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IRIS: startup intelligence

get_launches

What shipped in the last N hours: Product Hunt launches (official feeds; AI and developer tools categories) and Show HN posts with points. Each item links to its launch page; Show HN items include the product URL.

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_launches"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNo6-72, default 48
categoryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full behavioral burden, and it does disclose the sourcing ('official feeds'), the category scope, and the shape of returned items (links, points, product URL). It says nothing about ordering, empty-result behavior, or what happens at the hours boundaries. There is also a small internal mismatch: it mentions only 'AI and developer tools categories' while the schema enum offers AI, Developer tools, Show HN, and Tech.

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?

Two sentences, front-loaded with the core question the tool answers, followed by source detail and item shape. No filler, no restatement of the tool name, and every clause adds 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 two-parameter read-only aggregation tool with no output schema and no annotations, the description covers sources, item contents, and linking behavior — enough to call it correctly. The remaining gaps (ordering, result caps, category default interplay) are minor but real, so not 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?

Schema coverage is 50%: `hours` is documented with its range and default, but `category` has only an enum and no explanation. The description partly compensates by grounding categories in real sources ('AI and developer tools categories', 'Show HN'), but it leaves 'Tech' undefined and never clarifies the default or range behavior for hours. Baseline 3 is appropriate given the schema does half the work.

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 subject ('what shipped in the last N hours') and enumerates the two sources it aggregates: Product Hunt launches and Show HN posts. That is far more concrete than the sibling names (get_jobs, get_rounds, get_voices) would suggest, so an agent can tell what domain this covers. It stops short of explicitly contrasting itself with any sibling, which keeps it at 4 rather than 5.

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 by the time-window framing — you call this to see recent launches — but the description never says when to prefer it over get_daily_brief, get_wire, or get_upcoming, nor does it state any exclusions or prerequisites. The agent must infer the 'recency digest' use case from the wording alone.

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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