LinkIntel

Evidence for your AI's next X post.

От сообществаРаботаетБез входаГлобальныйБесплатноМожет изменять данные

Что умеет

  • Get Data Status: Report LinkIntel readiness per capability: metric freshness, registered and measured post counts, connected corpus sources, owned-X connection status, LinkedIn connection status, rema
  • Prepare Content Brief: Prepare a stored-data writing brief for a topic and an explicitly named public X reference cohort. Costs nothing and never fetches: it reads references already stored by add_ref
  • List References: List the stored reference topics and cohorts for this account, with which platform and how many posts each holds. Call this before prepare_content_brief so the topic and cohort are th

Какие данные видит

Нужен ли аккаунт

Не нужен: сервер работает без входа

Evidence for your AI's next X post. Connect X and LinkIntel reads your recent original posts and their metrics daily, tracks X's open-source ranking weights, and stores public X posts you pick as references for a topic. Your assistant gets it all through prepare_content_brief as one cited content brief, with the limits of the evidence stated plainly. It never publishes, never schedules and never searches X on its own. OAuth sign-in, $39/month.

Как подключить

Как подключить LinkIntel к Claude

Адрес сервера, который нужно вставить в настройках Claude:

https://linkintel--marcos.run.tools

  1. Откройте Claude (сайт claude.ai или приложение для компьютера).
  2. Перейдите в Настройки → Коннекторы (в английском интерфейсе: Settings → Connectors).
  3. Нажмите Добавить пользовательский коннектор (Add custom connector).
  4. Вставьте адрес сервера, скопированный ниже, в поле URL удалённого MCP-сервера и нажмите Добавить.
  5. В чате нажмите + → Коннекторы и включите новый коннектор.

Пользовательские коннекторы доступны на тарифах Free, Pro, Max, Team и Enterprise. На Free можно добавить только один. В Team и Enterprise коннектор сначала добавляет владелец организации в Настройки организации → Коннекторы.

Авторизация

После нажатия Добавить откроется окно входа в сервис. Войдите под своим аккаунтом и подтвердите доступ. Пароль Claude не видит.

Как подключить LinkIntel к ChatGPT

Адрес сервера, который нужно вставить в настройках ChatGPT:

https://linkintel--marcos.run.tools

  1. Откройте ChatGPT в браузере (chatgpt.com). Нужен тариф Plus, Pro, Business, Enterprise или Edu.
  2. Один раз включите режим разработчика: Настройки → Приложения → Дополнительные настройки → Режим разработчика (Settings → Apps → Advanced settings → Developer mode).
  3. Откройте Настройки → Коннекторы и нажмите Создать (Create).
  4. Заполните форму: Название (любое), Описание (одна фраза, что делает сервис), URL MCP-сервера (скопируйте ниже).
  5. В поле Аутентификация выберите OAuth, если сервис требует вход, иначе Нет. Нажмите Создать.
  6. В новом чате откройте + → Приложения/Коннекторы и включите коннектор.

Название раздела у OpenAI менялось (Connectors → Apps/Plugins). Если пункт называется иначе, ищите «режим разработчика» в настройках. В рабочих пространствах Business/Enterprise администратор должен разрешить пользовательские коннекторы.

Авторизация

При первом использовании ChatGPT откроет окно входа в сервис. Войдите и подтвердите доступ.

Как подключить LinkIntel к Cursor

Адрес сервера, который нужно вставить в настройках Cursor:

https://linkintel--marcos.run.tools

Быстрый способ: нажмите кнопку Открыть в Cursor ниже, Cursor сам предложит добавить сервер.

Вручную:

  1. В Cursor откройте Settings → Cursor Settings → MCP и нажмите Add new global MCP server.
  2. Вставьте JSON, скопированный ниже, в открывшийся файл ~/.cursor/mcp.json (для одного проекта: .cursor/mcp.json в корне проекта).
  3. Сохраните файл. Сервер появится в списке MCP; при необходимости пройдите авторизацию по кнопке рядом с ним.

Авторизация

Если сервис требует вход, рядом с сервером в списке MCP появится кнопка авторизации.

Список инструментов сервера (8)

Технические названия из tools/list. Нужны только разработчикам.

get_data_statusReport LinkIntel readiness per capability: metric freshness, registered and measured post counts, connected corpus sources, owned-X connection status, LinkedIn connection status, remaining reference lookups, token expiry and today's API budget. The top-level connected field reports MCP connection status. Use linkedin_connected for LinkedIn readiness and the ready object to check specific capabilities. Call this before the first analysis and whenever a number looks wrong or surprisingly low.
prepare_content_briefPrepare a stored-data writing brief for a topic and an explicitly named public X reference cohort. Costs nothing and never fetches: it reads references already stored by add_references, so call list_references first to use the exact stored labels. platform picks whose owned evidence the brief reads, because a post's length and structure do not transfer between platforms: "x" (the default) reads the creator's most recent stored X posts, which are not filtered to the topic, so the response asks the agent to read the excerpts and judge relevance itself; "linkedin" reads the creator's historical LinkedIn posts already tagged with this topic. The reference cohort is always public X posts, and the response says so plainly when platform is "linkedin" so that evidence is never mistaken for a same-platform comparison. Neither a LinkedIn connection nor a live platform fetch is required for cached references. Never searches X or predicts reach.
list_referencesList the stored reference topics and cohorts for this account, with which platform and how many posts each holds. Call this before prepare_content_brief so the topic and cohort are the exact stored labels rather than a guess, before add_references so a near-duplicate label is not created, and before remove_references (with include_posts) to see exactly which posts a removal would affect. Reads stored data only and costs nothing.
find_account_postsLook up a public X account's most recent original posts (replies and reposts excluded) and return their URLs, dates and short previews for the user to review. This draws on the same daily read budget add_references uses, and stores nothing: it never writes a reference, so it can be called freely to explore before anything is added. Only the "x" platform is supported today.
add_referencesStore public reference posts as evidence under a topic and a named comparison cohort, so prepare_content_brief can use them. Only the "x" platform is supported for reference import today. Reads only the URLs supplied. It never searches the platform, connects an account, collects the user's own analytics, or publishes anything.
remove_referencesRemove stored reference posts: specific posts by URL, or a whole topic and cohort group. Removing shapes every brief prepare_content_brief builds from what remains, so confirm the exact posts or group with the user first; call list_references with include_posts to show exactly what is stored before removing any of it. Prefer removing specific posts over a whole group where the user only meant to drop a few. Supply urls (optionally narrowed by topic and/or cohort) to remove those specific posts, or topic and cohort together with no urls to remove a whole group. It never searches or reaches any platform: it only deletes rows already stored by add_references.
get_owned_performanceThe tool for questions about the user's own X posts: 'how did my posts do', 'what was my best post', 'which posts got the most engagement'. Returns, per post, impressions, likes, replies, reposts, quotes and bookmarks, plus the owner's non-public and organic metrics where available, read from stored, profile-scoped snapshots of the connected account. Covers recent original posts only, not replies or reposts. Use sort_by to rank the returned posts by recency, impressions, total engagement, or engagement rate. It never searches the platform, imports posts, makes a live platform request, or predicts reach.
get_platform_signalsReturn X's published default ranking weights (what helps and what hurts, each with the commit link), the material weight changes from the last 14 days, and how to use them when drafting or reviewing a post. Reads stored data only and costs nothing: it never fetches X or GitHub live, and the values are defaults mirrored from X's open-source repo, synced daily. It describes what X's published ranking defaults reward in aggregate, not a reach prediction for any post. Use the signals as a final check that never overrides the evidence or the creator's voice. Only "x" has a readable source; other platforms return available: false.