Provides read-only access to Confluence Cloud, allowing users to read pages and search using CQL.
Provides read-only access to Jira Cloud, allowing users to retrieve issues and search using JQL.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@atlassian-readonly Search Jira for open bugs assigned to me and summarize them."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A small MCP server that gives AI assistants read-only access to Jira and Confluence Cloud. Read-only access is enforced in two layers:
The Atlassian tokens contain only read scopes.
The server implements only allowlisted GET requests.
It exposes four tools:
Read a Jira issue
Search Jira with JQL
Read a Confluence page
Search Confluence with CQL
There is no generic HTTP tool and no POST, PUT, PATCH, or DELETE implementation. Even if broader credentials were accidentally supplied, MCP clients would still have no tool for changing Jira or Confluence content. Responses are bounded, likely secrets are redacted, Confluence storage HTML is converted to Markdown, and optional JMESPath projections can reduce returned data.
Node.js 20 or newer
Access to the configured Atlassian Cloud tenant
Separate scoped API tokens for Jira and Confluence
An MCP client such as GitHub Copilot in 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 testOpen Atlassian API tokens and create two tokens.
Jira token
read:jira-workThis classic scope alone is verified for issue retrieval and JQL search.
Atlassian rejected Jira tokens containing only the equivalent granular scopes
with 401 Unauthorized; scope does not match.
Confluence token
read:page:confluence
read:content-details:confluence
search:confluenceThese granular scopes are verified for CQL search and full page retrieval.
Scopes are fixed when a token is created. Jira and Confluence require separate tokens. Copy each token from its one-time creation dialog and do not place it in source files, MCP configuration, shell history, issues, or chat.
Do not add write or administration scopes. The restricted tokens ensure Atlassian rejects write operations independently of the MCP implementation.
From the repository directory:
npm run configure -- jira
npm run configure -- confluenceThe hidden prompts save each token separately in Windows Credential Manager, macOS Keychain, or Linux Secret Service. The Atlassian account email belongs in the MCP environment, not the credential store.
Run MCP: Open User Configuration from the Command Palette:
{
"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"
}
}
}
}Reload VS Code, open Copilot Chat, select Configure Tools, and enable the four Atlassian tools.
Example prompts:
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.mjsRestart Copilot CLI after adding or changing the server.
Check that:
The email matches the Atlassian account that created the tokens.
The correct product token was stored.
The token is current and the account can access the requested content.
Jira uses read:jira-work, not only granular Jira scopes.
Confluence has all three scopes listed above.
The MCP host was restarted after replacing a token.
Scoped tokens must use Atlassian's product gateways:
https://api.atlassian.com/ex/jira/{cloudId}
https://api.atlassian.com/ex/confluence/{cloudId}This server uses the fixed tenant Cloud ID in src/atlassian.mjs.
TLS-inspection products such as Zscaler re-sign HTTPS traffic with a corporate certificate authority. Windows may trust that authority while Node.js still uses its bundled CA list. On supported Node.js versions, keep this in the MCP environment:
NODE_OPTIONS=--use-system-caIf necessary, export the non-expired corporate CA as Base-64 PEM and set
NODE_EXTRA_CA_CERTS to its absolute path. Never disable TLS verification.
WSL has a separate Linux trust store. Export the applicable corporate root and
intermediate certificates from Windows, save them with .crt extensions, copy
them to /usr/local/share/ca-certificates/, then run:
sudo update-ca-certificatesRestart WSL before retrying curl, Docker, Node.js, or other HTTPS clients.
If no desktop keyring is available, use restricted token files:
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 tokenConfigure these variables in the MCP environment:
ATLASSIAN_USER_EMAIL
ATLASSIAN_JIRA_TOKEN_FILE
ATLASSIAN_CONFLUENCE_TOKEN_FILEATLASSIAN_JIRA_API_TOKEN and ATLASSIAN_CONFLUENCE_API_TOKEN are supported
for process-scoped CI use, but should not be persisted in desktop
configuration.
Replace stored tokens:
npm run configure -- jira
npm run configure -- confluenceDelete stored tokens:
npm run configure -- jira delete
npm run configure -- confluence deleteLocal deletion does not revoke a token. Revoke it separately from Atlassian's token-management page.
This project uses defense in depth:
Token enforcement: the documented tokens contain only Atlassian read scopes, so Atlassian does not authorize writes.
Implementation enforcement: only four narrow read tools are exposed. Their URLs and HTTP method are fixed; callers cannot choose another host, endpoint, or method.
Response controls: responses are size-limited, likely secrets are redacted, and projections can minimize returned data.
Tokens still inherit the creator's visibility: the MCP can read only content that account can already access. Supplying a broader token weakens the token layer but does not add write operations to this server.
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. Dates show when Glama detected each change.
v0.1.0confluence_get_pageconfluence_searchjira_get_issuejira_search_issuesEach 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.
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