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Hermoso

by io.github.hermoso-ai

Import a channel’s past posts

backfill_posts
Idempotent

Import this brand's PAST posts from a channel into the performance record, so 'which hook works' can draw on history rather than only on what was published since Hermoso started recording. Supports facebook, instagram, threads, youtube, tiktok, pinterest and bluesky; the others say plainly why they cannot (LinkedIn and Reddit have no enumerate-my-posts endpoint on our grant, X bills per read so it is excluded from bulk import, Google Business has had no per-post insights since 2023, and Telegram's Bot API cannot read a chat's past messages at all — nothing published before Hermoso is recoverable through a bot token). BOUNDED, RESUMABLE AND QUOTED: it runs as a DRY RUN by default and tells you how many posts it found and what reading them will cost — pass confirm:true to import, and pass the returned cursor to continue. AN IMPORTED POST IS WEAKER EVIDENCE THAN A RECORDED ONE and is labelled 'backfilled': its hook is recovered ONLY where the post matches a Hermoso creation by asset or caption. A post made outside Hermoso stays UNATTRIBUTED — it counts toward channel and format totals but never votes on which hook works. Never guess a hook from a caption. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNohow many posts this page (default 50, max 200)
cursorNoresume from a previous run
channelYeswhich channel to import from
confirmNoactually import — omit for a dry run that only quotes the cost
accountRefNowhich Page / account, when the brand has more than one

TDQS

A4.6/5.0
Behavior5/5

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

The description far exceeds what annotations provide. It discloses the DRY RUN default, the cost-quoting behavior, the resumable cursor workflow, the bounded nature, the 'backfilled' labeling, the asset/caption matching rule for hook recovery, and the unattributed-post behavior. It also clarifies why excluded channels fail, which is operational behavior an agent would otherwise discover only by calling. No contradiction with annotations: readOnlyHint=false aligns with the import-on-confirm behavior, and idempotentHint=true aligns with the safe dry-run default.

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 (~200 words) but every section carries distinct operational value: purpose, channel support with per-channel failure reasons, dry-run/confirm/cursor workflow, evidence-quality caveats, and pricing. It is front-loaded with the core purpose and uses bolded section markers for scannability. The ALL-CAPS emphasis is stylistically noisy, and the first sentence partly repeats the title, which keeps it from a 5.

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?

This is a high-complexity tool — 5 params, 11 channel considerations, a dry-run/confirm mode split, resumability, cost quoting, and no output schema — and the description covers all of it. It explains what the dry run returns (post count and cost quote), how to continue (returned cursor), what records look like (labelled backfilled), and downstream semantics (attribution rules). Nothing an agent needs to safely invoke or correctly interpret the result is missing.

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 coverage is 100%, so the baseline is 3, but the description adds genuine value beyond the schema: it explains why only 7 of 11 enum channels work, ties confirm to the dry-run→import workflow, and ties cursor to the returned-value continuation loop. The schema already documents limit defaults and accountRef disambiguation, so the description doesn't need to repeat those.

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 first sentence states a specific verb and resource: 'Import this brand's PAST posts from a channel into the performance record,' with an explicit purpose ('so which hook works can draw on history'). The backfill concept clearly distinguishes it from sibling list_*, collect_post_metrics, and post_performance tools, which handle current or already-recorded data.

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 gives strong contextual guidance: use this to extend history before Hermoso started recording, and the 'WEAKER EVIDENCE' section explains when the output is trustworthy ('never votes on which hook works'). It also preempts futile calls by explaining exactly why LinkedIn, Reddit, X, Google Business, and Telegram cannot be backfilled. However, it names no explicit sibling alternative for recording current posts, so the when-not guidance is implied rather than stated with tool names.

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
Disambiguation2/5

With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.

Naming Consistency4/5

The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.

Tool Count1/5

293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.

Completeness4/5

The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.

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