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preview_merge

Fetches source (team) and target (personal) configuration to enable the LLM to merge team standards with personal customizations.

Instructions

Return source (team) and target (user) content for the LLM to intelligently merge.

The MCP server does NOT perform the merge. It returns both contents so the host IDE's LLM can understand the semantics and produce an intelligent merge that preserves personal customizations while incorporating team standards.

Args: namespace: Profile namespace config_name: Name of the config to merge (from list_profile_configs) project_root: Project root for project-relative targets

Returns: Dict with source_content, target_content (None if no existing file), target_path, config_type, and description

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceYes
config_nameYes
project_rootNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description discloses that the tool does not perform the merge and returns both contents. It also describes the return structure. Without annotations, this is good transparency, though it could mention any side effects or permissions.

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 well-structured with a clear summary, a note on behavior, and an Args section. It is not overly verbose, though the Args section could be more integrated. Front-loaded with the main purpose.

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?

The description covers purpose, behavior, parameters, and return values. It differentiates from siblings (e.g., apply_merge) and includes all necessary context for an agent to use the tool correctly.

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?

The schema has 0% description coverage, but the description's 'Args' section thoroughly explains each parameter's meaning, including origins (e.g., 'from list_profile_configs' for config_name). This fully compensates for the missing schema 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 that the tool returns source and target content for merging, and explicitly notes that the MCP server does not perform the merge. This distinguishes it from siblings like apply_merge, making the purpose specific and unambiguous.

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 explains that the tool is used to return contents for the LLM to intelligently merge, implying it is for previewing. It does not explicitly state when not to use it, but the context of sibling tools (e.g., apply_merge) provides implicit 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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