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akutishevsky

LunchMoney MCP Server

get_all_assets

Retrieve all manually-managed financial assets from your LunchMoney account to track investments, properties, and other holdings.

Instructions

Get a list of all manually-managed assets associated with the user

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'get_all_assets' tool. It fetches all assets from the Lunchmoney API endpoint `/assets` using the configured baseUrl and API token, handles errors by returning a text content error message, parses the response, type-annotates as Asset[], and returns the JSON-stringified list as text content.
    async () => {
        const { baseUrl, lunchmoneyApiToken } = getConfig();
        
        const response = await fetch(`${baseUrl}/assets`, {
            headers: {
                Authorization: `Bearer ${lunchmoneyApiToken}`,
            },
        });
    
        if (!response.ok) {
            return {
                content: [
                    {
                        type: "text",
                        text: `Failed to get assets: ${response.statusText}`,
                    },
                ],
            };
        }
    
        const data = await response.json();
        const assets: Asset[] = data.assets;
        
        return {
            content: [
                {
                    type: "text",
                    text: JSON.stringify(assets),
                },
            ],
        };
    }
  • Registers the 'get_all_assets' tool on the MCP server with the tool name, description, empty input schema object (no parameters), and the inline handler function.
    server.tool(
        "get_all_assets",
        "Get a list of all manually-managed assets associated with the user",
        {},
        async () => {
            const { baseUrl, lunchmoneyApiToken } = getConfig();
            
            const response = await fetch(`${baseUrl}/assets`, {
                headers: {
                    Authorization: `Bearer ${lunchmoneyApiToken}`,
                },
            });
    
            if (!response.ok) {
                return {
                    content: [
                        {
                            type: "text",
                            text: `Failed to get assets: ${response.statusText}`,
                        },
                    ],
                };
            }
    
            const data = await response.json();
            const assets: Asset[] = data.assets;
            
            return {
                content: [
                    {
                        type: "text",
                        text: JSON.stringify(assets),
                    },
                ],
            };
        }
    );
  • src/index.ts:29-29 (registration)
    Invokes registerAssetTools on the MCP server instance, which in turn registers the 'get_all_assets' tool along with other asset-related tools.
    registerAssetTools(server);
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it retrieves a list but doesn't specify if it's paginated, rate-limited, requires authentication, or what happens if no assets exist. This is a significant gap for a tool with zero annotation coverage.

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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose, making it efficient and easy to parse for an AI agent.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format, error conditions, or behavioral traits, which are crucial for a tool that retrieves user data. This leaves gaps in understanding how to handle the tool effectively.

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?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for this dimension.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get a list') and resource ('manually-managed assets associated with the user'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_all_crypto' or 'get_all_plaid_accounts', which might also retrieve asset-like resources, so it misses full sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing, or compare to other asset-related tools like 'get_all_crypto', leaving the agent to infer usage from context alone.

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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