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WilliamAxelC

lablab-mcp

by WilliamAxelC

get_hackathon_details

Retrieve complete Lablab.ai hackathon details, including deadlines, prizes, sponsors, schedule, rules, and registered teams, by providing a hackathon slug.

Instructions

Get comprehensive details about a specific Lablab.ai hackathon: deadlines, time remaining, prize breakdown, sponsor technologies, schedule, rules, and registered teams.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hackathon_slugYesSlug of the hackathon (e.g., 'assemblyai-voice-agent-hackathon', 'amd-lablab-ai-academy-challenge').

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. 'Get' implies a non-mutating read, and the enumeration of returned content is genuinely informative, but there is no mention of auth requirements, rate limits, or failure behavior for an invalid slug.

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?

One tightly packed sentence with the verb and resource front-loaded and the payload enumerated afterwards. No filler and nothing redundant.

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 single-parameter read tool with no output schema, the enumerated content fields effectively describe what comes back, which is the main risk area. Missing only edge-case behavior (invalid slug, auth) to be fully complete.

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 description coverage is 100% with a single parameter that carries its own examples, so the schema does the heavy lifting. The description adds no syntax, format, or matching guidance beyond what the schema already provides.

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?

States a specific verb (Get) and resource (details about a specific hackathon), and enumerates the payload (deadlines, prize breakdown, schedule, teams). It implicitly separates itself from the plural sibling list_hackathons by emphasizing 'a specific' hackathon, though it never names an alternative.

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 implied: call this when you need details about one known hackathon, presumably after list_hackathons. There is no explicit when-to-use statement, no prerequisites (e.g., needing a valid slug), and no exclusion or alternative named.

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