"Understanding Cache for Prompt Engineering" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Semantic caching MCP server for AI agent tool calls, providing exact and similarity-based cache lookup, store, invalidation, and metrics via MCP tools.

Exposes a protected, stateless MCP endpoint over a Cloudflare-hosted camera pipeline, letting agents list active devices and pull hour- or minute-level JPEG contact sheets for any time window. Agents can also enumerate exact capture timestamps, compare 2–10 chosen frames in a single derived image, fetch unmodified original frames, and generate temporary download links.

MCP server for diagnosing Apollo InMemoryCache normalization defects, providing cache analysis and repair planning tools for React Native apps.

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

Enforces team engineering standards across Git, code review, Rails, frontend, deployment, incidents, observability, API design, database, ADRs, and technical debt, with tools for branch name and commit message validation.

Analyzes, generates, simulates, and optimizes caching strategies across HTTP, CDN, and browser layers for AI agents.

A local STDIO MCP server that bridges MCP clients to the Codex CLI by sending instructions to a configured workspace, exposing task run, status, and result tools with a read-only sandbox and no remote transport.

A persistent, tree-sitter-backed code knowledge cache MCP server that reduces token usage by storing parsed structure and enabling fast symbol lookup, inheritance graph, call graph, and semantic search.

MCP server that turns coding workflows into explicit execution graphs (DAG) with deterministic command execution, human-in-the-loop gates, and tamper-proof run history for Claude Code.

Enables repository-aware lifecycle management and controlled delegation of coding tasks to trusted worker harnesses via MCP, with execution isolation, recovery, and verified handoff.

Provides coding agents with a shared, Markdown-based engineering knowledge base to search, capture, create, and update internal conventions, API details, infrastructure configs, and development setup via lightweight MCP tools.

Lets MCP hosts search the public Image3 GPT Image 2 prompt gallery, fetch complete prompts, list categories, and turn rough ideas into structured prompt briefs.

An MCP server that exposes three tools for auditing, scaffolding, and triaging agent intent specs against a unified template, helping prevent intent failures in AI agents.

An MCP server that gives an AI assistant a set of real mechanical-engineering tools. Provides deterministic tools like material properties, beam analysis, unit conversion, and curve fitting for engineering calculations.

Enables deterministic, read-only design of OpenSora video prompts and shot plans, including prompt diagnosis and access to workflow guides, without generating videos or requiring API keys.

Local MCP server that uses a local Ollama model to rewrite rough prompt drafts into structured, optimized prompts for paid APIs, saving tokens and improving output quality.

Enables prompt optimization loops and regression test suites for Claude Code, with a companion web UI for real-time visualization of scores and prompt revisions.

The control plane for AI prompts that scores, enforces policy, locks config, and audits every prompt decision.