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lablab-mcp

lablab-mcp

by WilliamAxelC
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

get_user_profile

Retrieve a Lablab.ai builder profile by username to view points, rank, bio, GitHub, social links, enrolled hackathons, and submitted projects.

Instructions

Fetch full details for any Lablab.ai builder profile: points, rank, bio, GitHub, social links, enrolled hackathons, and submitted projects.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_user_profile"
  }
}

Input Schema

NameRequiredDescriptionDefault
usernameNoLablab.ai username with or without '@' (e.g., 'WilliamA').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. 'Fetch' clearly signals a read-only operation and the field list tells the agent what data comes back, but there is nothing about authentication needs, rate limits, or handling of missing/private profiles.

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?

A single front-loaded sentence that lists the payload contents without waste. Nothing to trim and nothing buried.

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?

With no output schema, the description usefully enumerates the returned fields, which is exactly the compensating detail needed for a single-parameter read tool. Only the absence of any privacy/auth caveat for profile data keeps it from a 5.

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

Parameters3/5

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

Only one parameter and schema coverage is 100% – the schema already documents the accepted 'username' format including the optional '@'. The description adds no syntax or constraint detail beyond that, so the baseline 3 applies.

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?

Specific verb ('Fetch') plus specific resource ('Lablab.ai builder profile'), and it enumerates the returned fields (points, rank, bio, GitHub, social links, hackathons, projects). No sibling in the list overlaps – check_my_status is the closest and is about the caller's own status, so the distinction is easy to draw.

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 says what can be fetched but never states when to use this versus alternatives, nor any prerequisites. In particular there is no guidance on how it relates to the sibling check_my_status, which could plausibly return overlapping profile data.

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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