"Understanding Inference Models" matching MCP servers:

Enables any agent to purchase Claude and Grok inference metered per token, settled in BSV, via the hosted gateway without holding keys or running models locally.

Read-only MCP server for GenieLocker that exposes tools for service status, commercial inventory, live quotes, recipe search, and credit pricing, without enabling purchases or model inference.

Exposes InferenceIndexer's live and historical inference pricing as MCP tools, enabling search of models, providers, price comparisons, and SIT index trends.

Enables searching, comparing, and inspecting AI models by pricing, context window, and capabilities via the models.dev catalog.

Official remote MCP server for RunComfy. 31 tools across three products: Serverless API (ComfyUI) deployment management and async inference, Model API hosted-model inference, and Trainer API LoRA training jobs.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables governance-grade AIops for GPU inference clusters with root-cause analysis, metrics, and policy-governed operations for vLLM and Ray.

Provides an MCP-compatible profiler for measuring edge and on-device LLM inference latency, including TTFT, TPOT, tokens-per-second throughput, and P50/P90/P99 jitter distributions. It enables agents and developers to run these telemetry benchmarks from MCP clients such as Claude Desktop, Cursor, and Windsurf.

Provides A-share causal analysis capabilities including event studies, counterfactual validation, causal graph learning, and econometric inference through MCP, enabling clients to run complete causal inference workflows.

Enables AI agents and MCP clients to estimate VRAM footprint and tensor parallelization requirements for quantized llama.cpp models, returning structured outputs without external dependencies.

AI-native tooling and MCP integrations for understanding enterprise reports

Enables browsing TokenLab models, comparing pricing, and generating cURL examples for various AI endpoints.

Nectar is a stdio MCP server for Pollinations image and video generation. It gives MCP clients a small, focused toolset for generating images, editing images, generating videos, listing available image/video models, and checking pollen balance.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

Enables AI agents to make pay-as-you-go AI inference requests through x402 with automatic Solana USDC payments, no API keys or registration, while enforcing configurable spending limits.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Compiler introspection for the Hirð language (typed actors and effect rows on the BEAM): type inference, definition lookup, effect-row explanation, IR fragments, actor protocol and supervision graphs, and token-budget-aware symbol summaries.

Enables AI assistants to access live mining data for 126+ proof-of-work networks and 770+ hardware models, including network difficulty, hashrate, solo-mining odds, cost of production, hardware profitability, and mining news.

Delegates coding tasks to your locally installed pi CLI, inheriting providers, models, and extensions, with persistent sessions and cancellable calls.