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inceon

Bitbucket MCP Server

by inceon
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct aspect of pull requests: commenting, metadata retrieval, and diff retrieval. No functional overlap, making selection clear.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern (add_*, get_*, get_*), which is predictable and easy to follow.

    Tool Count3/5

    Three tools is minimal for a Bitbucket server; it covers only basic pull request read operations and commenting, missing core CRUD operations, but it is not extreme.

    Completeness2/5

    The toolset lacks essential operations like listing, creating, updating, or merging pull requests. Agents cannot perform a full workflow, leaving significant gaps.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 14 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
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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

  • Behavior3/5

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

    No annotations provided. Description declares write operation and environment variable requirement, but omits details on side effects, permissions, rate limits, or comment management (editing/deletion).

    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?

    Extremely concise: one sentence plus a note on prerequisites. No unnecessary information.

    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?

    Missing explanation of return values (no output schema), no guidance on combining file_path, line, line_type, line_side for inline comments. Given 9 parameters and write nature, more context needed.

    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%; each parameter is documented in the schema. The description adds no extra semantic value beyond summarizing the tool's purpose.

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

    Purpose5/5

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

    Clearly states it creates a comment on a Bitbucket pull request, specifying three types: general, file, or inline line comment. Different from sibling read-only tools.

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

    Usage Guidelines4/5

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

    Explicitly identifies it as a write operation requiring BITBUCKET_ENABLE_WRITE_TOOLS=true. Does not explicitly state when to use versus siblings, but the read-only nature of siblings implies the writing use case.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It describes what data is retrieved but does not mention any behavioral traits such as authentication requirements, rate limits, or potential performance implications. For a simple read operation, this is adequate but not rich.

    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 sentence that efficiently lists the retrieved data without any fluff. Every phrase adds value, and the structure is front-loaded with the verb and resource.

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

    Completeness4/5

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

    Given no output schema, the description does a good job of listing the expected return fields (metadata, description, state, author, reviewers, branches, timestamps, links). It is fairly comprehensive, though it could be improved by noting if any fields are nested or if there are pagination details.

    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 coverage is 100%, meaning all parameters are described in the schema. The description adds no extra meaning beyond the schema; it simply restates that the tool retrieves for a specific pull request. Baseline score of 3 is appropriate.

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

    Purpose5/5

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

    The description uses the specific verb 'Retrieves' and clearly lists the resource ('metadata, description, state/status, author, reviewers, source branch, destination branch, timestamps, and links for a specific Bitbucket pull request'). It distinguishes from sibling tools (add_pull_request_comment and get_pull_request_diff) by focusing on retrieval of metadata, not adding comments or getting diffs.

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

    Usage Guidelines3/5

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

    The description does not explicitly state when to use this tool vs alternatives. While the purpose is clear, there is no guidance on when not to use it or which sibling tool to choose for other tasks. The agent must rely on tool names and context, which is adequate but not explicit.

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

  • Behavior3/5

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

    No annotations provided. Description mentions return content (raw diff and structured files) but does not discuss side effects, authentication needs, or limitations such as file size constraints.

    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?

    Single sentence that is direct and waste-free, conveying the essential information efficiently.

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

    Completeness4/5

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

    For a retrieval tool with three fully described parameters and no output schema, the description adequately states what is returned. Slightly incomplete in not mentioning that diff may be large or truncated.

    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 covers all three parameters with descriptions (100% coverage). The tool description adds no extra meaning beyond the schema fields.

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

    Purpose5/5

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

    Description clearly states verb 'retrieves' and resources 'raw diff' and 'structured list of changed files' for a specific Bitbucket pull request, distinguishing it from sibling tools like add_pull_request_comment and get_pull_request.

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

    Usage Guidelines3/5

    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 vs siblings; usage is implied by the name and description but not directly stated.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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