lablab-mcp

brainstorm_ideas

Generate tailored hackathon project concepts for a Lablab.ai event based on sponsor tech, problem space, and judging criteria.

Instructions

Generate high-scoring, tailored hackathon project concepts for a specific Lablab.ai event based on its sponsor technologies, problem space, and judging criteria.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hackathon_slug": "<hackathon_slug>"
    },
    "name": "brainstorm_ideas"
  }
}

Input Schema

Name	Required	Description
`team_size`	No	Number of builders on the team (e.g., 1 for solo, 2-4 for team).
`complexity`	No	Desired scope and complexity of the ideas (default: 'balanced').
`focus_areas`	No	Optional areas of interest (e.g., ['devops', 'voice-agents', 'healthcare']).
`hackathon_slug`	Yes	Slug of the hackathon to brainstorm for.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 29, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose whether this is a read-only/offline generation versus a live API call, whether results are persisted, what happens with an invalid hackathon_slug, or how many ideas are returned. For a generation tool with zero annotation coverage this leaves meaningful gaps.

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 that packs the key qualifiers (high-scoring, tailored, sponsor technologies, judging criteria) without filler. Slightly dense but nothing extraneous.

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

Completeness3/5

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

With no output schema and no annotations, the description should ideally describe the shape of the returned ideas (count, structure, ranking). It gestures at quality ('high-scoring') but never specifies output format, leaving an agent unsure what to expect back.

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%, so all four parameters (including the enum values for complexity and the array semantics of focus_areas) are already documented in the schema. The description adds no parameter-level detail beyond what the structured fields provide, so the baseline 3 applies.

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 ('Generate') and resource ('hackathon project concepts') with qualifying scope ('tailored ... based on sponsor technologies, problem space, and judging criteria'). An agent can distinguish it from scaffold_project or get_hackathon_details by the output type, though no sibling is named 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?

The phrase 'for a specific Lablab.ai event' implies this is used during pre-build ideation, but there is no explicit when-to-use guidance, no prerequisites (e.g., does the slug need to exist?), and no mention of alternatives such as scaffold_project which the agent might confuse it with.

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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