Bedrock Multi-Model

bedrock-multi-model-mcp

MCP server for AWS Bedrock - text, image, video and embeddings from any model. Use Claude, GPT, Grok, Kimi, Gemma, Mistral, Nova, Qwen, DeepSeek, and more from Claude Code (or any MCP client).

Why: Bedrock already gives one AWS account access to models from eleven text providers, but each one is a different model ID, inference profile, region and request shape. This server puts them behind nine MCP tools and one set of AWS credentials, so an agent can ask a second model for a review, run the same prompt across five models side by side, have a judge model score those answers blind, or generate and edit an image, without leaving the chat or writing SDK code.

At a glance: 33 text models from 11 providers, 3 image models, 13 image-editing services, 1 video model and 3 embedding models, all defined as data in src/data/ and validated at startup. The 26 text models in the 2026-10-18 benchmark answered every call. Since that run, Claude Opus 5.5, Sonnet 5.5 and Haiku 5.5 and, in 0.7.0, twelve models from OpenAI, xAI, Moonshot AI, Google, MiniMax, NVIDIA, Mistral and Qwen joined the registry, while the five models AWS moved to Legacy on 2026-09-30 (Llama 4 Maverick and Scout, Llama 3.3 70B, Devstral 2, DeepSeek R1) left it.

A real bedrock_judge run from 2026-10-18, called through scripts/call-tool.mjs with the arguments in docs/demo/judge-args.json. The output lines are excerpted from that run with the table columns aligned; the full reply also carries the judge's reasoning and all three answers. Llama 4 Maverick has since gone Legacy on Bedrock and left the registry, so swap llama4 for another model to rerun it.

Tools

Supported Models

Model data lives in src/data/ (text-models.json, image-models.json, image-edit-models.json, video-models.json, embedding-models.json) - edit those files to add or remove models without touching code.

Text (Converse API, and bedrock-mantle for Gemma 4)

Gemma 4 is served only on Bedrock's OpenAI-compatible bedrock-mantle endpoint, not through Converse, so those entries carry "api": "mantle" and the server calls their Chat Completions API (https://bedrock-mantle. .api.aws/openai/v1) instead. Every tool works with them, side by side with Converse models in bedrock_compare and bedrock_judge. They need AWS_BEARER_TOKEN_BEDROCK: AWS also accepts SigV4 on that endpoint, but this server does not sign mantle requests.

Listed on Bedrock but not in the registry: GLM 5.3 and Claude Mythos 5.1 (access is gated per account; both failed the 2026-11-01 probe), and Grok 4.3 (answers on bedrock-mantle, but Grok 4.7 supersedes it). AWS lists no Active Meta model.

Bare-tier aliases (fable, claude-opus, claude-sonnet, claude-haiku, gpt-sol, gpt-terra) track the current model in that tier; version-pinned aliases (fable-5, claude-opus-5, claude-sonnet-5, claude-haiku-4.5, claude-opus-4.6, claude-sonnet-4.6) keep the previous generations reachable. AWS announced Claude Opus 5.5 on Bedrock on 2026-09-22, Sonnet 5.5 on 2026-09-28 and Haiku 5.5 on 2026-10-07, so the bare claude-opus, claude-sonnet and claude-haiku now resolve to the 5.5 models.

temperature is the one thing that differs across those entries. Anthropic deprecated temperature / top_p / top_k for Claude Opus 4.7 and later, where a non-default value returns a 400, so text-models.json marks those entries noTemperature and this server omits the parameter for them - the temperature argument is accepted but has no effect. The three 5.5 models return the same 400 (probed 2026-10-21), and so do GPT-6.1 Sol and Terra, Grok 4.7 and Kimi K3 ("This model doesn't support the temperature field", probed 2026-11-01). Gemma 4 and the other open-weight models take it. Claude Haiku 4.5, Opus 4.6 and Sonnet 4.6 still honor it, so pin claude-sonnet-4.6 or claude-haiku-4.5 if you need to set it on a Claude model.

The maxTokens value in text-models.json is the default output cap this server sends when a call omits max_tokens - deliberately below the model's own ceiling (Anthropic documents 128K max output for Fable 5.1 and the 5.5 and 5 models, 64K for Haiku 4.5). Pass max_tokens to go higher on a single call.

Structured output and effort

schema on bedrock_ask and bedrock_compare sends a JSON schema as Converse outputConfig.textFormat, so the reply is JSON that matches it, shown pretty-printed. Object schemas need "additionalProperties": false; Bedrock rejects numeric and string length constraints (minimum, maxLength and the like) with a 400. Probed on every registry model (2026-10-21, and 2026-11-01 for the models added then): all 10 Claude models, GPT-6.1 Sol and Terra, both GPT-OSS models, Grok 4.7, Kimi K3, Mistral Large 3, Ministral 3, all three Qwen3 models, DeepSeek V3.2, MiniMax M2.5, Nemotron 3 Super and the three Gemma 4 models accept it (structured_output in bedrock_list_models; for Gemma it is sent as Chat Completions response_format). Nova and Pixtral answer "This model doesn't support the outputConfig field", and Magistral Small accepts the field but ignores the schema, so this server refuses the call before it is billed. GPT-OSS puts a stray character before its JSON (B{...}); the reply parser skips anything before the first brace.

effort (low, medium, high, xhigh, max) sets the thinking depth and token spend through Converse outputConfig.effort. Every Claude model except Haiku 4.5 takes it, and so do GPT-6.1 Sol and Terra, Grok 4.7 and Kimi K3 (probed 2026-11-01). Gemma 4 takes low to xhigh as Chat Completions reasoning_effort and answers max with a 400 ("not supported with this model"). Claude Opus 5.5 and Haiku 5.5 default to medium, the other Claude models to high. On 2026-10-21, "How many primes are below 100?" cost Haiku 5.5 3 output tokens at low and 60 at max, with the same answer. bedrock_ask rejects effort for a model that does not take it; bedrock_compare and bedrock_judge apply it only to the candidates that do, so one value can tune the Claude answers in a mixed list.

Image (InvokeModel API)

Stability models take an aspect ratio, not pixel dimensions. The width and height arguments only select the closest supported ratio (1:1, 16:9, 9:16, 3:2, 2:3, 4:5, 5:4, 21:9, 9:21); the model picks the output pixels. The size reported by bedrock_generate_image is read back out of the saved PNG, so it is the real output size rather than what you asked for.

Image editing (InvokeModel API)

bedrock_edit_image takes a local PNG, JPEG or WebP (image_path) and an operation. Each operation is one of Stability AI's image services, called through its US inference profile (us.stability.*) from us-west-2.

| `sty