AI 어시스턴트에게 Jira 및 Confluence Cloud에 대한 읽기 전용 액세스를 제공하는 소형 MCP 서버입니다. 읽기 전용 액세스는 두 계층에서 강제됩니다:
Atlassian 토큰에는 읽기 범위(read scope)만 포함됩니다.
서버는 허용 목록(allowlist) 방식의 GET 요청만 구현합니다.
다음 네 가지 도구를 제공합니다:
Jira 이슈 읽기
JQL로 Jira 검색
Confluence 페이지 읽기
CQL로 Confluence 검색
일반적인 HTTP 도구는 없으며 POST, PUT, PATCH, DELETE 구현도 존재하지 않습니다. 더 넓은 범위의 자격 증명이 실수로 제공되더라도 MCP 클라이언트에는 Jira 또는 Confluence 콘텐츠를 변경할 수 있는 도구가 없습니다. 응답 크기는 제한되고, 비밀 정보로 보이는 값은 삭제되며, Confluence storage HTML은 Markdown으로 변환되고, 선택적 JMESPath 프로젝션(projection)을 사용하여 반환 데이터를 줄일 수 있습니다.
Node.js 20 이상
구성된 Atlassian Cloud 테넌트에 액세스할 수 있어야 함
Jira와 Confluence에 대한 별도의 범위 지정 API 토큰
VS Code의 GitHub Copilot과 같은 MCP 클라이언트
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 testAtlassian API tokens 페이지를 열고 토큰 두 개를 만듭니다.
Jira 토큰
read:jira-work이 클래식(classic) 범위 하나만으로도 이슈 조회와 JQL 검색이 검증되었습니다. Atlassian은 동일한 기능의 세분화된(granular) 범위만 포함한 Jira 토큰을 401 Unauthorized; scope does not match 오류로 거부했습니다.
Confluence 토큰
read:page:confluence
read:content-details:confluence
search:confluence이 세분화된 범위들은 CQL 검색 및 전체 페이지 조회에 대해 검증되었습니다.
범위(scope)는 토큰 생성 시 고정됩니다. Jira와 Confluence에는 각각 별도의 토큰이 필요합니다. 각 토큰은 최초 생성 대화상자에서 복사하되, 소스 파일, MCP 구성, 셸 히스토리, 이슈, 채팅 등에 넣지 마세요.
쓰기 또는 관리 범위(admin scope)를 추가하지 마십시오. 제한된 토큰을 사용하면 MCP 구현과 무관하게 Atlassian이 쓰기 작업을 거부합니다.
저장소 디렉터리에서 실행하세요:
npm run configure -- jira
npm run configure -- confluence숨겨진 프롬프트는 각 토큰을 Windows Credential Manager, macOS Keychain, 또는 Linux Secret Service에 개별적으로 저장합니다. Atlassian 계정 이메일은 자격 증명 저장소가 아니라 MCP 환경에 있어야 합니다.
명령 팔레트에서 MCP: Open User Configuration을 실행하세요:
{
"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"
}
}
}
}VS Code를 다시 로드하고, Copilot Chat을 연 다음 Configure Tools를 선택하고 네 가지 Atlassian 도구를 활성화하세요.
예시 프롬프트:
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.mjs서버를 추가하거나 변경한 후에는 Copilot CLI를 다시 시작하세요.
다음을 확인하세요:
이메일이 토큰을 생성한 Atlassian 계정과 일치하는지.
올바른 제품 토큰이 저장되었는지.
토큰이 유효하고 해당 계정이 요청한 콘텐츠에 액세스할 수 있는지.
Jira에 세분화된 범위만이 아니라 read:jira-work가 포함되었는지.
Confluence에 위에 나열된 세 가지 범위가 모두 포함되었는지.
토큰을 교체한 후 MCP 호스트를 다시 시작했는지.
범위가 지정된 토큰은 Atlassian의 제품 게이트웨이를 사용해야 합니다:
https://api.atlassian.com/ex/jira/{cloudId}
https://api.atlassian.com/ex/confluence/{cloudId}이 서버는 src/atlassian.mjs에 있는 고정된 테넌트 Cloud ID를 사용합니다.
Zscaler와 같은 TLS 검사 제품은 기업 인증 기관(CA)으로 HTTPS 트래픽을 다시 서명합니다. Windows는 해당 인증 기관을 신뢰할 수 있지만 Node.js는 자체 번들 CA 목록을 사용합니다. 지원되는 Node.js 버전에서는 이 설정을 MCP 환경에 유지하세요:
NODE_OPTIONS=--use-system-ca필요한 경우 만료되지 않은 기업 CA를 Base-64 PEM으로 내보내고 NODE_EXTRA_CA_CERTS에 절대 경로를 설정하십시오. TLS 검증을 절대 비활성화하지 마십시오.
WSL은 별도의 Linux 신뢰 저장소를 사용합니다. 관련 기업 루트 및 중간 인증서를 Windows에서 내보내고 .crt 확장자로 저장한 후 /usr/local/share/ca-certificates/에 복사한 다음 실행하세요:
sudo update-ca-certificatescurl, Docker, Node.js 또는 기타 HTTPS 클라이언트를 다시 시도하기 전에 WSL을 다시 시작하세요.
데스크톱 키링(keyring)이 없는 환경에서는 제한된 토큰 파일을 사용하세요:
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 tokenMCP 환경에 다음 변수를 구성하세요:
ATLASSIAN_USER_EMAIL
ATLASSIAN_JIRA_TOKEN_FILE
ATLASSIAN_CONFLUENCE_TOKEN_FILEATLASSIAN_JIRA_API_TOKEN 및 ATLASSIAN_CONFLUENCE_API_TOKEN은 프로세스 범위의 CI 사용에는 지원되지만, 데스크톱 구성에 유지해서는 안 됩니다.
저장된 토큰 교체:
npm run configure -- jira
npm run configure -- confluence저장된 토큰 삭제:
npm run configure -- jira delete
npm run configure -- confluence delete로컬 삭제는 토큰을 해지하지 않습니다. Atlassian의 토큰 관리 페이지에서 별도로 해지하세요.
이 프로젝트는 방어를 위한 심층 방어(defense in depth)를 사용합니다:
토큰 구속: 문서화된 토큰에는 Atlassian 읽기 범위만 포함되어 있기 때문에 Atlassian이 쓰기를 승인하지 않습니다.
구현 구속: 읽기 전용 도구 네 개만 노출되며, 각 도구의 URL이나 HTTP 메서드는 고정되어 있어 호출자가 다른 호스트, 엔드포인트 또는 메서드를 선택할 수 없습니다.
응답 구속: 응답 크기에 제한이 있고 비밀 정보로 보이는 값이 삭제되며 프로젝션을 통해 반환 데이터를 최소화할 수 있습니다.
토큰은 여전히 생성자의 가시 범위를 상속받습니다. 즉, MCP는 해당 계정이 이미 액세스할 수 있는 콘텐츠만 읽습니다. 더 넓은 범위의 토큰을 제공하면 토큰 계층이 약화되지만 이 서버에 쓰기 작업이 추가되지는 않습니다.
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.
