"DNS Doctor" matching MCP servers:

Enables diagnosing web hosting and server issues by running live HTTP, SSL, DNS, and security-header checks alongside a searchable troubleshooting knowledge base.

Production-grade MCP server providing deterministic evaluation, AST sandboxed execution, DNS routing audit, and runtime guardrails for autonomous LLM agents.

MCP server that provides a citation-first memory layer for AI agents, enabling verified search across Git-backed repositories and optional conversation memory. It exposes tools for doctor, sync, search, get, init, and ingest, returning JSON results with verified citations or abstain status.

Diagnoses web hosting and server issues by combining live HTTP, SSL, DNS, and security-header checks with a searchable knowledge base of troubleshooting notes, enabling MCP clients to investigate symptoms and explain root causes.

Enables local research of the entire English SRD 5.2.1 through hybrid vector and full-text search, exact cited rule retrieval, and deterministic constraint checks for actions, movement, spellcasting, concentration, damage, rolls, and characters.

An MCP server implementation that enables interaction with the Unstructured API, providing tools to list, create, update, and manage sources, destinations, and workflows.

Enables Claude and other LLMs to search through any published MkDocs documentation site using the Lunr.js search engine, allowing the AI to find and summarize relevant documentation for users.

Enables MCP clients to delegate analytics questions, sample-size calculation, knowledge retrieval, and metric listing to a deterministic statistical engine over stdio JSON-RPC.

Enables an LLM to search the D&D 5e (2014) rule catalogue and fetch any wiki page as Markdown.

Integrates CRM data with AI agents to manage leads, track pipeline statistics, and search price catalogs via Supabase. It enables automated lead creation, stage updates, and history tracking through natural language commands.

A Python backend implementing the Model Context Protocol with Azure OpenAI integration, enabling applications to interact with LLMs through a standardized interface with streaming capabilities.

Enables AI agents to perform grounded web research with injection resistance, claim verification, and cost-aware routing through MCP tools like web_search, fetch_url, extract_claims, and check_grounding.

Enables intent-based semantic search over external design system documentation, allowing MCP-compatible AI assistants to retrieve relevant design tokens, component architectures, and code patterns via a single tool.

Enables interaction with the Unstructured API to manage data workflows, including creating and managing source connectors (S3, Azure, Google Drive, etc.), destination connectors (Weaviate, Pinecone, MongoDB, etc.), and workflows for document processing and data pipelines.

Exposes code search and file reading tools over the Model Context Protocol, enabling any MCP-compatible client to query a codebase with natural language.