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aimarket-mcp-packager

generate_claude_desktop_config

Creates the claude_desktop_config.json snippet that registers a Docker-run MCP server with Claude Desktop, from capability inputs.

Instructions

Generate a claude_desktop_config.json snippet for the packaged MCP server.

Takes the same capability inputs as package_capability and returns the mcpServers entry that registers the docker-run MCP server with Claude Desktop (or any MCP host that reads this config format).

Returns: A JSON object (string) containing an mcpServers block — merge it into the user's existing claude_desktop_config.json and restart the host.

Example: generate_claude_desktop_config( capability_id="translate.multi@v2", product_id="prod-translate", name="Lyra Translator", )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable display name for the packaged MCP server, shown to end users in MCP clients such as Claude Desktop (e.g. 'Lyra Translator').
registryNoContainer registry namespace for the built image. Defaults to 'aifactory'. Set your own org/namespace to publish under a different account (e.g. 'ghcr.io/acme').aifactory
product_idYesOwning product ID on the AIMarket hub, e.g. 'prod-translate'. Used to namespace the Docker image and the MCP manifest.
descriptionNoOne- or two-sentence summary of what the capability does. Surfaced in the MCP manifest and the Claude Desktop config. Optional but strongly recommended — it becomes the tool description in the generated server.
input_schemaNoJSON Schema object describing the capability's input. For example, an object with a required string property 'text'. Omit or pass null for a schema-less capability.
capability_idYesFully-qualified capability identifier to package, e.g. 'translate.multi@v2'. This is the single capability the generated MCP server will expose as a tool.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states the tool generates and returns a JSON snippet, but does not mention any side effects, permissions, or state changes. For a generation tool, this is adequate but could be more explicit about its non-destructive nature.

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 front-loaded with the main purpose, followed by usage context and an example. It is relatively concise, though the example repeats parameter names already defined. No superfluous sentences.

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 the tool has 6 parameters and no annotations, the description covers the return value format and usage. The output schema exists, so not detailing its structure is acceptable. It is complete enough for an agent to understand when and how to use it.

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?

Schema coverage is 100%, yet the description adds value: it groups parameters as 'same inputs as package_capability', explains the 'description' parameter's purpose in the generated config, and notes the default for 'registry'. This goes beyond the schema alone.

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 verb 'generate' and the resource 'claude_desktop_config.json snippet'. It distinguishes from siblings by noting it takes the same inputs as 'package_capability' but returns an 'mcpServers' entry, not a Dockerfile or packaged capability.

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?

It explains the tool's output (mcpServers entry) and relation to 'package_capability', implying when to use it (after packaging). It also provides an example. Does not explicitly exclude scenarios, but context is clear enough.

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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