ChiefLab

chieflab_record_manual_metrics

P116 — paste-what-you-see metric fallback for channels without public-API measurement (X, LinkedIn, Email, landing pages, Product Hunt). USE WHEN a published action's channel doesn't have a public-JSON metrics endpoint (anything except hacker_news / reddit) AND the founder is ready to paste the visible numbers. Reads metadata.proof.artifactUrl to confirm the post is recorded, writes the provided metrics into metadata.proof.metrics + measuredAt, auto-classifies the outcome (channel-specific thresholds: X needs likes/replies, LinkedIn needs reactions/comments, Email needs opens/clicks/replies, etc.), and creates next-move actions just like the auto-measurement path. Returns the metric schema for the channel so callers can render the right form fields.

Input Schema

TableJSON Schema

Name	Required	Description
`force`	No	Re-record even if metadata.proof.measuredAt is already set. Default false.
`metrics`	No	Free-form metrics map. Channel-specific keys: x → {impressions, likes, replies, retweets, clicks}; linkedin → {impressions, reactions, comments, reposts, clicks}; email → {sent, opens, clicks, replies, unsubscribes}; landing_hero → {sessions, signups, avg_time_seconds, bounce_rate_pct}; product_hunt → {upvotes, comments, rank}. Call without metrics to just retrieve the schema for the action's channel.
`actionId`	Yes	The action id to attach metrics to.
`workspaceId`	No	Optional workspace id.

TDQS

A4.7/5.0

Behavior4/5

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

Since no annotations are provided, the description carries full burden. It discloses that it writes metrics, auto-classifies outcomes based on thresholds, and creates next-move actions. It mentions the force parameter for re-recording. However, it does not explicitly state whether the operation is destructive or if there are side effects beyond the force flag.

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 but dense with information. It front-loads the purpose and then details use case, behavior, and parameters. While not broken into sections, it is concise and contains minimal redundancy for the complexity involved.

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

Completeness5/5

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

Given no output schema, the description mentions that it returns the metric schema for the channel. It covers all 4 parameters, including nested objects, and explains the auto-classification and next-move creation outcomes. It is complete for the tool's purpose.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant value beyond the schema. It explains the channel-specific keys for metrics (e.g., x, linkedin, email) and clarifies that calling without metrics retrieves the schema. The force parameter's behavior is also explained.

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 it's a manual metric fallback for channels without public-API measurement (X, LinkedIn, etc.), distinguishing it from siblings like chieflab_measure_hacker_news and chieflab_measure_reddit. It specifies the verb 'record' and the resource 'manual metrics', with a specific use case.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'USE WHEN a published action's channel doesn't have a public-JSON metrics endpoint (anything except hacker_news / reddit) AND the founder is ready to paste the visible numbers.' This clearly defines context and implies alternatives (automated tools for HN/Reddit).

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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