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ateam_conversation

Send a chat message to a deployed solution. No skill_id needed — the system auto-routes to the right skill.

ALWAYS ASYNC: returns a chain_id immediately — the assistant's reply is NOT in this response (a conversation can run for minutes across handoffs + subcalls, so a synchronous wait would hit the 100s edge timeout → 524).

POLL BY CHAIN, NEVER BY JOB: an individual job can terminate while the chain is still running, so poll ateam_chain_status(chain_id) on a loop (~2s) and stop when chain_done === true (or pending_question is set — the assistant is waiting on the user). That is the cheap chip-quick poll (Core's whole-chain computeChainStatus — the same thing the standard chat uses). Use ateam_get_chain(chain_id) only ONCE at the end if you want the full tree / per-job detail — it's too heavy to loop on.

Multi-turn: pass the actor_id from a previous response back in to continue the same thread (e.g. reply to a confirmation prompt). Each call starts a new chain; the same actor_id maintains conversation context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesThe message to send (e.g., 'send email to X' or 'I confirm')
actor_idNoOptional: actor ID from a previous response to continue the conversation. Omit for a new conversation.
solution_idYesThe solution ID

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: it's ALWAYS ASYNC and returns a chain_id immediately, the reply is not in the response, and a conversation can run for minutes. It also explains the difference between chain and job termination and identifies which polling endpoint is cheap vs heavy. This goes far beyond typical descriptions.

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?

Every sentence contributes critical information: purpose, async behavior, polling strategy, and multi-turn context. The description is longer than typical but each part earns its place and is logically structured in short paragraphs. No redundancy or filler.

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?

Despite having no output schema, the description explains the return (chain_id), how to obtain final results (poll ateam_chain_status, use ateam_get_chain once at end), and how to handle multi-turn. For a tool with async complexity and interaction with sibling tools, this is thoroughly complete.

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 covers all parameters so baseline is 3. The description adds meaning over the schema by explaining actor_id's role in multi-turn continuity, giving example message content, and clarifying that solution_id identifies the deployed solution. It doesn't repeat schema text verbatim, adding practical semantics.

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's purpose: 'Send a chat message to a deployed solution.' It specifies the resource (deployed solution), the verb (send), and adds a key differentiator ('No skill_id needed — the system auto-routes'). This distinguishes it from sibling tools like ateam_chain_status and ateam_get_chain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage guidance: never poll by job, poll by chain using ateam_chain_status, use ateam_get_chain only once at the end, and pass actor_id for multi-turn. It even names alternative tools and when to use them, which is excellent.

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.