"Tools for extracting structured data from web pages for LLM use" matching MCP servers:

Enables Claude to query other LLMs like ChatGPT, Perplexity, and Gemini for verification and comparison, eliminating manual copy-pasting.

Enables OpenAI-compatible clients to access external tools, APIs, repositories, and data sources through MCP with allowlisting, approval, and argument validation, deployable as a FastAPI service or Python library.

Provides Dify apps with a secure, app-scoped MCP tool catalog and action gateway for authorized FlowFlox operations, enabling intent-driven planning and fixed operational processes without exposing global credentials.

Enables agents to detect and redact PII and secrets in text, HTML, and PDF documents before they reach an LLM, with reversible placeholder, mask, or hash redaction, and to list supported detectors and redaction styles.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Provides vision capabilities to Reasonix, enabling analysis of images/videos, OCR, and comparisons via OpenAI-compatible vision models.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

Enables ChatGPT to delegate writing, analysis, second opinions, and Japanese text polishing to Gemini through a single ask_gemini tool, without copying text to Gemini's web interface. Self-hosted on Cloudflare with your own Gemini API key and Google login.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

Translates natural language commands into structured geometric operations for a simulated CAD engine using LLMs. It enables users to create shapes, modify dimensions, and perform 3D operations like extrusion through the Model Context Protocol.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.

Bridges DeepSeek (text-only) with vision models via MCP, enabling image description and compression using any OpenAI-compatible visual API.

Enables MCP clients to extract validated typed records from unstructured text using named schemas, with schema listing and extraction tools. It returns validated JSON or tool errors, optionally using an offline parser or an OpenAI-compatible model with retries on validation failures.

Enables MCP-capable agents to run TypeSafe's Jev judgment model as typed yes/no, choice, and score tools, with calibrated probabilities, confidence thresholds, escalation for uncertain or non-judgment tasks, and an optional action gate that fails open.

Enables cost-aware LLM routing over MCP with verification-based cascading, offering tools for routing queries, resuming escalated approvals, estimating costs, comparing policies, and explaining routing decisions, plus read-only resources and a policy-selection prompt.

Enables MCP clients to hand fast, no-text-output decisions to Jev, reducing latency and token usage while escalating uncertain cases back to the LLM.

Token cost math for LLM API calls: current per-million-token rates for 69 models across 17 providers, with local arithmetic for estimates, comparisons and monthly budgets. Rates are verified and date-stamped.

A local-first LLM routing MCP server that keeps sensitive data on your own models, with fail-closed privacy and manager-worker delegation, exposing route and complete tools to any MCP client.

Provides real-time LLM pricing and availability data as an MCP server, enabling AI agents to make optimal model routing decisions at inference time with cited pricing sources.

Lets MCP clients delegate private or bulk text tasks to a local OpenAI-compatible LLM while keeping data on the machine.