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chieflab_measure_reddit

P115 — pull a Reddit post's score + comments + upvote ratio + top replies via the public Reddit JSON API and write them to the action's metadata.proof. USE WHEN a published Reddit action is ripe for 24h measurement. Parses subreddit + post id from the publishedUrl, calls https://www.reddit.com/r//comments/.json (no auth), auto-creates next-move actions based on the outcome (trending → reply + cross-post; engaged → reply; downvoted → rewrite angle). Idempotent — second call returns existing measurement unless force:true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRe-measure even if metadata.proof.measuredAt is already set. Default false.
actionIdYesThe action id whose publishedUrl points at the Reddit post. Required.
workspaceIdNoOptional workspace id.

TDQS

A4.4/5.0
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses idempotency (unless force:true), auto-creation of next-move actions based on outcome (trending, engaged, downvoted), and that it uses the public Reddit API with no auth. This is comprehensive behavioral transparency.

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 paragraph of about 5 sentences. It is front-loaded with the main action and then logically proceeds to usage condition, technical detail, side effects, and idempotency. It is concise and informative, though slightly dense.

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?

The description explains the core functionality and side effects well, but lacks explicit details about the return value or result format. Since there is no output schema, the description should ideally mention what the function returns or how the result is structured. It also omits error handling or edge cases.

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

Parameters4/5

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 context beyond the schema: it explains that actionId must have a publishedUrl pointing to the Reddit post, and that force:true triggers a re-measurement. This adds meaningful value, justifying a score above baseline.

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 clearly states the verb 'pull' and the resource 'Reddit post's score + comments + upvote ratio + top replies', and specifies it writes to metadata.proof. It also describes the side effect of creating next-move actions. The name and description distinguish it from its sibling 'chieflab_measure_hacker_news' as it is specific to 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 provides a clear usage condition: 'USE WHEN a published Reddit action is ripe for 24h measurement.' It also explains that it parses subreddit and post id from the publishedUrl. However, it does not explicitly state when not to use it or mention alternative tools, but the context is sufficient for an AI agent to decide.

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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