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ateam-mcp

by io.github.ariekogan

ateam_design_advisor

CONSULT THIS DURING DESIGN — before and while you design a skill/solution. Describe what you're building; it returns POINTERS to the platform capabilities that fit (per-actor storage, widgets, triggers, sub-agents, mobile data, run-scripts, multi-skill, GitHub, …), each with the /spec topic to read next (via ateam_get_spec) and the tool to wire it. Also returns 'missing' hints (capabilities your goal implies but the design hasn't wired) and lifecycle hints (e.g. connect GitHub when the project will iterate). ADVISORY ONLY — you decide and own the design. Stateless: pass the current design_state each call; consult it as often as you like as the design evolves.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesWhat you're trying to build, in your own words (e.g. 'a coach that tracks each user's meals from photos and shows a dashboard').
design_stateNoOptional. The design so far (skills, connectors, capabilities already wired) so the advisor can point at what's still missing. Pass {} at the start.

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full burden and discloses statelessness, advisory-only nature, and that it returns pointers/missing/lifecycle hints. It explains how to use design_state and that results are pointers, not mutations, though it doesn't detail output structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured, front-loading the purpose with 'CONSULT THIS DURING DESIGN' and then detailing outputs, advisory nature, and statelessness. Though long, each sentence adds distinct value and the tool's complexity justifies the length.

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 no annotations or output schema, the description covers when to use, what it returns (pointers, missing, lifecycle), how to call (design_state, stateless), and constraints (advisory only). It lacks an explicit return structure, but the pointer descriptions and references to ateam_get_spec sufficiently guide an agent.

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?

Schema has 100% coverage, so baseline is 3; the description adds semantics by explaining design_state as 'the design so far' and instructing to pass {} at the start. It also ties goal to 'Describe what you're building' with an example, going beyond schema.

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 tool is a design advisor to consult during design, returning pointers to platform capabilities, missing hints, and lifecycle hints. It distinguishes itself from siblings by directing users to ateam_get_spec and other tools for follow-up, reinforcing its advisory role.

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?

It explicitly says 'before and while you design a skill/solution' and 'consult it as often as you like as the design evolves.' It also sets a boundary with 'ADVISORY ONLY — you decide and own the design,' though it doesn't name alternative tools for design advice beyond pointing to specs.

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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TDQS

A3.9/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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