"React Hook Form" matching MCP servers:

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

Enables an agent to create, read, and list the Markdown cards in a user-owned card book, capturing people, events, places, things, and in-jokes as durable canon. Cards are stored as plain files with YAML frontmatter that the user can inspect and edit, while keyword-triggered surfacing is handled separately by a gateway or hook.

Enables read-only, deterministic MCP access to scholarly Sanskrit data such as sūtras, dhātus, śabdas, and stored rūpāṇi. Supports offline search, retrieval, form lookup, pagination, and provenance-aware evidence for downstream agents.

Enables writers and AI agents to preserve continuity in long-form fiction by maintaining a narrative knowledge graph and exposing MCP tools for querying outlines, entities, references, and consistency diagnostics.

Provides 10 structured reasoning strategies (Chain of Thought, ReAct, Tree of Thoughts, etc.) for complex problem-solving with session persistence, branching, and tool integration capabilities.

Temporal knowledge graph for codebases that captures decision traces, links test failures to code changes, learns co-edit patterns, predicts regression risk, and enforces learned constraints at the edit boundary via a PreToolUse hook.

Enables long-form web novel writing workflows by ingesting chapters into a state database, running deterministic consistency checks, querying entity histories, replaying extraction results, and retrieving conflict reports via MCP tools.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables AI-driven long-form novel creation and management, including chapter generation, character and timeline tracking, semantic memory retrieval, version savepoints, and deep consistency checking across multiple projects through MCP tools, with support for local LM Studio or any OpenAI-compatible API.

Gives AI assistants a persistent key/value state store backed by MariaDB, exposing tools to create, read, update, soft-delete, and list named variables with descriptions and long-form content. Runs as a remote OAuth 2.1-authenticated MCP endpoint, so state survives across sessions.

Turns saved X posts and long-form articles into a private, searchable context base accessible via MCP. Lets coding agents import, search, read, and enrich the library using the user's existing browser session.

Zero-tool-call codebase intelligence for Claude Code and MCP clients. Automatically injects the right code context, functions, callers, and call chains, before the LLM starts thinking. Replaces 4-6 grep/read round-trips with a single 5ms hook injection, cutting token usage by 3-8x.

Helps LLMs maintain coherent timelines, track events, and manage characters across long-form narratives for fiction writing or historical accounts.

Enables semantic search over the CCI Black Book, a scanned grow manual, retrieving both text and visual content with cited evidence packs for AI assistants.

Persistent, semantically-searchable memory for AI agents using local PostgreSQL, pgvector, and Ollama embeddings, exposed via MCP with hybrid retrieval, knowledge graph, and auto-recall hook.

Enables AI assistants to save and recall information from files or free-form notes using natural language, acting as a long-term memory system.

Enables MCP clients to create and manage long-form story projects with isolated databases, persistent drafts, canonical continuity checks, controlled updates, and local semantic retrieval through MCP tools, resources, and prompts.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

A local-first memory engine for AI agents that stores embeddings in polar-quantized form in a single SQLite file, enabling private, compact, and human-like recall.