Built-in MCP server for the social-flow plugin — direct SNS publishing (Threads·Instagram·Facebook·YouTube) + research search (5 SerpApi tools·SNS issue scout·8 Naver Open API types) + image·video·voice·music generation (OpenAI GPT Image·Veo 3.1·Seedance·Gemini TTS·ElevenLabs·Lyria·Suno) + optional on-device MLX Core / mlx-serve (mlx_*)
14 tools with explicit schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. All tools have names starting with action verbs (portal_*) and descriptions (10-100 chars), meeting baseline naming conventions. However, many parameters lack descriptions or have minimal ones (e.g., 'episodeDir' described only as 'Episode directory path (optional)' without format/path constraints). Output schemas are not documented, LLMs cannot predict response structure. Error handling is minimal; no recovery guidance or categorization. Tool composition is reasonable (each does one thing), but parameter relationships are underdocumented (e.g., episodeId vs episodeDir mutual resolution logic not explained). Zod schemas present but descriptions do not reference constraints (regex patterns, length limits, enum values) in human-readable form.
Create checkpoint of episode on ttalkkakstory portal
Create new episode on ttalkkakstory portal
Acquire, release, or check lease status for episode on ttalkkakstory portal
Restore episode to previous revision on ttalkkakstory portal
Get revision history and diffs for episode on ttalkkakstory portal
Get or update episode status on ttalkkakstory portal
Output schemas not documented. LLMs cannot predict response structure, forcing them to guess what fields are returned and how to chain tools. No documentation of pagination, error responses, or field types.
Parameter descriptions lack constraint details. Zod schemas define regex patterns (e.g., kebab-case channel slug), length limits, and enums, but descriptions do not expose these constraints in human-readable form. LLMs cannot read JSON Schema, they rely on description text.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
Submit render allocation request to ttalkkakstory portal
Choose scenario candidate on ttalkkakstory portal
Pull scenario from ttalkkakstory portal to local directory
Save scenario candidate to ttalkkakstory portal
List storyboards from ttalkkakstory portal
Pull storyboard from ttalkkakstory portal to local directory
Save storyboard to ttalkkakstory portal
Check ttalkkakstory portal workspace configuration and availability
Undocumented parameter relationships. Many tools accept both episodeId and episodeDir (mutually exclusive or resolution logic unclear). No description explains how the tool resolves them or which takes precedence. LLMs will pass both, causing ambiguous behavior.
No error handling guidance. Tools return errors (implied by PortalError type) but descriptions do not explain error conditions, recovery steps, or whether errors are retryable. LLMs cannot self-correct without actionable error messages.
STDIO transport only. Server is not remotely accessible and cannot be used by hosted MCP clients. Limits deployment to local/embedded scenarios.