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clikader

bitbucket-python-mcp

by clikader

add_memory

Store important standards, patterns, or learnings from PR comments and code reviews for future reference in other repositories.

Instructions

Store a new memory/learning for future reference.

Use this tool to remember important standards, patterns, or learnings discovered from PR comments, code reviews, or user instructions. These memories will be available when reviewing other PRs or repositories.

Common use cases:

  • Workspace coding standards (e.g., "use uv for package management")

  • Pipeline requirements (e.g., "use shared-pipeline for SonarQube scans")

  • Testing standards (e.g., "mock all external API calls in tests")

  • Code style preferences (e.g., "use type hints for all function parameters")

Args: content: The learning/standard to remember (be specific and actionable) category: Category - one of: pipeline, testing, coding_style, tools, workflow, general tags: Comma-separated tags for easier searching (e.g., "sonarqube,pipeline,ci") workspace: Workspace this applies to (omit for global/all workspaces) repository: Repository this applies to (omit for all repos in workspace) source_type: Source of this memory - user, pr_comment, or api_response pr_id: If from a PR comment, the PR ID

Returns: Confirmation with the created memory details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
pr_idNo
contentYes
categoryNogeneral
workspaceNo
repositoryNo
source_typeNouser

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Describes the action and return value, but with no annotations, it lacks details on side effects, persistence, or idempotency. Adequate but could be more thorough.

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?

Well-structured with a short main description, bulleted use cases, and clear Args section. Every sentence is informative and no wasted words.

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

Completeness5/5

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

Covers purpose, usage scenarios, parameter details, and return value. Given the complexity and output schema, it provides all necessary context for correct invocation.

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

Parameters5/5

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

Given 0% schema coverage, the description compensates fully by explaining each parameter with examples and hints, such as listing category options and tag formatting, which adds significant value.

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 'Store a new memory/learning for future reference' and provides specific use cases, distinguishing it from related tools like search_memories or list_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?

Explicitly says when to use (remember standards/patterns from PRs, code reviews, etc.) and lists common use cases. Does not mention when not to use or alternatives like remember_from_pr_comment, but still provides strong guidance.

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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