Categories › AI & Agents › Multi-agent, orchestration & workflows
Connect AI clients to your Dactic work context through a hosted MCP service.
Remote server: your requests and data go to mcp.app.dactic.io. Claude Code: claude mcp add --transport http dactic https://mcp.app.dactic.io/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
list_agents — List the Dactic agents you (the signed-in user) have deployed, newest first. Returns each agent's id, name, platform and status. Use load_agget_agent — Get one of your Dactic agents by id: its name, platform, status, complete instructions and every skill (name, description, full procedure). load_agent — Load one of your Dactic agents (by agent_id or exact agent_name) so you can run it in this chat: returns its complete instructions and skillinvoke_agent — Run one of your deployed Dactic agents. Claude agents start a Managed Agent session; Monday agents are run by Monday's own board automation.resolve_terms — Look up what company-specific terms mean, from your organization's confirmed Dactic terminology only. Terms that aren't confirmed come back check_freshness — Is my Dactic knowledge up to date? Reports each surface (app sync, knowledge, terminology, playbook, department policies, skills, and optionget_policy — Get your department's current policies with their source document. With query: the policies that apply to it (all of them when your departmelist_skills — List your active Dactic skills (procedures derived from your interviews). disclosure_tier L1 returns a catalog; L2/L3 add the procedure. Useget_skill — Get one of your Dactic skills by id, with its full procedure at disclosure_tier L3.ask_knowledge — Answer a question from your own Dactic context: matches holds evidence from your synced work data and current department policies related toget_context_bundle — Get everything a task needs from Dactic in one call; use it before doing a task, especially when running an agent. With agent_id: that agentget_user_context — Your own Dactic profile for personalising answers: job title, team, department and role; work context (role, guardrails, preferences); knowlget_knowledge_provenance — Where did that come from? For refs returned by other Dactic tools (1-20), describes each item's source and a confidence of high, medium or lsubmit_agent_feedback — File feedback about one of your own Dactic agents (e.g. 'it keeps CC'ing finance'). Quote the user's words in detail. First call without concheck_against_policy — Advisory check of a draft reply, plan or action against your department's Dactic policies. Returns findings that quote the policy and the drget_skill_usage — How often your Dactic skills were listed and loaded by AI clients (ChatGPT, Claude…) over the last 7, 30 or 90 days, how many live agents ussynthesize_agent — Draft a new Dactic agent for a task the user describes, built only from the user's own Dactic skills, policies and work context. Runs in thecreate_agent_from_process — Draft a new Dactic agent, and the new skills it needs, from a process the user describes in this chat — use this when the user's existing skget_task_status — Status of a background task you started (e.g. synthesize_agent): queued, running, succeeded (with its result) or failed (with error_message)search — Search your Dactic knowledge (your skills, agents and playbooks, and your organization's confirmed terminology, plus current department polifetch — Fetch the full text of one Dactic item by the id search returned, with a link to it.get_usage — Your Dactic AI usage (tokens by operation) over the last N days; scope=org is for org admins.get_sync_status — Report recorded sync progress, last successful sync, approved source coverage and safe recovery guidance. Credential linkage is not proof ofget_feedback_status — Follow the recorded processing/application state of your submitted agent feedback. No immediate agent change is implied.recommend_agent — Match a task to accessible deployed agents by their stored instructions and skills. Returns candidate evidence and limitations; never runs ocompare_agent_versions — Compare two stored artifact versions of the same accessible agent/platform, with instruction diffs and skill additions/removals. Missing guafind_context_gaps — Assess task preparation using available knowledge, skills, policies, terminology and sync readiness. Returns blocking/nonblocking gaps; unknget_entity_context — Follow bounded explicit stored links from an approved board, accessible agent/project or confirmed term. Returns entities, relationship baseget_context_delta — Retrieve added/updated/removed permitted knowledge and published policies since an actor-bound snapshot cursor. Omit cursor for a complete bretrieve_similar_cases — Find comparable recent runs or resolved feedback owned by you. Returns recorded summaries/outcomes, citations and uncertainty; missing actioquery_work_dataset — Query the approved synced Monday item view with supported scalar filters, sorting, grouping and count/sum aggregation. Fields are base identguide_workflow — Build advisory human-reviewed workflow options using accessible agents, context gaps, connection/sync readiness and provisional platform caprecommend_new_agents — After confirming the user's goal, save a new recommendation iteration containing validated Claude draft proposals from their own skills/contdeploy_agent — First deployment of your selected saved draft. Call without confirmation_code for a preview, show it to the user and obtain authorization, tNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.