Mcp

A GEO AI search optimization agent is a software system that automatically audits your content for AI-search citability, rewrites it to maximize visibility in LLM-generated answers, and verifies that ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews actually cite you. You build it by assembling open-source building blocks: a 0-100 readiness scorer, an AutoGEO-style rule extractor, a rewrite engine, and a citation verifier wired together over an MCP-compatible loop. This guide walks through each step. ...

A planner-executor agent workflow MCP orchestrator splits a hard task into two roles: a planner that decomposes the goal into a step-by-step plan, and one or more executors that carry out each step with tool access, all connected through Model Context Protocol (MCP) tool servers. This pattern handles open-ended, path-dependent work that a linear pipeline cannot, because the planner can re-plan mid-run based on what executors return. It is the canonical multi-agent orchestration architecture for tasks where the full sequence of steps is not known in advance. ...

Vecline is a bit-exact SVG and document toolkit that converts flat art — logos, icons, UI, screenshots, pixel art — into editable SVG with perfectly measured output: SSIM 1.0000, PSNR infinity, zero differing pixels. Unlike most vectorizers that claim close-enough accuracy, Vecline re-renders every SVG it produces and scores it against the source, and its lossless mode fails rather than returning a near-miss. Built for JavaScript as a zero-dependency core with an MCP server, it is the honest answer to overhyped “100% accuracy” vectorizers. ...

PromptShield is a prompt injection scanner that audits repositories for malicious instructions and hidden Unicode before AI agents ever consume them. It statically scans agent instruction files, detects zero-width character attacks and taint patterns, and maps findings to the OWASP Agentic Top 10 (2026) so teams can block supply-chain and prompt-injection risk in CI/CD. What Is PromptShield and Why Repo Scanning Matters PromptShield belongs to a fast-growing category of tools built to answer one uncomfortable question: can you trust the instructions your AI agent is about to read? As coding agents like Claude Code, Codex, and Cursor become the default way teams ship software, the files those agents read — READMEs, AGENTS.md files, MCP configurations, and skill definitions — have become a new attack surface. ...

Rungraph is an open-source, zero-instrumentation tool that reads the agent session transcripts already on your disk and renders them as interactive graphs. With a single npx rungraph command, it visualizes the orchestrator, subagents, and tool calls of any Claude Code, Codex, Hermes Agent, opencode, or Cursor run — then lets you ask, in plain language through an MCP server, “what happened?” and lights up the exact nodes the answer refers to. ...

Coding agents lose context across repositories because a session is scoped to one repo while the system under investigation spans many. WCTX is a local-first MCP server that solves this by importing finished coding-agent sessions into a SQLite + FTS5 store, linking repositories with typed relations, and exposing seven tools that let a fresh session search prior evidence with git-based freshness verdicts. No cloud account, no embeddings, no transcript upload. ...

If you have ever run two AI coding tools on the same repository, you have already met the problem PeerBridge MCP solves: left to themselves, Claude Code, Codex, Cursor, and Gemini edit the same files as if they were separate developers who never talk to each other. PeerBridge MCP is a local-first coordination server that gives those peers a shared, auditable state layer through the Model Context Protocol, so every read, write, and commit is tracked locally and drift is caught before it corrupts your codebase. In short, it turns a group of independent AI editors into a coordinated team with a written record of everything they touched. ...

ContextNest vs Mem0 vs Zep is the wrong framing: these three are complementary layers of the same memory stack, not competing products. Zep provides session log memory, Mem0 handles personalization memory, and ContextNest governs corporate knowledge — and production agents typically need all three. A single memory database is the most common architectural pitfall, because it cannot serve session continuity, user personalization, and compliance-ready knowledge at the same time. This guide dissects each layer, compares them head-to-head, and shows how to stack them. ...

Open-source memory for coding agents synced over SSH lets you carry your AI assistant’s context, preferences, and project knowledge across every machine you work on — without a cloud service. The most portable approach is a git-backed markdown store: symlink your agent’s memory files, push and pull over SSH, and let auto-sync hooks handle the rest. This guide walks through the three proven methods and how to pick the right one. ...

MCP observability turns the Model Context Protocol into a two-way interface: AI agents don’t just call tools, they receive ground-truth telemetry from kernel tracepoints, eBPF programs, and kprobes. By exposing low-level system instrumentation through MCP servers, agents get a real-world model of the live system instead of hallucinated state — closing the observability gap that traditional APM leaves wide open. What Is MCP and Why It Needs Observability The Model Context Protocol (MCP) is an open standard that standardizes how AI agents discover and invoke tools, resources, and prompts. Instead of every agent building bespoke integrations with every service, MCP defines a common protocol: a host (the agent runtime) connects to MCP servers, which expose tools the model can call and resources it can read. ...