lablab-mcp

list_hackathons

Retrieve live, upcoming, and featured AI hackathons from Lablab.ai with slugs, titles, and event URLs. Set an optional limit to control how many results return.

Instructions

List live, upcoming, and featured AI hackathons on Lablab.ai with slugs, titles, and event URLs.

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": "list_hackathons"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of hackathons to return (default: 10).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 29, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the returned fields and the scope filter (live/upcoming/featured), which implies a read-only operation, but says nothing about auth requirements, pagination, or rate limits.

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 carries the scope, source, and return shape with no filler; nothing could be cut without losing information.

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?

For a simple one-parameter, no-output-schema list tool, the description gives enough to call it correctly and know what comes back. Only minor gaps remain around pagination behaviour and how 'featured' interacts with 'upcoming'.

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?

Schema coverage is 100%, so the single 'limit' parameter and its default are already fully documented in the schema. The description adds no syntax or default details beyond what the schema provides, making the baseline 3 appropriate.

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 names a specific verb ('List') and resource ('live, upcoming, and featured AI hackathons on Lablab.ai') and even enumerates the returned fields (slugs, titles, event URLs). It is clearly distinguishable in intent from get_hackathon_details, though it never names that sibling explicitly.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the entry-point list call that precedes get_hackathon_details, but the description states no when-to-use condition, no alternatives, and no exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server