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hermoso-ai

Hermoso

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by hermoso-ai
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JavaScript
Remote

What to fix next, post by post

diagnose_posts
Read-only

Diagnose a post's performance to identify what's wrong and where to edit next: change hook, rebuild middle, or fix offer. Returns nothing-needs-fixing when no problem exists.

Instructions

WHAT TO FIX NEXT, post by post — and the tool that fixes it. post_performance tells you which hook is AHEAD; this tells you what is WRONG with a given post and where the next edit goes. Under-distributed is A HOOK PROBLEM (change the opening: mine_angles, then list_hooks, then plan_variations). Seen but not held is A RETENTION PROBLEM (plan_variations to rebuild the middle against the same hook). Seen, held, and still not converting is AN OFFER PROBLEM. FOUR REFUSALS, AND YOU SHOULD REPEAT THEM RATHER THAN PAPER OVER THEM: (1) a post younger than ~24h is TOO EARLY and is never called a failure — it has not had its run; (2) a metric the platform does not publish is UNMEASURED, never zero — Facebook has published no post reach since 2026-06-15, Reddit publishes no impressions, and Google Business publishes nothing per-post at all; (3) below 5 measured posts on a channel there is no baseline of the brand's own, and the ONLY fallback is a published short-video hook floor that is NOT our measured number and does not transfer off TikTok/Instagram/YouTube — it is attributed in the output and you should attribute it too; (4) it does not always find a problem, and 'nothing here needs fixing' is a real answer rather than a failure to look. Hermoso cannot see conversions for an organic post — no channel reports installs or purchases against a post id — so the offer rung runs ONLY when the user tells you they are not converting and you pass converting:false. Print summary verbatim. Read-only, 0 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNohow many recent posts to diagnose (default 25, max 200). The baseline is always built from EVERY post recorded for the brand, never only these, so a bad month can never become its own definition of normal.
channelNorestrict to one channel: facebook, instagram, threads, x, linkedin, youtube, tiktok, reddit, pinterest
convertingNopass false ONLY when the user has told you these posts are getting seen and are not converting — it re-reads the ones that are earning their reach as an offer problem instead of a win. Omit when you do not know; we cannot measure it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.161

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only say read-only/non-destructive; the description adds substantial behavioral detail: four refusal conditions, the inability to see conversions for organic posts, the requirement to print summary verbatim, and the attribution of fallback baselines. These go well beyond the annotations and materially shape how an agent interprets results.

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 long and heavily formatted, but each block carries diagnostic heuristics, routing, or refusal logic that would otherwise be absent. It is front-loaded with the core purpose, though the all-caps style and several run-on passages keep it from being maximally clean.

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?

For a complex diagnostic tool with no output schema, the description covers selection, invocation, edge cases, refusal behavior, fallback attribution, conversion limitations, and the required summary output. An agent has enough context to call and interpret the tool correctly without inventing missing details.

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 coverage is 100%, so per the rubric the baseline is 3. The description mainly reinforces converting:false behavior already documented in the schema, and it does not add new parameter-level meaning beyond what the input schema already gives for limit, channel, and converting.

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?

States a specific diagnostic function: it tells what is wrong with a given post and where the next edit goes, and explicitly contrasts with sibling post_performance (which hook is ahead vs what is wrong). This makes the tool's unique role unambiguous.

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?

Gives explicit routing guidance: use post_performance to see which hook is ahead, use diagnose_posts to identify hook/retention/offer problems, and follow with mine_angles/list_hooks/plan_variations. It also defines when NOT to diagnose (posts younger than ~24h, unreliable metrics, fewer than 5 baseline posts) and when converting:false should be passed.

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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