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Marketic

Marketic

by Das-rebel
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Python
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curate_ugc

Discover and rank user-generated posts for a hashtag by monitoring feeds and filtering for aesthetic quality. Returns sorted list by relevance and aesthetic score.

Instructions

Curate user-generated content for a given hashtag. Discovers posts via hashtag monitoring, filters by aesthetic score, and returns a list sorted by combined relevance + aesthetic score.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hashtag": "<hashtag>"
    },
    "name": "curate_ugc"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
hashtagYes
platformNoinstagram
min_aesthetic_scoreNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the operational flow (hashtag monitoring, aesthetic filtering, sorted return) and implies a read-only selection action, but it does not mention authentication, platform access, rate limits, or whether any UGC states change.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences, with the verb+resource in the first clause and no filler. Every phrase adds information about scope, process, or output ordering.

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 states what is returned (a sorted list) and the core selection logic, which is adequate for a 4-parameter tool. However, with no output schema, return fields are unknown, and platform/limit semantics are unaddressed, leaving non-trivial gaps for an agent deciding whether defaults are acceptable.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate for the schema's bare parameter names. It clarifies hashtag and alludes to min_aesthetic_score via 'filters by aesthetic score', but it never explains platform or limit, even though both have defaults that materially change results.

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 names a specific verb ('curate'), a resource ('user-generated content'), and a scope ('for a given hashtag'). It then spells out the pipeline — discover, filter, sort — which clearly distinguishes it from siblings like request_ugc_permission and track_ugc.

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 intended context is clear: use this when you want to identify/rank UGC for a hashtag via aesthetic and relevance scoring. It does not explicitly name alternatives or exclusion cases, but the process description makes the primary use unambiguous.

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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