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lablab-mcp

lablab-mcp

by WilliamAxelC
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

get_submission_guidelines

Retrieve Lablab.ai hackathon submission guidelines: checklist, video pitch requirements, repository criteria, and tips to improve judging scores.

Instructions

Get the official Lablab.ai hackathon submission checklist, video pitch requirements, repository criteria, and tips to maximize judging scores.

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_submission_guidelines"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
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 read-only retrieval and the enumerated content conveys roughly what comes back, but there is no disclosure of auth requirements or return format. For a low-risk static-content fetch this is adequate, but not rich.

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?

A single front-loaded sentence with no filler, leading with the verb and resource. Slightly list-heavy but every clause adds scope, so nothing needs trimming.

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 no-param, no-output-schema retrieval tool, the description adequately conveys both the source and the content returned. Nothing essential for correct invocation is missing; only a return-format hint would add value.

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 takes zero parameters, so there is no parameter semantics to document – the baseline is 4. The description correctly adds nothing param-related, since none exist.

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 a well-defined resource ('Lablab.ai hackathon submission checklist'), then enumerates the exact content returned (video pitch requirements, repository criteria, judging tips). An agent can distinguish it from siblings like get_hackathon_details, though it never explicitly names a contrasting sibling.

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?

Usage is only implied by the topic. The description never states when to call this versus get_hackathon_details or when it is not needed (e.g., after submission closes), and names no alternatives or prerequisites.

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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