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Generate AI Media

generate_ai_media

Generate AI images or videos using approved media providers.

Supported providers:

  • heygen-mcp: HeyGen Direct API or MCP video/avatar generation

  • codex-oauth-image: Codex OAuth image generation for gpt-image-2

Returns a job ID that can be polled for status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesCreative prompt for generation
providerYesAI media provider
media_typeNoType of media to generate (default: video)
parametersNoProvider-specific parameters (e.g. duration, resolution, style, aspectRatio)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it reveals the asynchronous nature by stating it 'Returns a job ID that can be polled for status.' It also provides provider-specific details and implies access restrictions via 'approved media providers.' Annotations indicate a write operation and non-idempotency, which is consistent with generation; no contradiction exists.

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?

The description is compact and well-structured: it starts with a clear purpose sentence, uses a scannable list for providers, and ends with the key return contract. Every sentence earns its place without redundant filler.

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 covers purpose, providers, and return value, but the nested 'parameters' object is entirely open-ended with no examples or guidance. Since there is no output schema, the job ID return is stated, but provider-specific invocation details and error/status behavior are left implicit. This is adequate but has clear gaps for a complex generation tool.

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 enriches the provider parameter with meaningful explanations (e.g., 'HeyGen Direct API or MCP video/avatar generation' and 'Codex OAuth image generation'). It also mentions the job ID return, which clarifies what the prompt and provider parameters are used for, though it does not detail the nested parameters object.

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 gives a specific verb and resource ('Generate AI images or videos') and clearly distinguishes this tool from siblings like generate_content or upload_media by specifying media generation and listing supported providers. It also states the return type (job ID), further clarifying its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool—when AI-generated images or videos are needed—and lists supported providers, but it does not explicitly explain when not to use it or mention alternatives like generate_content for text or upload_media for existing media. The context is clear but exclusion/alternative guidance is missing.

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

B3/5.0
Disambiguation2/5

With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.

Naming Consistency3/5

Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.

Tool Count1/5

148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).

Completeness3/5

The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.

Resources