OpenRouter cross-provider model catalog (200+ models normalized across 50+ inference providers). Each entry has per-token pricing (prompt + completion + image + request), context window, modality (e.g. text+image->text), instruct_type, tokenizer, top provider metadata (max_completion_tokens, mode...
Single-target operation
Part of the TensorFeed MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call get_openrouter_models to retrieve information from TensorFeed without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though get_openrouter_models only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
tools:
get_openrouter_models:
rules:
- action: allowSee the full TensorFeed policy for all 42 tools.
Agents calling read-class tools like get_openrouter_models have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.
OpenRouter cross-provider model catalog (200+ models normalized across 50+ inference providers). Each entry has per-token pricing (prompt + completion + image + request), context window, modality (e.g. text+image->text), instruct_type, tokenizer, top provider metadata (max_completion_tokens, moderation flag), and supported_parameters. Use this to find the long tail of OSS models on cloud inference, including the cheapest model for a given workload, the model with the largest context window, or the free-tier set. Pairs with get_model_pricing (curated frontier-lab catalog). Refreshed daily at 14:00 UTC. Free, no auth.. It is categorised as a Read tool in the TensorFeed MCP Server, which means it retrieves data without modifying state.
Add a rule in your Intercept YAML policy under the tools section for get_openrouter_models. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the TensorFeed MCP server.
get_openrouter_models is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_openrouter_models rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the Intercept policy for get_openrouter_models. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
get_openrouter_models is provided by the TensorFeed MCP server (@tensorfeed/mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic policy on every MCP tool call. Per-identity grants. Full audit log.