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hn-front-count

Count current Hacker News front-page stories. Titles discarded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions that titles are discarded, which hints at the output format (a count without titles), but it does not disclose network access, potential failure modes, rate limits, or what exactly the count represents. The behavior is minimally transparent and leaves key runtime aspects unspecified.

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?

The description is extremely concise, with two short sentences and no filler. It front-loads the core action and adds a behavioral hint. However, its brevity borders on under-specification, leaving out critical details an agent would need for correct invocation.

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?

Given the tool has 9 optional parameters and no output schema, the description is far from complete. It does not explain how the parameters might influence the count (if any do), what the return format is beyond a number, or what happens when the front page is unavailable. The description lacks the context needed for an agent to use the tool reliably.

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?

The schema description coverage is 100%, so each parameter has a generic description, but these descriptions are not tool-specific and do not clarify how they relate to counting HN stories. The description itself does not mention any parameters, leaving the agent unsure which of the 9 optional parameters are relevant or whether any are required. The generic 'discarded' notes in the schema suggest most parameters are ignored, but the description does not confirm that or explain the purpose of accepting them.

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 clearly states the action (count) and the resource (current Hacker News front-page stories), making the primary purpose unambiguous. It does not explicitly distinguish from sibling tools, but no sibling appears to overlap, so the purpose is clear enough for an agent to identify when this tool is relevant.

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?

There is no guidance on when to use this tool versus alternatives, nor any mention of prerequisites or conditions. The description only states what it does, not when to invoke it. An agent would have to infer usage from the name and sibling context.

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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