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Bitbucket MCP Server

Bitbucket MCP Server

by chzkyy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_readme

Retrieve the README file from a Bitbucket repository by specifying the repo slug. Automatically finds and reads README.md, README.txt, or readme.rst, with optional branch and workspace settings.

Instructions

Fetch and read the README file of a repository. Automatically finds README.md, README.txt, or readme.rst.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "repo_slug": "<repo_slug>"
    },
    "name": "get_readme"
  }
}

Input Schema

NameRequiredDescriptionDefault
branchNoThe branch name. Defaults to the main branch.
repo_slugYesThe repository slug.
workspaceNoThe workspace slug. Optional if BITBUCKET_WORKSPACE env var is set.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It does disclose a key behavior: automatically searching for README.md, README.txt, and readme.rst. But it does not mention what happens when no README is found, how the content is returned (raw text vs. structured object), or any permission requirements.

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 one short sentence that immediately states what the tool does, followed by a concrete list of supported extensions. Every word contributes value with no redundancy.

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

Completeness3/5

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

The tool is simple and the schema rich enough for invocation, but no output schema exists and the description does not explicitly explain the return type or format. For an AI agent, it is clear a README is fetched, but ambiguity remains about what 'read' returns (raw content, parsed text, etc.) and edge-case behavior when multiple or no README versions exist.

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 baseline is 3. The description adds no parameter-specific meaning beyond the schema; it neither explains repo_slug/workspace semantics nor the branch defaulting behavior, but the schema fields already carry detailed descriptions.

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 clearly states the action ('Fetch and read the README file') and identifies the resource ('of a repository'). It also lists the specific file names that are auto-detected, distinguishing this tool from generic file access tools like get_file_content and browse_directory.

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?

The description gives a clear context for when to use the tool: when you need the README file and don't know or want to specify the exact filename. However, it does not explicitly mention alternatives or exclusion cases, so it misses the top bar for explicitness.

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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