Небольшой MCP-сервер, который предоставляет ИИ-ассистентам доступ только для чтения к Jira и Confluence Cloud. Доступ только для чтения обеспечивается на двух уровнях:
Токены Atlassian содержат только области действия для чтения.
Сервер реализует только авторизованные GET-запросы.
Он предоставляет четыре инструмента:
Прочитать задачу Jira
Поиск в Jira с помощью JQL
Прочитать страницу Confluence
Поиск в Confluence с помощью CQL
Здесь нет универсального HTTP-инструмента и нет реализации POST, PUT, PATCH или DELETE. Даже если будут случайно переданы более широкие учётные данные, клиенты MCP всё равно не будут иметь инструмента для изменения контента Jira или Confluence. Ответы ограничены по размеру, вероятные секреты маскируются, HTML-код из хранилища Confluence конвертируется в Markdown, а опциональные JMESPath-проекции могут сократить объём возвращаемых данных.
Node.js 20 или новее
Доступ к настроенному облачному тенанту Atlassian
Отдельные ограниченные API-токены для Jira и Confluence
MCP-клиент, такой как GitHub Copilot в VS Code
Related MCP server: MCP Atlassian Server
git clone https://github.com/AlexSchaap-TMMC/atlassian-readonly-mcp.git C:\Tools\atlassian-readonly
Set-Location C:\Tools\atlassian-readonly
npm install
npm testОткройте API-токены Atlassian и создайте два токена.
Jira-токен
read:jira-workТолько классической области достаточно для получения задачи и поиска по JQL. Atlassian отклонил Jira-токены, содержащие только эквивалентные детализированные области, с ответом 401 Unauthorized; scope does not match.
Confluence-токен
read:page:confluence
read:content-details:confluence
search:confluenceЭти детализированные области проверены для поиска по CQL и полного чтения страниц.
Области действия фиксируются при создании токена. Jira и Confluence требуют отдельных токенов. Скопируйте каждый токен из однократного диалога создания и не помещайте его в исходные файлы, конфигурацию MCP, историю командной оболочки, задачи или чат.
Не добавляйте области на запись или администрирование. Ограниченные токены гарантируют, что Atlassian отклоняет операции записи независимо от реализации MCP.
Из каталога репозитория:
npm run configure -- jira
npm run configure -- confluenceСкрытые запросы ввода сохраняют каждый токен отдельно в Windows Credential Manager, macOS Keychain или Linux Secret Service. Электронная почта учётной записи Atlassian должна находиться в окружении MCP, а не в хранилище учётных данных.
Выполните MCP: Open User Configuration из палитры команд:
{
"servers": {
"atlassian-readonly": {
"type": "stdio",
"command": "node",
"args": ["C:\\Tools\\atlassian-readonly\\src\\server.mjs"],
"env": {
"ATLASSIAN_USER_EMAIL": "your.atlassian.email@example.com",
"NODE_OPTIONS": "--use-system-ca"
}
}
}
}Обновите VS Code, откройте Copilot Chat, выберите Configure Tools и включите четыре инструмента Atlassian.
Примеры запросов:
Read HEC-123 and summarize its acceptance criteria.
Search Jira for open bugs assigned to me.
Search Confluence for pages about Kafka retry handling.copilot mcp add atlassian-readonly `
--env ATLASSIAN_USER_EMAIL="your.atlassian.email@example.com" `
--env NODE_OPTIONS="--use-system-ca" `
-- node C:\Tools\atlassian-readonly\src\server.mjsПерезапустите Copilot CLI после добавления или изменения сервера.
Проверьте, что:
Адрес электронной почты соответствует учётной записи Atlassian, создавшей токены.
Были сохранён правильный токен продукта.
Токен актуален, а учётная запись может получить доступ к запрашиваемому контенту.
Jira использует read:jira-work, а не только детализированные области Jira.
У Confluence есть все три указанных выше области действия.
Хост перезапу ал.
Токены с ограничениями должны использовать продуктовые шлюзы Atlassian:
https://api.atlassian.com/ex/jira/{cloudId}
https://api.atlassian.com/ex/confluence/{cloudId}Этот сервер использует фиксированный Cloud ID тенанта в src/atlassian.mjs.
Продукты для проверки TLS, такие как Zscale, повторно подписывают HTTPS-трафик с помощью корпоративного удостоверяющего центра. Windows может доверять этому центру, в то время как Node.js по-прежнему использует встроенный список CA. В поддерживаемых версиях Node.js добавьте это в окружение MCP:
NODE_OPTIONS=--use-system-caЕсли требуется, экспортируйте непростроченный корпоративный CA в формате Base64‑PEM и укажите в NODE_EXTRA_CA_CERTS его абсолютный путь. Никогда не отключение проверку TLS.
WSL имеет отдельное двоение Linux. Экспортируйте из Windows корневые и промежуточные сертификаты, сохраните их с расширением .crt, скопируйте в /usr/local/share/ca-certificates/, затем выполните:
sudo update-ca-certificatesПерезапустите WSL перед повторной попыткой curl, Docker, Node.js или других HTTPS‑клиентов.
Если keyring рабочего стола недоступен, используйте файлы токенов с ограниченными правами:
mkdir -p ~/.config
install -m 600 /dev/null ~/.config/atlassian-jira-token
install -m 600 /dev/null ~/.config/atlassian-confluence-token
read -rsp "Jira API token: " token; echo
printf '%s' "$token" > ~/.config/atlassian-jira-token
read -rsp "Confluence API token: " token; echo
printf '%s' "$token" > ~/.config/atlassian-confluence-token
unset tokenНастройтете эти переменными в окружении MCP:
ATLASSIAN_USER_EMAIL
ATLASSIAN_JIRA_TOKEN_FILE
ATLASSIAN_CONFLUENCE_TOKEN_FILEATLASSIAN_JIRA_API_TOKEN и ATLASSIAN_CONFLUENCE_API_TOKEN поддерживаются для процесса в CI, но не должны сохраняться на рабочих станциях.
Замените сохранённые токены:
npm run configure -- jira
npm run configure -- confluenceУдалите сохранённые токены:
npm run configure -- jira delete
npm run configure -- confluence deleteЛокальное удаление не отзываетя токенек. Отзовите его отдельно через страницу управления токеном.
Проект использует защиту на нескольких уровнях:
Ограничение токена: документированные токены имеют только чтение-области Azure, поэтому Atlassian не авторизует записы.
Ограничение реализации: открыт только четыре узких инструмента чтения. Их URL и HTTP-метод фиксированы; вызывающие не может выбирать другой хост, endpoint or method.
Контроль ответов: ответы размерно ограничены, вероятные секреты маскируются, а проекции могут уменьшать отдача.
Токены по-прежнему получают уровень видимости создателя: сервер MCP может читать только те данные, которые пользователь уже имеет право читать. Подключение более широкого токена ослабляет слой токена, но не добавляет операции записи данному серверу.
Read a Confluence page by ID and convert storage HTML to Markdown. This server has no write operations.
| Name | Required | Description | Default |
|---|---|---|---|
| page_id | Yes | ||
| projection | No | Optional JMESPath projection to reduce returned fields. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It discloses the conversion behavior (storage HTML to Markdown) and states the server has no write operations, which is helpful. However, it does not mention error handling, response format, or any limits, leaving gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The main purpose is front-loaded, and the read-only clarification is concise and useful. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with only two parameters and no output schema, the description covers the core purpose and behavioral nuance. It does not explain return structure or error scenarios, but these are less critical given the tool's simplicity. Overall, it is sufficiently complete for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%: projection has a description, but page_id does not. The description does not compensate for the missing page_id semantics beyond reiterating 'by ID'. It adds nothing about projection beyond the schema, so minimal value is provided for parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Read') and a specific resource ('Confluence page by ID'), plus a distinctive detail (conversion to Markdown). This distinguishes it from sibling tools like confluence_search and jira_get_issue without needing to inspect the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies usage when a page ID is known, but it does not explicitly contrast with confluence_search or state when NOT to use this tool. The read-only note provides general context, but no alternative routing guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Confluence with CQL. Results are bounded; this server has no write operations.
| Name | Required | Description | Default |
|---|---|---|---|
| cql | Yes | ||
| limit | No | ||
| projection | No | Optional JMESPath projection to reduce returned fields. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It adds useful context by stating 'Results are bounded' and 'this server has no write operations,' which helps the agent understand output scaling and that the tool performs no mutations. It does not detail result shape or error behavior, but for a simple search tool this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and contains no filler. The safety note about no write operations is brief and earns its place given the lack of annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a three-parameter search tool, the description plus schema covers the basic calling contract, but there is no output schema and the description does not mention what the search returns, pagination behavior, or any example CQL. 'Results are bounded' hints at limits but leaves practical expectations underspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33% because only projection has a description. The main description mentions CQL, which gives some meaning to the cql parameter, but it does not explain how to construct CQL, what limit controls beyond its schema constraints, or how projection interacts with results. The description does not sufficiently compensate for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action and resource: 'Search Confluence with CQL.' This clearly separates it from sibling tools like jira_search_issues and confluence_get_page, since the resource (Confluence) and operation (search) are both explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool should be used for searching Confluence, but it does not explicitly say when to choose it over siblings such as confluence_get_page or jira_search_issues. There is no when-not-to-use guidance or mention of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read one Jira issue. This server has no write operations.
| Name | Required | Description | Default |
|---|---|---|---|
| issue_key | Yes | ||
| projection | No | Optional JMESPath projection to reduce returned fields. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does mention the read-only nature and that the server has no write operations, which is useful context. However, it does not describe error behavior, response format, or what happens when the issue does not exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, both earning their place. It front-loads the core action and adds the server-wide write constraint without any redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool, the inputs and general action are covered, but the absence of an output schema means the return value is only implied. The description does not clarify what fields are returned or how to interpret the response, leaving a moderate gap for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides a pattern for issue_key and a description for projection, but the tool description itself adds no parameter-level explanation. With schema coverage at 50%, the description does not compensate for the undocumented issue_key semantics, though the parameter name is fairly self-explanatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('read') and resource ('one Jira issue'), making the tool's purpose unmistakable. It is clearly differentiated from sibling search and Confluence tools by focusing on a single issue retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific issue key is available, but it does not explicitly mention jira_search_issues as the alternative for query-based retrieval. There is no warning against using it for searches or a clear when-not-to-use statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Jira with JQL. Results and fields are bounded; this server has no write operations.
| Name | Required | Description | Default |
|---|---|---|---|
| jql | Yes | ||
| limit | No | ||
| projection | No | Optional JMESPath projection to reduce returned fields. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It adds two useful traits: 'Results and fields are bounded' and 'this server has no write operations', which are not present in the schema. However, it omits details like pagination, ordering, rate limits, or error behavior, so transparency is partial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tight sentence that front-loads the core action and immediately adds a scope limitation. No filler words or redundant repetition of the tool name. Every phrase contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters, no output schema, and no annotations, the description is not sufficient. It does not describe what a search result looks like, how to construct a valid JQL query, or the effect of 'limit' and 'projection'. The bounded/no-write note is useful but only addresses safety, not invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only 33% of the parameters have schema descriptions (projection). The description clarifies that 'jql' is the Jira Query Language string, but it does not explain the 'limit' parameter or add syntax/format details. Since schema coverage is low, the description should compensate more but does not for two of the three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Search') and resource ('Jira'), and correctly implies searching issues via the tool name and JQL mention. It distinguishes from jira_get_issue (which fetches a single issue) and confluence_search (different product), though it does not explicitly say 'issues' or contrast these siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description does not mention jira_get_issue, confluence_search, or any condition that would select one over the other. The only usage hint is 'Search Jira with JQL', which is implied functionality rather than a directive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0confluence_get_pageconfluence_searchjira_get_issuejira_search_issuesScored across 4 tools
Each tool has a clearly distinct purpose: get versus search for both Jira and Confluence. There is no overlap or ambiguity between any pair, enabling precise tool selection.
All tool names follow a consistent product_action pattern with snake_case (e.g., jira_get_issue, confluence_search). The naming is uniform and predictable.
With 4 tools covering two products (Jira and Confluence) each having a get and search operation, the count is well-scoped for a read-only server. No tool feels redundant or missing.
The read-only surface provides essential get and search for both products. Minor gaps exist such as bulk fetch or additional metadata endpoints, but core querying needs are well covered.
Connect to Atlassian Jira, Confluence, Loom, and more to search, create, and manage your work.
Safe, read-only Postgres and MySQL access for AI agents. Audit log + column-level controls.
Secure AI access to OpenOak tasks, notes, and Kanban boards.
Connect AI to store orders, products and inventory with scoped access and human approvals.
