ChiefLab

chieflab_measure_hacker_news

P109 — pull a Hacker News post's score + comments + top replies via the public HN API and write them to the action's metadata.proof. USE WHEN a published Hacker News action is ripe for 24h measurement (checks the publishedUrl on the action, parses the item id, calls the Firebase API). Auto-creates a next-move action based on the outcome (trending → reply + cross-post; engaged → reply; stalled → rewrite angle). Idempotent — second call returns the existing measurement. No API key required; HN is public-read.

Input Schema

TableJSON Schema

Name	Required	Description
`force`	No	Re-measure even if metadata.proof.measuredAt is already set. Default false.
`actionId`	Yes	The action id whose publishedUrl points at the HN post. Required.
`workspaceId`	No	Optional workspace id.

TDQS

A4.2/5.0

Behavior5/5

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

No annotations are provided, so the description fully covers behavioral traits: it explains the internal process (parses item id, calls Firebase API), side effects (auto-creates next-move action), idempotency, and the lack of API key requirement. No contradictions.

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 a single dense paragraph that front-loads the primary action. It is efficient but could benefit from slight restructuring (e.g., bullet points for readability). No wasted sentences.

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?

Given the lack of output schema, the description should explain what the metadata.proof structure contains or what the return value is. It mentions side effects (creating a next-move action) but does not describe the outcome format, leaving a gap for an agent.

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 baseline is 3. The description adds minimal extra information about parameters (e.g., force is mentioned implicitly via 'Re-measure'), but it does not significantly improve understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb-resource pair ('pull a Hacker News post's score + comments + top replies') and specifies the target (via publishedUrl). It distinguishes itself from sibling tools by focusing on HN measurement, unlike chieflab_measure_reddit for Reddit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use ('USE WHEN a published Hacker News action is ripe for 24h measurement') and explains the outcome-based behavior. It does not directly name alternatives or when-not-to-use, but the context is clear enough.

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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TDQS

A3.7/5.0

Disambiguation4/5

Most tools have distinct purposes, e.g., approve_action vs execute_approved_action vs publish_approved_post. However, alias overloading (e.g., chieflab_launch_product and chieflab_get_users_after_build pointing to the same handler) introduces some ambiguity. The detailed descriptions mostly mitigate confusion, but an agent might still struggle to choose between near-identical aliases.

Naming Consistency4/5

Tools predominantly follow a 'chieflab_verb_noun' pattern (e.g., chieflab_approve_action, chieflab_connect_provider). A few exceptions exist (chieflab_help, chieflab_inbox, chieflab_boot) that are single nouns, but these are clearly distinct and the overall consistency is high.

Tool Count3/5

32 tools is on the high side for an MCP server, but the domain of a growth/marketing launch platform naturally requires many operations (launch, approve, execute, measure, iterate, connect providers, etc.). The count is borderline but still manageable; it doesn't reach the 50+ extreme.

Completeness4/5

The tool set covers the full launch lifecycle: create, approve, execute, measure, and iterate. It includes provider connections, manual fallback, brain summary, and work requests. Minor gaps exist (e.g., no explicit tool for deleting a launch or revoking approval), but core workflows are fully supported.

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