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clikader

bitbucket-python-mcp

by clikader

remember_from_pr_comment

Extract and store learnings from Bitbucket pull request comments. Categorize by type like coding style or testing, and optionally apply across all repos in a workspace.

Instructions

Extract and store a learning from a PR comment.

Use this when you identify a standard or pattern in a PR comment that should be remembered for future reference.

Args: repository: Repository where the PR comment was found pr_id: PR ID where the comment was found comment_content: The original comment content (for reference) learning: The extracted learning/standard to remember category: Category - pipeline, testing, coding_style, tools, workflow, general tags: Comma-separated tags workspace: Workspace (uses default if not specified) apply_to_all_repos: If True, applies to all repos in workspace; if False, only this repo

Returns: Confirmation with the created memory

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
pr_idYes
categoryNogeneral
learningYes
workspaceNo
repositoryYes
comment_contentYes
apply_to_all_reposNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the parameters and that a memory is created, but lacks details on side effects, permissions, idempotency, or error conditions. Essential behavioral context is missing.

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 uses a clear docstring format with Args and Returns sections. It's front-loaded with purpose, and each sentence is necessary. Slightly verbose but still efficient.

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?

Given the tool's complexity (8 params, 4 required) and the presence of an output schema, the description covers purpose, usage, and parameter details. However, it lacks behavioral transparency, which prevents a higher score.

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

Parameters4/5

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

Although the schema has 0% description coverage, the tool's description explains each parameter's meaning and default values (e.g., category enum, apply_to_all_repos behavior). This adds significant value beyond the schema titles.

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 tool's purpose: 'Extract and store a learning from a PR comment.' It specifies a specific verb and resource, distinguishing it from similar tools like add_memory that operate on generic memories.

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 explicitly states when to use the tool: 'Use this when you identify a standard or pattern in a PR comment that should be remembered for future reference.' It does not mention alternative tools, but the context is clear given the sibling tools.

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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