agnes-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AGNES_API_KEY | Yes | Your Agnes AI API key | |
| AGNES_BASE_URL | No | Base URL for the Agnes API (default: https://apihub.agnes-ai.com/v1) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| agnes_modelsA | List all available Agnes AI models (text/chat, image, video) with their limits. Useful to discover model names before calling the other tools. |
| agnes_chatA | Capability 1 — Text generation & reasoning. OpenAI-compatible chat completions. Supports multi-turn conversation, system prompts, tool/function calling, streaming, and Thinking mode. Models: agnes-2.0-flash, agnes-1.5-flash. agnes-2.0-flash supports up to 1M-token context (set max_tokens up to 1048576). Vision is also available here by passing image_url content parts; use agnes_vision for a simpler image-understanding interface. |
| agnes_visionA | Capability 4 — Multimodal understanding. Send one or more images (public URLs or data URIs) plus a text instruction and the model describes, analyzes, OCRs, or answers questions about the visual content. Models: agnes-2.0-flash, agnes-1.5-flash (both accept image_url input). |
| agnes_imageA | Capability 2 — Image generation & editing. Text-to-Image and Image-to-Image (edit/transform/multi-image compose). Models: agnes-image-2.1-flash, agnes-image-2.0-flash. Required: model, prompt, size. For image-to-image, pass 'image' (array of public URLs or data URIs). Note: image dimensions must be multiples of 16. Put response_format inside extra_body (handled automatically here). |
| agnes_video_createA | Capability 3 — Video & audio-video generation (async). Create a video task from text, a single image, multiple images, or keyframes. Model: agnes-video-v2.0. Returns task_id and video_id. Poll the result with agnes_video_query or agnes_video_wait. Highlights: width/height accept up to 4K (3840). Video dimensions must be multiples of 64. num_frames ≤ 441 and must equal 8n+1 (e.g. 81, 121, 161, 241, 441). frame_rate 1–60. seconds = num_frames / frame_rate. |
| agnes_video_queryA | Query the status/result of an async video task. Recommended: pass video_id (returned by agnes_video_create). Legacy: pass task_id. When status is 'completed', the final video URL is in remixed_from_video_id. |
| agnes_video_waitA | Poll an async video task (by video_id) until it reaches a terminal state (completed/failed) or times out. Returns the final result including the video URL when completed. Video generation can take tens of seconds to minutes. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool targets a distinct capability (chat, image, video creation, video query, video wait, vision, model listing). Slight overlap between agnes_chat and agnes_vision regarding vision tasks is clarified in descriptions, so agents can disambiguate.
All tools use 'agnes_' prefix, but naming patterns vary: some are single nouns (chat, image, models, vision) while others use verb_noun compounds (video_create, video_query, video_wait). The convention is not fully consistent but still readable.
7 tools is well-scoped for an AI API server covering text, image, video generation, and model discovery. Each tool serves a clear purpose without unnecessary overlap or missing essential operations.
Core workflows for text generation, image generation/editing, video generation (async with status polling), and vision understanding are covered. Minor gaps include no explicit tool for deleting or updating generated content, but these are typical for generation APIs.