this coding agent: install the CLI, initialize a local PGLite or Supabase brain, register MCP, capture per-remote trust policy.
- [/ship](ship/SKILL.md): Ship it: merge the base, run tests, review
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layers, a plain LLM is cheaper and faster.
**How an agent should call it:**
- [MCPserver (recommended)](https://worldmonitor.app/mcp): Streamable HTTP; issue `tools/list` for the live inventory. Auth: OAuth2
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lines, little on prose. Library (no API key, in-process), proxy, and MCPserver. Apache 2.0, local-first.
Headroom is shipped as a Python package (`headroom-ai`), a TypeScript package
1 project uses a version of this
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copy and CLI commands (jsrepo, shadcn).
- [MCP Setup](https://www.reactbits.dev/get-started/mcp): Set up a MCPserver to help you with development.
- [Pro catalogue](https://www.reactbits.dev/pro): On-domain previews
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codebase-memory-mcp
> Codebase Memory is an open-source local MCPserver for AI coding agents. It connects code, documentation, configuration, and infrastructure in a persistent knowledge graph, with
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configuration. The OpenAI-compatible example controls can point a live example at that local server.
- [MCPservers](docs/gitbooks/en/developing/embed/integrations/mcp.md): Enable the `mcp` Cargo feature and declare a server with `AgentSpec::mcp
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Linera and trading on Linera prediction markets.
- [mcp-demo](https://github.com/linera-io/mcp-demo): Demo MCPserver connecting Claude to a Linera counter application via GraphQL.
- [linera-web](https://github.com/linera-io/linera-web):
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tool contexts, and test tool execution paths.
- [Model Context Protocol](https://openai.github.io/openai-agents-python/mcp/): Integrate MCPservers so agents can dynamically request data or actions from external providers via a standard
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manage schemas, and work with tool contexts.
- [Model Context Protocol](https://openai.github.io/openai-agents-python/mcp/): Connect MCPservers so agents can request external data or actions through standardized tool APIs.
## Operations
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level package.
AI assistants can use AKShare without writing code through the MCPserver
maintained by the AKShare team in the companion project AKTools
(`https://github.com/akfamily/aktools`, MCP Registry name
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github.com/NirDiamant/agents-towards-production/tree/main/tutorials/LangGraph-agent): Building a stateful agent as a graph with LangGraph.
- [Agent with MCP](https://github.com/NirDiamant/agents-towards-production/tree/main/tutorials/agent-with-mcp): Connecting an agent to tools over the Model Context Protocol.
- [A2A](https://github.com
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Bilibili (B 站). One
> `docker compose up` gives you a REST API, an MCPserver and a web console,
> backed by an identity pool (cookies + proxies + fingerprints) that mints,
> tests
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docs.corsair.dev/mcp-adapters/claude-sdk.md): Use Corsair with the Claude Agent SDK via an in-process MCPserver.
- [Vercel AI SDK](https://docs.corsair.dev/mcp-adapters/vercel-ai.md): Connect Corsair to the Vercel AI SDK over HTTP
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permissions, undo, durable jobs, loops that repeat until a test command passes, an embedded MCPserver with 49 tools, and an isometric house called the World where the agents
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tokens are revoked.
- [Auth: MCP Auth](https://nango.dev/docs/guides/auth/mcp-auth.md): Let your users connect to MCPservers through Nango
- [Agent sessions](https://nango.dev/docs/guides/agent-sessions.md): Optimized interface for AI agents
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Kami API, no account, and no API key: the integration surface is a local MCPserver, JSON content schemas, and CLI checks.
## When to use Kami
Reach for Kami when
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