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demandscope

demandscope

by baobabcat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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hn_trend

Compare Hacker News story counts between recent and prior periods to identify accelerating attention for any query.

Instructions

Hacker News story count for the last N days vs the prior window; growth_ratio > 1 means accelerating attention.

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": {
      "query": "<query>"
    },
    "name": "hn_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
window_daysNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden and does add useful behavioral detail: it compares the last N days against the prior window and defines growth_ratio > 1 as accelerating attention. However, it does not describe query semantics, window_days defaults, edge cases like zero prior counts, or the return shape.

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?

The description is a single sentence with no filler, front-loading the core metric and adding an interpretation of the result. Every word contributes to understanding the tool's function.

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

Completeness2/5

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

For a tool with no output schema, no annotations, and two parameters, this description is too sparse. It does not define the required query parameter, explain defaults or edge cases, or provide enough information to decide when to use this tool over its siblings, making confident invocation difficult.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for both parameters. It only hints that window_days maps to 'last N days'; the required query parameter is never mentioned or explained, leaving the agent to guess what string should be supplied.

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 resource and metric: Hacker News story count compared across two time windows, with growth_ratio as the output. It is clear about the tool's analytical intent, but it does not explicitly distinguish itself from the sibling hn_signal tool, leaving some differentiation to inference.

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 'accelerating attention' implies this tool is for measuring acceleration in Hacker News story interest, but there is no explicit when-to-use or when-not-to-use guidance. It names no alternatives and gives no conditions for preferring hn_trend over hn_signal or github_trend.

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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