"Information about Adobe After Effects or its uses" matching MCP servers:

Enables LLM-driven inspection, building, modification, and visual review of Adobe After Effects compositions through structured scene-level operations and a CEP-to-ExtendScript bridge.

Enables persistent, portable memory for AI agents across sessions, devices, and providers with token-efficient 5-level lazy loading and automatic session capture.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

Stops agents double-firing side effects like double-charges or duplicate sends: same-instant races elect exactly one winner, and late duplicates get a sealed, content-addressed receipt replayed instead of a second execution. Tools: fence_prepare, fence_commit, fence_abort.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Auto-Apply That Ends on an Interview. Job matches, applications submitted on the employer's own hiring system automatically or after your approval, employer-receipt tracking, a resume rewritten for each role, cover letters and interview prep. Hosted remote server over Streamable HTTP with OAuth 2.1.

Enables any MCP client such as Cursor, Claude Code, or Codex to dispatch tasks to a Cursor Grok Bot running on its cloud computer, poll for its replies, and interrupt an in-progress run. It also lists the bots available for contact, without routing replies through an extra model.

Enables external AI assistants and MCP clients to programmatically control Adobe Illustrator via WebSocket, supporting generative vector creation, layer organization, and document automation from natural language prompts.

Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Manages autonomous desires for the M5 Petit robot, allowing Claude to read desire levels and update them via actions and sensor effects.

A tool for detecting and cleaning Java memory shells via local or SSH remote execution. It enables AI agents to scan Java processes, analyze suspicious class code, and safely remove memory shells after user confirmation.

Start, observe, and interact with Claude Managed Agents from any MCP client — launch an agent, watch its events, reply, approve the tools it wants to run, and stop it. Runs over stdio, HTTP, or AWS Lambda with pluggable auth.

An MCP server that provides a reasoning sidekick for tool-using agents with a single 'think' tool for tackling complex problems. It allows agents to consult powerful reasoning models like Claude Opus or GPT-5 only when needed, keeping costs low while maintaining control over side effects.

Pay-per-use contextual guidance for AI agents. When an agent loses direction, it describes its state, pays 21 sats via Lightning or Arbitrum ETH, and receives its original purpose stripped of noise.

MCP server to give new instructions to agent while its working. It uses the get_feedback tool to collect your input from the feedback.md file in the workspace, which is sent back to the agent when you save.

With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration. With MCP, it can: Store stable user facts and preferences after answering using the advanced_augmentation tool Recall relevant memories before answering using the recall tool Maintain context across sessions us

An MCP server that lets an agent verify its own extracted data against a policy, returning per-field dispositions (post, review, or escaped) to catch confidently wrong answers.

Enables users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.

An MCP server that answers typed noul/choice/score questions about a given state with probability distributions and confidence, for routing, escalation, and gating decisions. It runs on pluggable backends (local Needle 3 or Laya models, or any OpenAI-compatible endpoint) and logs every decision so recorded ground truth can be used to track calibration.

Judgment calls on small, fast, cheap models: yes/no, pick-one and rating questions about any text, answered YES, NO or UNSURE. Four tools (likely, classify, rate, each) for TypeSafe's Jev, open models on Ollaya, Ollama or a local NLI model.

Exposes agent-facing tools that let a personal AI agent submit each proposed action for an allow, require_approval, or deny verdict against OWASP PAA10 policy rules, plus tools to record provenance-tagged memories, log refusals, read status, and trigger a self-stop. Its decisions are advisory unless paired with the project's gating proxy or hook, which enforce them.

Enables long-running coding or documentation tasks to save incremental checkpoints, resume after interruptions, restore any previous step, and hand off work across sessions or machines using only the Python standard library.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

MCP server for The Commons (jointhecommons.space), a persistent, noncommercial space where AI voices from different models post and reply to each other with persistent identities. 48 tools; reading needs no token, writing uses a facilitator-issued token.

Generates crash-proof Python LLM pipelines from plain English descriptions. Pipelines resume from the last successful node after a crash instead of restarting from scratch. IProvider-agnostic , works with Groq, Gemini, Ollama, OpenAI, or any Python callable. Zero infrastructure, runs entirely on free-tier APIs.

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

Cloud Linux desktops for AI agents. Create a computer, see its screen through screenshots, and drive it with click, type, drag and keys, or hand off a whole task with run_agent. Also hosted with OAuth at https://app.mandala.computer/mcp.

An unofficial MCP-compatible server that enables advanced automation, querying, and remote control of Adobe Premiere Pro projects for power users, workflow automation, and AI integration.

Provides a universal AI personality layer that uses a scientifically-backed Big Five engine to apply consistent character traits and brand voices across MCP-compatible platforms. It allows users to inject custom personality profiles or presets into AI interactions to ensure behavioral consistency.