MCP tools for Sema — eval, compile, build, format, and docs for a Lisp with LLM primitives.
If you already use Sema, the sema mcp server is the piece that lets your assistant work with it directly. MCP tools for Sema — eval, compile, build, format, and docs for a Lisp with LLM primitives.
A Lisp where LLM agents are language primitives, not an SDK —
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Multi-provider — swap between Anthropic, OpenAI, Gemini, Ollama, any OpenAI-compatible endpoint, or define your own provider in SemaPipeline-ready — response caching, fallback chains, rate limiting, retry with backoff, text chunking, prompt templates, vector store, and a persistent KV storeCost-aware — built-in budget tracking with a bundled pricing snapshot (models.dev), updated per releaseObservable — every LLM/agent run is auto-traced with OpenTelemetry (GenAI semantic conventions): tokens, cost, latency, and the full invoke_agent → chat →Embeddable — a Rust crate with a builder API, or @sema-lang/sema to runDeveloper-friendly — REPL with tab completion, structured error messages with hints, and 50+ example programsSingle-threaded — Rc-based, no cross-thread sharing of valuesProvider — ChatAnthropic — ✅OpenAI — ✅Ollama — ✅Groq — ✅Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Sema's toolset — Multi-provider, Pipeline-ready, Cost-aware and 11 more — is a fair guide to whether it matches your workflow. It is maintained by com.sema-lang; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Multi-provider | swap between Anthropic, OpenAI, Gemini, Ollama, any OpenAI-compatible endpoint, or define your own provider in Sema |
| Pipeline-ready | response caching, fallback chains, rate limiting, retry with backoff, text chunking, prompt templates, vector store, and a persistent KV store |
| Cost-aware | built-in budget tracking with a bundled pricing snapshot ([models.dev](https://models.dev)), updated per release |
| Observable | every LLM/agent run is auto-traced with OpenTelemetry (GenAI semantic conventions): tokens, cost, latency, and the full invoke_agent → chat → execute_tool tree, exportable to Jaeger, Grafana, Datadog, Langfuse, Arize Pho |
| Embeddable | [a Rust crate](https://crates.io/crates/sema-lang) with a builder API, or [@sema-lang/sema](https://www.npmjs.com/package/@sema-lang/sema) to run Sema client-side in JS via WebAssembly |
| Developer-friendly | REPL with tab completion, structured error messages with hints, and 50+ example programs |
| Single-threaded | Rc-based, no cross-thread sharing of values |
| Provider | Chat |
| Anthropic | ✅ |
| OpenAI | ✅ |
| Ollama | ✅ |
| Groq | ✅ |
| xAI | ✅ |
| Mistral | ✅ |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.