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that this is a read operation ('View', 'reads'), states the default branch is production, and lists the return fields. It does not mention pagination limits or auth, but the read-only nature and default behavior are well conveyed.
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 compact sentences front-load the purpose and immediately give the most important usage tip. Every word earns its place; no filler, fluff, or repetition of schema details.
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 read-only tool with three parameters and no output schema, the description covers purpose, output contents, and branch semantics. It could mention auth requirements or error behavior, but given the tool's simplicity and the schema's completeness, this is adequate and near-complete.
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 coverage is 100%, so the baseline is 3. The description adds value by explaining the real-world meaning of the `ref` parameter (prod vs dev) and confirming the default branch. The `limit` parameter is present in the schema with a default, and while not detailed in prose, the schema already handles it.
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 specific verb and resource: 'View commit history for a solution's GitHub repo.' It clearly distinguishes this from sibling github_* tools like diff, read, or status by stating the exact output (commits with messages, SHAs, timestamps, links).
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?
It gives clear context on when to use the default 'main' branch (prod) and when to pass 'ref: 'dev'' for in-progress work. It does not explicitly name alternative tools or exclusion cases, but the branch guidance is practical and sufficient for a tool whose sibling alternatives are obvious from names.
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.
