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sprkly-mcp

Get TikTok posting options

sprkly_get_tiktok_posting_options
Read-only

This creator's allowed TikTok privacy levels and interaction settings, fetched live from TikTok. You usually do NOT need this before scheduling: sprkly_schedule_post checks privacyLevel against this same list itself and, when it is wrong, returns the levels that would work. Call this only when the user asks what their options are, or you want to offer them a choice.

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": {
      "profile_id": "<profile_id>"
    },
    "name": "sprkly_get_tiktok_posting_options"
  }
}

Input Schema

NameRequiredDescriptionDefault
profile_idYesThe TikTok profile id to query, from sprkly_list_profiles.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety and external-dependency traits are covered. The description reinforces the open-world nature ('fetched live from TikTok') and adds genuinely useful cross-tool behavior — that sprkly_schedule_post performs the same validation and self-corrects — but says nothing about failure modes such as a disconnected profile, expired token, or rate limiting on the live fetch.

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?

Three short sentences, zero waste: what it returns, why you probably don't need it, then the narrow conditions that justify calling it. The negative guidance is front-loaded where it has the most impact.

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

Completeness4/5

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

With only one parameter and no output schema, the description carries the return-value burden and does say what is returned (privacy levels and interaction settings). It stops short of describing the shape of the response or error behavior, which leaves a small gap for a tool with no output schema.

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 description coverage is 100% and the single parameter is fully documented, including its provenance ('from sprkly_list_profiles'). The description adds no additional meaning about profile_id, so this sits at the baseline for schema-complete tools.

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 the specific resource precisely: 'This creator's allowed TikTok privacy levels and interaction settings, fetched live from TikTok.' The agent can tell exactly what comes back, and the scope (per-creator, live) separates it from the many read tools in the sibling list.

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 says when NOT to use it ('You usually do NOT need this before scheduling') and why, naming the alternative (sprkly_schedule_post) that already validates privacyLevel and returns the valid levels on mismatch. It then gives the two positive conditions: the user asks about options, or the agent wants to offer a choice.

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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