Deepseek MCP Server vs Ateam MCP

ateam_get_spec

Get the A-Team specification — schemas, validation rules, system tools, agent guides, and templates. Start here after bootstrap to understand how to build skills and solutions. Use 'section' to get just one part of the skill spec (much smaller than the full spec). Use 'search' to find specific fields or concepts across the spec. When designing a persona that orchestrates logic via run_python_script (the Python-as-orchestrator pattern), also fetch topic='python_helpers' — that returns the adas.* helper namespace reference. Skills designed without knowing about adas.* produce 5-10x larger / brittler scripts. When wiring widgets (UI plugins) into a solution, fetch topic='widgets' — that returns the widget spec (catalog model, how_to_use blocks, opener_call shape, persona phrasing rules, binding semantics) so you can declare `ui_plugins` correctly. For the live catalog of widgets actually available in a deployed tenant, use ateam_get_widget_catalog instead.

ateam_get_workflows

Get the builder workflows — step-by-step state machines for building skills and solutions. Use this to guide users through the entire build process conversationally. Returns phases, what to ask, what to build, exit criteria, and tips for each stage.

ateam_get_examples

Get complete working examples that pass validation. Study these before building your own.

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.

ateam_spec_search

Semantic search over the FULL ateam platform /spec documentation — the deep fallback behind ateam_design_advisor. Ask a natural-language 'how do I…' question and get the most relevant doc chunks (with their topic + heading), then read the full topic via ateam_get_spec(topic). Use this when the advisor's pointer isn't enough, or for details/examples on anything — including topics outside the curated capability list. Read-only.

ateam_build_and_run

DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.

ateam_test_skill

Send a test message to a deployed skill and get the execution result. Wait modes (wait_for): • 'root' (default, back-compat) — wait until the message's root job completes, return single-job result. Fast, ignores any sub-skills the root delegated to via askAnySkill. • 'chain' — wait until EVERY job in the chain (root + handoffs + askAnySkill subcalls, recursively) reaches a terminal state, then return the full chain tree. Use when testing multi-skill flows (orchestrator → workers, builders → sub-builders, etc.). The response.chain field carries chainJobs[] with parentJobId/relation/depth and executionSteps[] with tool-nesting (opId/parentOpId/_toolDepth). Legacy: wait:false is equivalent to wait_for:'never' — returns job_id immediately for polling via ateam_test_status. wait:true is the same as the default wait_for:'root'.

ateam_test_notification

Fire a REAL notification at an existing actor in a deployed solution — for end-to-end testing of the system-initiated notification path (telegram/push/app channels). Unlike ateam_test_skill (synthetic test actor with no channels) and ateam_conversation (user-initiated thread), this calls the /api/internal/notify-user path that PCM and other sibling services use — so the actor's real enabled channels actually receive the message. Use for: • Channel fan-out smoke (does telegram/push/app actually receive it?) • Delivery-result verification (per-channel ok/failed in the response). Auth: forwards your authed api_key to Core (no master-secret involvement). Tenant is pinned by the key itself — cross-tenant targeting is structurally impossible. ⚠️ SAFETY: • The text is prefixed with [TEST] in the actual notification — visible to the user, anti-phishing. • Rate-limited: 10 calls/min per session. • Every call is audited (caller, tenant, actor, content hash) regardless of outcome. • actor_id is scoped to your tenant — cross-tenant targeting is rejected by Core's per-tenant Mongo isolation. • reply_handler is NOT supported via api-key auth (Core ignores it). Routing the user's next reply to an arbitrary skill is a privilege-escalation surface. For routing/engagement tests, use ateam_test_skill.

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.

ateam_test_pipeline

Test the decision pipeline (intent detection → planning) for a skill WITHOUT executing tools. Returns intent classification, first planned action, and timing. Use this to debug why a skill classifies intent incorrectly or plans the wrong action.