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petrsovadina

MCP Atlassian Server

by petrsovadina
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: one searches Confluence content using CQL, and the other searches Jira issues using JQL. There is no overlap in purpose or target resource, making misselection highly unlikely.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern: [product]_[action] (confluence_search, jira_search). This verb_noun style is uniform and predictable across the set.

    Tool Count2/5

    With only two tools, the server feels thin for covering Atlassian's ecosystem. While Confluence and Jira are major products, the lack of additional operations (e.g., create, update, delete) or coverage of other Atlassian tools (e.g., Bitbucket, Trello) makes the scope appear incomplete.

    Completeness2/5

    The server only provides search functionality for two Atlassian products, missing essential CRUD operations (e.g., create pages in Confluence, update issues in Jira) and other lifecycle actions. This creates significant gaps that will limit agent workflows and likely cause failures in broader tasks.

  • Average 2.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It mentions 'CQL' (Confluence Query Language) but doesn't disclose behavioral traits like pagination, rate limits, error handling, or what happens with invalid queries. The description is minimal and lacks critical operational context for a search tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence with zero waste, making it appropriately sized and front-loaded. However, it's overly concise to the point of under-specification, lacking necessary details for effective use, which slightly reduces its score from perfect.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations, no output schema, and a search tool with potential complexity (CQL queries), the description is incomplete. It doesn't explain return values, error cases, or behavioral nuances, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, with clear documentation for 'query' (CQL query string) and 'limit' (results limit 1-50). The description adds no additional parameter semantics beyond what the schema provides, such as CQL syntax examples or default behaviors. Baseline 3 is appropriate as the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Search Confluence content using CQL' clearly states the action (search) and resource (Confluence content), but it's vague about what 'content' specifically includes (pages, blogs, spaces, etc.) and doesn't distinguish from sibling tool 'jira_search'. It provides a basic purpose but lacks specificity about scope.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives. The description doesn't mention the sibling 'jira_search' tool or any other search methods, nor does it specify prerequisites like authentication or appropriate contexts for CQL queries. Usage is implied through the name but not explicitly stated.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the basic function but doesn't describe important behavioral aspects like authentication requirements, rate limits, pagination behavior (beyond the 'limit' parameter), error handling, or what format/search capabilities JQL provides. For a search tool with no annotation coverage, this leaves significant gaps.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a search tool and front-loads the essential information. Every word earns its place by specifying what, where, and how.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations and no output schema, the description is incomplete for a search tool with 3 parameters. It doesn't explain what the search returns (issue objects, summaries, or full details), how results are structured, or any behavioral constraints. For a tool that presumably returns complex Jira issue data, more context about output format would be helpful.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description mentions JQL but doesn't add any parameter-specific context beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description could have explained JQL syntax or field selection patterns.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Search Jira issues using JQL' clearly states the action (search), resource (Jira issues), and method (JQL). It distinguishes from the sibling tool 'confluence_search' by specifying Jira issues rather than Confluence content. However, it doesn't specify what kind of search results are returned or the scope beyond 'issues'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'confluence_search' tool or any other Jira tools that might exist. There's no indication of prerequisites, limitations, or typical use cases beyond the basic function.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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