"Information about Context7" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Give AI assistants secure access to your organization's structured business data. Search records, create and update records, retrieve schema information, and manage workflow states using natural language. You need two values for every request: x-api-key — your Web Data Forms API Key x-group-id — your Web Data Forms Group ID You can find these in your Web Data Forms accounts group->information page. Preferred method: request header When possible, pass the credentials as HTTP headers: x-api-key: <your-api-key> x-group-id: <your-group-id> This is the preferred option because it keeps credentials out of the URL and is more secure. Fallback method: query parameters If your MCP client does not support custom headers, the server also accepts the credentials as URL query parameters. Example: https://mcp.webdataforms.com?x-api-key=abc123&x-group-id=xyz456 Detailed information here: https://github.com/Web-Data-Forms/mcp-server-docs/blob/main/README.md
Zero-Ops deploy of a private AI coding workspace onto your own VPS — straight from your AI chat. Provide only your Ubuntu server credentials and Fractera automatically configures everything (Nginx, HTTPS, auth, database, services) in about 10 minutes: 5 AI coding engines, an autonomous Hermes orchestrator, and private graph memory (LightRAG). No terminal, no DevOps. IP-first and free; a custom domain with HTTPS is an optional later step.
People + life-in-days knowledge for AI agents. Public MCP; x402 on Base; OAuth for private tools.
Neutral verification registry for femtech (women's health) information sources. It indexes sources by provenance, authority tier, jurisdiction, and machine-readable compensation disclosure. No diagnosis, no efficacy claims, no referral fees.
Describe what's going wrong with your AI; get the named technique both of you can read. No auth.
Search and read public Wikivibe articles about AI coding, agents, MCP, GEO, bots and deployment.
Authoritative information about Jennifer Rebholz, Arizona personal injury attorney and trial lawyer.
Intelligent context infrastructure for AI teams: knowledge graph, sessions, tasks, documents.
A wiki about your life that writes itself. Save from any AI chat, recall it in the next.
Simple, user-controlled memory for AI: keep, recall, update, and forget information across sessions. Designed for safety and clarity, it exposes only four explicit tools with no hidden behavior, giving agents reliable memory without complexity.
Christian source evidence for AI agents, with provenance, tradition coverage, and citation checks.
Rafter holds a team's durable knowledge — skills, agents and memory files, each versioned — and serves it to AI tools over MCP. Agents search across the team's artifacts before answering questions about how the team works or what was decided, fetch full artifact text along with its citation edges (cites, cited_by, links) to explore related material, and write new learnings back as memory. Also covers workspace, team and membership management.
Universal memory for AI agents and tools. Save, organize and search context anywhere.
Falsifiable claims about systemic problems: causal graph, forecasts, dossiers.
Ask about an operator's work, reusable patterns, availability and offer. Typed tools, eval-gated.
Your portable context layer — load it into any AI.
Personal finance, bank account, and shared memory connector for Claude, ChatGPT, Gemini Spark & more
A portable context layer for MCP-speaking AI clients. Connect any client to one endpoint and it boots with your containers — structured context, live work state, and accumulated knowledge — carried across every client you use.
Stop re-explaining yourself to Agents. Give it the right context, right when needed.
MCP memory server with shared team workspaces, typed knowledge chunks (decision, finding, convention, state, question, reference), role-based access, and cross-tool support for Claude, Cursor, and Codex. The only MCP memory server built for engineering teams. Features automatic deduplication, two-layer retrieval (LLM KB selection + hybrid vector/BM25/RRF fusion), a web dashboard with knowledge graph visualization, and attribution tracking. Zero server-side LLM costs.
