OpenRouter Agents MCP Server

Here is the **critical synthesis** of all sub-queries for the ORIGINAL RESEARCH QUERY: *"Design for routing to vision-capable models for image-conditioned research; include model detection via dynamic catalog and graceful degradation if not supported."* --- ## 🔎 Sub-Query Statuses Overview - **Sub-Query 1**: SUCCESS - **Sub-Query 2**: SUCCESS - **Sub-Query 3**: SUCCESS - **Sub-Query 4**: SUCCESS (but limited info; mostly inferred best practices) - **Sub-Query 5**: SUCCESS - **Sub-Query 6**: SUCCESS - **Sub-Query 7**: SUCCESS (partial contradiction on exact JSON schema details) - **Sub-Query 8**: SUCCESS - **Sub-Query 9**: SUCCESS (but fallback is mostly application-side, not declarative in docs) No FAILED sub-queries — all areas have at least partial answers. --- ## 🧩 Critical Comparison Across Sub-Queries ### Sub-Query 1 — Catalog Listing of Vision Models - **Consensus**: The OpenRouter **Models Catalog** (https://openrouter.ai/models) lists modality metadata. - **Contradiction**: - One model claimed "no dedicated filter or labeling for vision" (z-ai/glm-4.5v). - Another model confirmed **“Vision” filter and tags (e.g., GPT-4V, Qwen-VL)** exist. - **Synthesis**: OpenRouter *does* list modalities (text, image, etc.), and filtering for vision is possible. The discrepancy may come from differences in UI vs. API metadata. - **Confidence**: High that OpenRouter exposes modality information. --- ### Sub-Query 2 — Dynamic Catalog API - **Consensus**: OpenRouter exposes a `/models` endpoint and metadata includes `modalities` (and possibly `supports_vision`). - **Contradiction**: One answer inferred fields; another confirmed `modalities` field explicitly. - **Synthesis**: `/models` should be called dynamically. Client must filter on `modalities=["image"]` or boolean flags (`supports_vision`). Filtering by modality is **client-side**, not server-side. - **Confidence**: High that `/models` returns modalities info; Medium on exact field names (since direct schema isn’t in provided snippets). --- ### Sub-Query 3 — Routing and Fallback - **Consensus**: OpenRouter provides **Model Routing** and **Provider Routing**, supporting fallback if a model/provider is unavailable. - **Discrepancy**: Does fallback extend to *modalities* specifically? - One view: No explicit modality fallback in docs. - Another: Fallback chains and presets are possible. - **Synthesis**: Fallback is robust for availability (provider/model outages). But **modality-based fallback (vision → text)** is not explicitly documented — must be implemented application-side. - **Confidence**: High on availability fallback, Low on modality fallback. --- ### Sub-Query 4 — Best Practices for Routing Logic - **Consensus**: Use the `model` parameter to explicitly target multimodal models, and provider-routing/presets for optimization. - **Gap**: Docs don’t provide explicit best practices for vision handling. - **Synthesis**: Developers should: - Use `modalities` metadata for detection, - Prefer known vision models, and - Configure fallbacks using presets or app logic. - **Confidence**: Medium — mostly inferred best practices, weak in official doc evidence. --- ### Sub-Query 5 — Major Model Families with Vision - **Consensus**: - **OpenAI**: GPT-4V, GPT-4o (vision) - **Anthropic**: Claude 3 series (Opus, Sonnet, Haiku) - **Google**: Gemini 1.0/1.5 Pro Vision - Confirmed by official provider docs. - **Confidence**: High. --- ### Sub-Query 6 — Structured Routing Flow for Research Applications - **Consensus**: - Dynamically detect vision models via `/models`. - Apply provider prioritization using metadata (e.g., `provider`). - Gracefully fallback to text-only models if vision is unsupported. - **Details**: Both answers agree on using app-level logic for fallbacks since OpenRouter does not enforce modality-aware fallback. - **Confidence**: High. --- ### Sub-Query 7 — JSON Schema for `/models` - **Contradiction**: - One source: No confirmed schema, speculative fields (`modalities`, `supports_image`). - Another source: Explicitly states `modalities` and `supports_vision` exist in some models. - **Synthesis**: *Likely schema includes at least*: - `modalities: ["text","image",...]` - `supports_vision: true/false` (but may not always be present). - **Confidence**: Medium-High — schema presence inferred from observed model responses, but not explicitly documented in provided materials. --- ### Sub-Query 8 — Provider Official Capability Docs - **OpenAI**: GPT-4o, GPT-4 Turbo Vision ([docs](https://platform.openai.com/docs/models/gpt-4o), [vision guide](https://platform.openai.com/docs/guides/vision)) - **Anthropic**: Claude 3.5 Sonnet Vision ([docs](https://docs.anthropic.com/claude/docs/models-overview#claude-35-sonnet)) - **Google**: Gemini Vision ([docs](https://ai.google.dev/docs/models/gemini); [multimodal prompts](https://ai.google.dev/docs/multimodal_prompts)) - **Consensus**: All providers **explicitly document image input support.** - **Confidence**: High. --- ### Sub-Query 9 — Customizing Fallback Behavior - **Consensus**: OpenRouter supports flexible **routing**, but lacks declarative fallback for modality. - **Details**: - Users can implement “aliases” or “presets” to simulate fallback chains. - Modality-aware fallback **must be implemented client-side**. - **Confidence**: High for provider/model fallback; Low for modality-specific fallback. --- ## 📌 Synthesized Research Answer (Integrated) For designing **routing to vision-capable models in a research application with OpenRouter**: 1. **Dynamic Catalog Detection** - Use OpenRouter’s `/models` endpoint ([docs](https://openrouter.ai/docs), [models catalog](https://openrouter.ai/models)) to programmatically retrieve models and their metadata. - Check **`modalities`** (look for `"image"`) and **`supports_vision`** flags (if present). - Vision-capable models currently documented: - OpenAI **GPT-4o / GPT-4V** ([OpenAI Docs](https://platform.openai.com/docs/models/gpt-4o); [Vision Guide](https://platform.openai.com/docs/guides/vision)) - Anthropic **Claude 3.x / Claude 3.5 Sonnet** ([Anthropic Docs](https://docs.anthropic.com/claude/docs/models-overview#claude-35-sonnet)) - Google **Gemini Pro Vision, Gemini 1.5 Pro** ([Google AI Docs](https://ai.google.dev/docs/models/gemini)) 2. **Routing & Prioritization** - Implement **provider prioritization** using the `provider` field in metadata. - Example policy: Prefer OpenAI → Anthropic → Google → fallback open-source (Mistral/LLaVA/Qwen-VL). - Use OpenRouter features like **Model Routing** and **Provider Routing** ([docs](https://openrouter.ai/docs)). 3. **Graceful Fallback** - OpenRouter natively handles **fallback for provider/model outages**, but **not modality-specific fallbacks**. - For vision → text fallback, implement application-layer logic: - If input includes images, attempt a vision-capable model. - On failure (model missing or rejecting input), degrade gracefully by retrying with the best available text-only model. 4. **Recommended Practices** - Use prescriptive **model aliases** or **presets** to configure fallback sequences. - Validate image inputs (size, MIME type) before routing. - Cache model lists from `/models` periodically. - Use **structured outputs** and **prompt caching** for consistency. - Always confirm capabilities against **official provider capability docs** (see URLs above). --- ## ✅ Final Confidence Assessment - **Vision model detection via `/models`:** High - **Provider prioritization:** High - **Graceful fallback (availability):** High - **Graceful fallback (modality):** Medium (client-side only) - **JSON schema exactness:** Medium (observed but not fully doc-verified) - **Major model family vision support (OpenAI / Anthropic / Google):** High --- 📌 **In sum:** OpenRouter exposes the necessary metadata (via `/models`) to detect vision-capable models (e.g., GPT-4o, Claude 3.5, Gemini), allows provider prioritization using its routing system, but requires **application-side logic** to degrade gracefully when vision modalities are unsupported. Official provider docs confirm multimodal capabilities, so a robust research workflow should combine OpenRouter’s routing with client-side checks and controlled fallbacks. --- Would you like me to **draft a reference implementation in Python** showing a full routing pipeline (vision detection, provider prioritization, and fallback switching)?