Dactic

io.dactic由 dactic.io 发布官方发布者v0.1.2更新于 Nov 4, 2026

Connect AI clients to your Dactic work context through a hosted MCP service.

已验证Streamable HTTP可网页运行效率与工作流知识与记忆AI 与机器学习

概览

AI 生成的概览

托管的 Dactic MCP 服务,让助手使用登录用户自己的智能体、技能、术语、部门政策和工作上下文。

可以做什么
  • 列出、查看、加载并运行用户已部署的智能体,也可起草、推荐、比较或部署新智能体
  • 搜索并获取用户的技能、已批准剧本、已确认术语和部门政策
  • 基于用户自身工作上下文回答问题、解析公司术语,并追溯每条结果的来源
  • 对照部门政策检查草稿、提交智能体反馈,并跟踪后台任务与同步状态
  • 查询已批准的 Monday 同步条目视图,查看用量、上下文缺口、变更快照和相似历史案例
适用场景
当助手需要依据某个组织自己的智能体、流程和政策而非通用知识来工作时适用,例如在对话中运行已部署的智能体、检查草稿回复是否符合部门政策,或基于现有技能起草并部署新智能体。
运行要求
需要一个远程 Streamable HTTP 端点,以及 Dactic 账号和相应功能权限;可用工具取决于账号与权限。登录通过客户端的 OAuth 授权流程完成。可选的 stdio 桥接需要 Node.js 22.12 或更高版本,无头或 Docker 场景需通过 DACTIC_MCP_TOKEN 环境变量提供 bearer 凭证。
安装前请注意
该服务持有工作上下文、政策和同步的工作数据,因此应把 OAuth 缓存目录(MCP_REMOTE_CONFIG_DIR,通常位于用户主目录下)和任何 DACTIC_MCP_TOKEN 值当作密码保管,切勿提交到代码库。会修改数据的工具(如提交反馈或部署智能体)会先要求确认并返回带 confirmation_code 的预览;部署还会经过计费校验。政策检查与推荐仅为建议,不构成合规签核。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Dactic,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.dactic%2Fdactic-mcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "dactic": {
      "type": "http",
      "url": "https://mcp.app.dactic.io/mcp"
    }
  }
}

README

Dactic MCP

Connect MCP clients to your Dactic work context through the hosted service at https://mcp.app.dactic.io/mcp. A Dactic account and access to the requested capabilities are required. Available tools depend on your account and permissions.

This repository contains the public client manifests, installation guidance, and complete source for the stdio launcher. The launcher uses the pinned mcp-remote transport and OAuth implementation. Backend processing remains on the hosted Dactic service. Reviewers and users use the same integration.

Draft release: the npm installation example is reserved for after @dactic/[email protected] is published. Use a local checkout today. Marketplace approval and authenticated production verification are tracked in the release checklist.

Connect directly (recommended)

Add a Streamable HTTP MCP server with URL https://mcp.app.dactic.io/mcp in your client's MCP settings, then complete the client's OAuth sign-in and consent flow. No API key is embedded in this repository.

For Cursor, merge the following entry into your existing MCP configuration:

json
{"mcpServers":{"dactic":{"url":"https://mcp.app.dactic.io/mcp"}}}

For Claude Code, use the bundled .mcp.json or run:

sh
claude mcp add --transport http dactic https://mcp.app.dactic.io/mcp

The repository also includes Cursor, Claude, and portable plugin manifests. For a local Claude plugin check, run claude plugin validate .; for a local session, run claude --plugin-dir /absolute/path/to/dactic-v1-mcp-public. See client installation for the marketplace formats.

Connect through stdio

Install Node.js 22.12 or later. From a checkout:

sh
npm cinpm run verifynode src/cli.js --help

Merge examples/stdio.local.json into the host's configuration and replace its absolute path with your checkout path. Launch from the MCP host so stdin/stdout stay connected. The bridge opens your browser when OAuth consent is needed. Do not enter account credentials in client JSON.

After the npm release, examples/stdio.npm.json runs the same bridge using npx -y @dactic/[email protected].

Headless environments and Docker

When your account supports a Dactic bearer credential, supply it through the DACTIC_MCP_TOKEN environment variable. Configure the value in your host's secret settings or a private environment; never commit it. The bridge passes a literal environment placeholder to its child process, never the credential in process arguments. OAuth and bearer credentials retain the backend's normal permission checks.

sh
docker build -t dactic-mcp:local .docker run --rm -i -e DACTIC_MCP_TOKEN dactic-mcp:local

This uses an already-set host environment variable. Do not use -t: stdout must remain a clean MCP stream. Headless Docker requires a bearer credential; an interactive browser OAuth callback is not configured by this container.

Configuration and diagnostics

VariableBehavior
DACTIC_MCP_TOKENOptional bearer credential; omitted means browser OAuth
DACTIC_REQUEST_IDOptional safe correlation ID; otherwise a UUID is created
MCP_REMOTE_CONFIG_DIROptional OAuth cache directory; contains credentials
DACTIC_MCP_URLProduction URL or a loopback fixture for local verification

OAuth requests the base scopes openid offline_access mcp/mcp:tools. Additional capabilities depend on server authorization and client consent; this launcher does not request organization-wide usage scopes by default. OAuth storage and refresh are handled by mcp-remote, normally under ~/.mcp-auth. Protect that cache like a password and do not share it.

The bridge writes only protocol messages to stdout. JSON lifecycle diagnostics go to stderr with a request_id, which is also sent as X-Request-ID to the service. Logs classify connection failures and retain safe stack function frames; upstream response bodies, authorization URLs, and credentials are discarded. Search diagnostics by request_id and events bridge.fail or bridge.dependency.fail. Connection establishment is bounded by a 15-second connect timeout, 60-second headers timeout, 300-second body idle timeout, and 120-second browser authorization callback timeout.

For authentication failures, reconnect using your host's sign-in flow. For permission failures, check account access; repeated authentication will not grant new permissions. For network errors, check endpoint availability and your network. Share only a correlation reference with support.

Public source boundary

Public code implements transport and integration configuration. It contains no retrieval engine, internal prompts, database access, production infrastructure, or customer fixtures. Tool descriptions and schemas returned by the service are visible to MCP clients and may be captured by directory inspection. See review scope, security, and the Dactic privacy policy.

Development

npm run verify checks official schema snapshots, manifest consistency, assets, packaging boundaries, and offline protocol integration tests against a synthetic loopback MCP server. Tests do not contact Dactic or read user credentials. CI runs the same checks and builds the non-root Docker image. No registry or marketplace publication runs in CI.

Source is licensed under MIT. Branding and the hosted service are outside that license; see NOTICE.

来源:README.md,提交 51437d0

工具

34

list_agents

List my Dactic 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_agent to run one or get_agent for its details. Returns not_found when you have none.

输入参数limitcursorenrichtarget

get_agent

Get a Dactic agent

Get one of your Dactic agents by id: its name, platform, status, complete instructions and every skill (name, description, full procedure). Never returns another user's agent.

输入参数enrichagent_id必填

load_agent

Load a Dactic agent to run here

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 skills. Follow them exactly. Returns not_ready when the agent has no approved instructions, and not_found when no agent matches; it never returns a partial agent.

输入参数agent_idagent_name

invoke_agent

Run a Dactic 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. The response always includes the agent's complete instructions and skills. Retrying the same request returns the same run instead of starting another.

输入参数inputagent_id必填idempotency_keyinclude_context

resolve_terms

Look up company terms

Look up what company-specific terms mean, from your organization's confirmed Dactic terminology only. Terms that aren't confirmed come back in gaps; never guess their meaning.

输入参数teamterms必填

check_freshness

Check if my Dactic knowledge is current

Is my Dactic knowledge up to date? Reports each surface (app sync, confirmed knowledge, terminology, approved playbook, department policies, skills, and optionally one agent) as fresh, stale, empty or unknown, with a recommendation for anything not fresh. Pass refs from other Dactic tools to check specific items. scope=org is for org admins.

输入参数refsscopeagent_id

get_policy

Get my department's policies

Get your department's current policies with their source document. With query: the policies that apply to it (all of them when your department has few, else the closest matches; coverage says which). Returns not_found when none are published.

输入参数limitquerycategorypolicy_id

list_skills

List my Dactic skills

List your active Dactic skills (procedures derived from your interviews). disclosure_tier L1 returns a catalog; L2/L3 add the procedure. Use get_skill for one skill. Returns not_found when you have none.

输入参数limitqueryapproved_onlydisclosure_tier

get_skill

Get a Dactic skill

Get one of your Dactic skills by id, with its full procedure at disclosure_tier L3.

输入参数skill_id必填disclosure_tier

ask_knowledge

Ask my Dactic knowledge

Answer a question from your own Dactic context: matches holds evidence from your synced work data and current department policies related to the question; your_profile holds your work context, skills, agents and connected apps. When nothing matches, say so; never another user's data, even for org admins.

输入参数boardlimitquestion必填source_type

get_context_bundle

Get everything a task needs

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 agent's complete instructions and skills. Without: your most relevant skills. Plus your confirmed knowledge and related evidence, your department's applicable policies with citations, confirmed meanings of company terms (unresolved terms are listed, never guessed) and a freshness summary. Fits token_budget by dropping whole lower-priority items, listed in omitted.

输入参数task必填includeagent_idtoken_budget

get_user_context

My Dactic profile

Your own Dactic profile for personalising answers: job title, team, department and role; work context (role, guardrails, preferences); confirmed knowledge (paginated; unconfirmed files are counted, not shown); connected apps; tools you use. Only ever your own data, even for org admins.

输入参数limitcursorsections

get_knowledge_provenance

Where did that come from?

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 low from a fixed rubric, with the rule that applied in `basis`. Rubric — terminology: high when confirmed with score >= 0.9 and no conflict, medium when confirmed otherwise, low when not confirmed. Knowledge: high when confirmed in an interview, medium when inferred from 3+ evidence items (or confirmed without a recorded confirmation), low otherwise or when disputed. Evidence: high when it supports confirmed knowledge, medium when it supports unconfirmed knowledge, low otherwise. Policy: high when in the current published COE version, medium when ingested from a document but not published, low with no source document. Skill: high when the owner has an approved playbook and a live agent uses it, medium when active, low when retired. Playbook: high approved, medium needs follow-up, low otherwise. Never repeats evidence text.

输入参数refs必填

submit_agent_feedback

Send feedback about my agent

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 confirmation_code: you get a preview and a confirmation_code and nothing is saved; show the preview, ask the user to confirm, then call again with the same fields plus confirmation_code. It's applied at the agent's next refresh, not immediately. Only for your own agents.

输入参数agent必填detailtargetsummary必填categoryseverityidempotency_keyconfirmation_codeexpected_behavior

check_against_policy

Check a draft against my policies

Advisory check of a draft reply, plan or action against your department's Dactic policies. Returns findings that quote the policy and the draft verbatim, with a suggested change, and says how many policies were checked. Present the result as advice: it is never a compliance sign-off. not_assessed means the check couldn't run; the applicable policies are listed instead.

输入参数kinddraft必填contextcategories

get_skill_usage

How my Dactic skills are used

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 use each, and a suggestion (promote, keep, review or trim) with its basis. Suggestions are advice and never change anything. data_since is when counting started; there is no earlier history. scope=org is for org admins.

输入参数limitscopeskill_slugnext_cursorwindow_days

synthesize_agent

Draft a new agent from my skills

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 the background: it returns a task_id at once; call get_task_status with it until the status is succeeded or failed. The result is a draft saved for review in the Dactic app — it is never deployed from here. If none of the user's skills fit, the result says not_enough_material: then offer create_agent_from_process, which builds the agent and its new skills from the process the user describes. If the user's existing agents or drafts already use every skill that fits, the result says existing_agent_covers and names them instead of drafting a duplicate; for a genuinely new process use create_agent_from_process. Confirm the task with the user before calling. target_platform claude only for now.

输入参数nameidempotency_keytarget_platformtask_description必填

create_agent_from_process

Create a new agent from a process I describe

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 skills don't cover the work (synthesize_agent only reuses existing skills). Before calling, ask the user for the purpose, the concrete steps in order, when it should run, the data sources or tools it uses and anything it must never do; then confirm the summary with them. Runs in the background: returns a task_id; call get_task_status until succeeded (with the draft and its review link) or failed. The draft is saved for review in the Dactic app and is never deployed from here. target_platform claude only; Monday agents are created in the Dactic app.

输入参数namenotessteps必填purpose必填triggerresourcesconstraintsidempotency_keytarget_platform

get_task_status

Check a Dactic background task

Status of a background task you started (e.g. synthesize_agent): queued, running, succeeded (with its result) or failed (with error_message). Only your own tasks; anything else is not_found.

输入参数task_id必填

fetch

Fetch a Dactic item

Fetch the full text of one Dactic item by the id search returned, with a link to it.

输入参数id必填

get_usage

My Dactic AI usage

Your Dactic AI usage (tokens by operation) over the last N days; scope=org is for org admins.

输入参数daysscope

get_sync_status

Get sync status

Report recorded sync progress, last successful sync, approved source coverage and safe recovery guidance. Credential linkage is not proof of current access.

无需输入参数。

get_feedback_status

Get feedback status

Follow the recorded processing/application state of your submitted agent feedback. No immediate agent change is implied.

输入参数feedback_id必填

recommend_agent

Recommend agent

Match a task to accessible deployed agents by their stored instructions and skills. Returns candidate evidence and limitations; never runs or creates an agent.

输入参数task必填limit

compare_agent_versions

Compare agent versions

Compare two stored artifact versions of the same accessible agent/platform, with instruction diffs and skill additions/removals. Missing guardrails/parameters remain unknown. Never rolls back or deploys.

输入参数agent_id必填platformto_version必填from_version必填

find_context_gaps

Find context gaps

Assess task preparation using available knowledge, skills, policies, terminology and sync readiness. Returns blocking/nonblocking gaps; unknown requirements or execution permissions remain unknown.

输入参数task必填agent_idrequired_inputsavailable_inputs

get_entity_context

Get entity context

Follow bounded explicit stored links from an approved board, accessible agent/project or confirmed term. Returns entities, relationship bases, citations and missing-link gaps. Text similarity never establishes a dependency.

输入参数limitentity_id必填entity_type必填

get_context_delta

Get context delta

Retrieve added/updated/removed permitted knowledge and published policies since an actor-bound snapshot cursor. Omit cursor for a complete bounded baseline. Expired cursors or changed scopes/permissions require a fresh baseline. Returns snapshot differences, not every intermediate change event; the host retains the cursor.

输入参数cursorinclude

retrieve_similar_cases

Retrieve similar cases

Find comparable recent runs or resolved feedback owned by you. Returns recorded summaries/outcomes, citations and uncertainty; missing actions/outcomes remain unknown. Past outcomes do not guarantee success.

输入参数limitsituation必填

query_work_dataset

Query work dataset

Query the approved synced Monday item view with supported scalar filters, sorting, grouping and count/sum aggregation. Fields are base identifiers or column:<stored-column-id>. Returns typed rows, pagination and explicit sync/coverage limits. Older syncs may lack structured columns. Never accepts SQL.

输入参数sortviewlimitcursorfieldsfiltersboard_idgroup_byaggregate

guide_workflow

Guide workflow

Build advisory human-reviewed workflow options using accessible agents, context gaps, connection/sync readiness and provisional platform capabilities. Separates source, execution-platform and host connectors. Returns next actions and conservative net human-hours estimates only from user-confirmed measurements. Missing measurements trigger a question. Never executes, connects, schedules or deploys.

输入参数goal必填agent_idwhere_stuckmanual_stepsmeasurementsrequired_inputstarget_platformavailable_inputs

recommend_new_agents

Recommend new agents

After confirming the user's goal, save a new recommendation iteration containing validated Claude draft proposals from their own skills/context. Optional priorities produce up to three proposals. A proposal never copies an agent or draft the user already has: when their skills are all in use the result is existing_agent_covers with those agents. Returns a background task; poll get_task_status. Monday synthesis is unavailable in this path. Never deploys.

输入参数goal必填prioritiesidempotency_keytarget_platformprevious_iteration_id

deploy_agent

Deploy agent

First deployment of your selected saved draft. Call without confirmation_code for a preview, show it to the user and obtain authorization, then repeat the same definition/target with the code. Checks skills, source approvals and existing deploy readiness/billing gates. Returns a background task; poll get_task_status for deployment state. An existing deployment is identified without redeploying.

输入参数definition_id必填target_workspace必填confirmation_code

版本历史

1
  1. v0.1.2最新Nov 4, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分