BREAKING: SpaceXAI just released a major Grok Build update, with Grok 4.7 now available in Grok Build. The update improves crash recovery, prompt handling, navigation, configuration reliability and overall subagent performance. It also adds new subagent controls, configurable request limits and long-reasoning reminders. Release Notes Version 1.0.41 — September 22, 2026 Features • Added a new sports_search tool that can look up live NFL scores, standings, schedules, player stats and team records directly from X data. • Added a subagent model inheritance setting to /settings. It persists in config.toml and respects managed and overlay layers. • Per-model request size limits can now be configured to match provider HTTP body caps and control inline image eviction. • A long-reasoning reminder can now be enabled through config to nudge the model after lengthy hidden-reasoning steps. Bug Fixes • Fixed the subagent fullscreen view so it no longer shows a stray [Dashboard] button in the header. • Fixed agent frontmatter mcpServers so headers and URLs from the active agent’s agent.md correctly override config.toml and survive config reloads or agent switches. • Ctrl+P now opens the command palette immediately from the welcome screen. • The Dashboard now focuses the “+ New Agent” row instead of leaving the previous session selected. • Sessions interrupted by a crash now show a clear marker instead of silently dropping the turn. • Pressing Ctrl+Z immediately after stashing a prompt now restores it. • The collapsed edit blocks setting now correctly collapses edits even when expanded_by_default is pinned to true. • Interjections now receive a visible reply before the agent resumes its previous tasks. • Cancelling a turn blocked on spawn_subagent now tells the model that the child moved to the background instead of claiming it was never executed. • Saving a queued-prompt edit now returns focus to the composer so the next keystrokes type the next message. • Subagent activation is now consistent between the TUI and Grok Agent Studio. Tables that only set limits or models no longer disable subagents. • Effort-level selection now accepts menu labels in addition to IDs.

# Useful but Little-Known Features of Claude Agent SDK 🌍 Want to give each subagent its own skills, MCP servers, and memory? AgentDefinition has you covered with rich per-agent configuration! Claude Agent SDK's AgentDefinition supports skills, memory, mcpServers, and many more fields for comprehensive per-agent customization. 📌 Title: Per-Agent Configuration: skills / memory / mcpServers 🔗 URL: 🧩 Overview `AgentDefinition` includes numerous optional fields beyond the required `description` and `prompt`. Use `skills` to preload domain-specific knowledge, `memory` to set the memory source (`user`, `project`, or `local`), and `mcpServers` to connect MCP servers by name or inline config. Additional fields include `tools`, `disallowedTools`, `model` (aliases like `sonnet`/`opus`/`haiku`/`inherit` or a full model ID), `effort`, `permissionMode`, `maxTurns`, `background`, and `initialPrompt` for comprehensive behavior control. 🛠 How to Use ```python # Python - fully configured agent definition from claude_agent_sdk import AgentDefinition agent = AgentDefinition( description="Database migration specialist", prompt="You are a DB migration expert. Propose safe migration strategies.", tools=["Read", "Grep", "Glob", "Bash"], disallowed_tools=["Bash(rm *)"], # Block specific operations model="opus", # Use high-quality model skills=["db-migration"], # Preload skills memory="project", # Use project memory mcpServers=["postgres-server"], # Connect MCP servers effort="high", # Higher reasoning level max_turns=20, # Limit maximum turns permission_mode="acceptEdits", # Auto-approve edits background=False, # Run in foreground ) ``` ```typescript // TypeScript const agent: AgentDefinition = { description: "Database migration specialist", prompt: "You are a DB migration expert. Propose safe migration strategies.", tools: ["Read", "Grep", "Glob", "Bash"], disallowedTools: ["Bash(rm *)"], model: "opus", skills: ["db-migration"], memory: "project", mcpServers: ["postgres-server"], effort: "high", maxTurns: 20, permissionMode: "acceptEdits", background: false, }; ``` 🏗 Integration into Production Systems - Use `skills` to preload domain-specific knowledge for specialized agents (unlisted skills remain invocable via the Skill tool) - Set appropriate `memory` scope to control context sharing between agents - Manage database and external service connections via `mcpServers` by name or inline configuration - Use `maxTurns` for cost control and runaway prevention; set `effort` to match task importance - Enable `background: true` for non-blocking execution to improve parallel task efficiency 💡 Use Cases 🗄 Database migration agents with MCP server connections for direct DB access 📚 Expert agents with domain-specific skills preloaded at startup ⚡ High-speed workflows with background agents running parallel analyses ⚠️ Caveats - Omitting `tools` inherits all tools from the parent; specify explicitly if you want restrictions - Subagent `permissionMode` is forcibly inherited when the parent uses `bypassPermissions`, `acceptEdits`, or `auto` and cannot be overridden - Skills listed in `skills` are preloaded at startup, but unlisted skills can still be invoked via the Skill tool - In the Python SDK, field names use camelCase to match the wire format ✨ With AgentDefinition's rich configuration fields, you can build purpose-built specialist agents instead of generic ones. Tailor every aspect to the task at hand! #ClaudeAgentSDK# #AIAgent#

MCP meets agent skills MCP already gave agents a standard way to connect to tools, resources, and external systems. Now it also defines a standard way to discover and load Agent Skills directly from MCP servers. The flow is simple: → connect to MCP server → discover available skills → inspect skill metadata → load the relevant 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 only when needed Under the hood, Skills are served through MCP’s existing Resources primitive. That means 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱, references, scripts, examples, and other supporting files are exposed as resources that the client can read on demand. This is especially useful for context window management. Instead of loading every workflow instruction upfront, the agent can first discover what skills are available and pull in only the one required for the current task. A useful mental model is: tools = what the agent can do resources = what the agent can access skills = how the agent should perform a reusable workflow Previously, that workflow knowledge often lived separately in docs, repos, prompt files, or custom integrations. Now the MCP server can expose the capability and the playbook for using it together. So you get: → standardized skill discovery → on-demand context loading → cleaner distribution and versioning → reusable workflows that travel with the server MCP was already the connection layer. Skills now add a standardized way to ship reusable agent know-how on top of it. The illustration below visually summarizes everything that we discussed so far. Read more: Cheers! :)

MCP: new enough to be exciting, old enough to create a sprawl problem. Tell me if this sounds familiar. One team spins up an MCP server. Then five more. All built differently of course! Nobody knows what's out there or who owns what. Yikes. Agents got flooded with tools they don't need and you accidentally spent a months worth of tokens in a day (and you can't even blame Fable). Shadow infra at it's finest! But now it's got AI speed, so it's like supercharged chaos. That's why we built a central gateway for all AI context. One place to enforce authentication and security for all your data and tools. One point of observability to see which tools are being used, by which agents, and at what cost.

An MCP server that lets AI assistants search and analyze datasets from France's national open data platform directly through conversation. - Public instance available at with no access restrictions - Works with Claude, ChatGPT, Gemini, Mistral, and 13+ other clients - Ask questions in plain French like "Quels jeux de données sont disponibles sur les prix de l'immobilier ?" - Covers search, exploration, and analysis of datasets Explore it here:

I created an MCP server so my team can draft emails for me. Just one tool: draft_email(to, subject, body_markdown, cc, bcc) I check my drafts and click send. Running on @render free tier. instrumented with @pydantic Logfire, using GH/samuelcolvin/cloudkv for storage. Free to run. (not open source because it's mostly vibe coded)

The Braintrust MCP server now exposes write tools. Your coding agent can author prompts, scorers, and classifiers, configure the Topics pipeline, build monitor views, create alerts, and run evals, all without leaving your agent environment. Read more →

I built a design MCP server for Claude Code, Codex, and OpenCode. It searches hundreds of beautiful websites and gives your coding agent design inspiration. Launching next week.

OpenClaw and the Atlassian MCP Server are now featured in SoloHost on Pi Desktop, expanding the range of self-hosted tools users can run locally. OpenClaw offers a more contained local AI setup with streamlined installation, while the Atlassian MCP Server supports AI-integrated Jira workflows for professionals through a locally run connection. Go to the Pi mining app to learn more about both apps and how SoloHost expands the utility of Pi Desktop and Nodes beyond blockchain infrastructure by bringing new AI and developer tools into the Pi ecosystem.