ContextStream MCP Server
RemoteAbout ContextStream MCP Server
ContextStream MCP Server gives AI agents code search, project-decision recall, and context across sessions. It links code, documents, and conversations, with configurable data capture and traceable sources.
Key features
- 01Find relevant code files
- 02Save and retrieve project decisions
- 03Retain context across sessions and tools
- 04Traceable sources and configurable data capture
- 05Local and hosted MCP connection options
Use cases
- 01Locate files relevant to an upcoming code change
- 02Retrieve saved decisions in a new session
- 03Give agents team context for project tasks
MCP support
The MCP server exposes code search, decision retrieval, and project context to clients. It supports local connections and a hosted MCP endpoint; a hosted connection alone does not sync a local checkout.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.contextstream.io/mcpclaude mcp add --transport http contextstream https://mcp.contextstream.io/mcp{"mcpServers":{"contextstream":{"type":"http","url":"https://mcp.contextstream.io/mcp"}}}Open in Cursor{"servers":{"contextstream":{"type":"http","url":"https://mcp.contextstream.io/mcp"}}}Open in VS Code{"mcpServers":{"contextstream":{"type":"http","url":"https://mcp.contextstream.io/mcp"}}}{"mcpServers":{"contextstream":{"serverUrl":"https://mcp.contextstream.io/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project README
ContextStream MCP Server
The context platform for AI agents.
Your agent knows code. Give it the decisions behind yours.
Connect your code, docs, and conversations so your agents can find the right files, recall saved decisions, and build on past work across sessions and tools.
The numbers, their sources, and what they do not claim are under Benchmarks.
Start free with MCP
10,000 monthly credits. No credit card required.
Works with Claude Code, Cursor, Codex, GitHub Copilot, Gemini CLI, Qwen Code, Kimi Code, Muse Code, ZCode, Zed, and more MCP clients, on Claude, GPT, Gemini, Kimi, GLM, Qwen, and other models. Create your account or sign in during MCP onboarding. No separate website signup needed.
macOS and Linux
curl -fsSL https://contextstream.io/scripts/mcp.sh | bash
Windows PowerShell
irm https://contextstream.io/scripts/mcp.ps1 | iex
Paste the command into your terminal and follow onboarding to connect your project and supported editor. Restart your editor after setup.
What gets installed and indexed? · Supported clients · Setup guide · Prefer to start on the web?
Make the next session useful
Ask your connected agent:
Use ContextStream to find the files relevant to my next change. Cite the sources and retrieve any saved project decisions that should guide the work.
Then ask it to save a real decision and its reason. Start a new session and retrieve that decision without explaining it again. Indexing supplies code context; it cannot recover every undocumented decision.
Benchmarks
Each benchmark runs through the production API, is registered before its results are read, and is published with its method, confidence interval, and losses.
| Benchmark | ContextStream | Compared with |
|---|---|---|
| LongMemEval-S, all 500 questions | 96.8% (484/500); 96.0% on the half held out from tuning | Above Mastra (94.87%) and supermemory (85.4%) on the same official GPT-4o judge. Level with agentmemory's self-reported 96.2%, which is inside our 95% interval |
| LoCoMo, all 1,540 questions | 95.7% (1474/1540); 95.8% held out; 97.8% on the audited answer key | Above every system in the EverMemOS paper's same-judge table: EverMemOS 93.05%, Zep 85.22%, MemOS 80.76%, MemU 66.67%, Mem0 64.20% |
| Code search, 177 judged developer queries | 98.77% best answer in the top 10; 100% a useful file in the top 10 | Embedding-only search 72.84% and ripgrep 42.59% on the same queries and judgments |
| Agent tasks, 8 tasks × 3 trials | 23/24 passed (96%); 9/9 on tasks that need team knowledge | 14/24 (58%) and 0/9 with the repository alone |
61 ms median server-side search time (127 ms p95) in the October 27 production run. End to end it was 223 ms, because the client ran in a different region from the one serving the workspace. Your latency depends on your distance from that region.
Competitor figures are as each vendor or the EverMemOS paper published them; we did not re-run them, and readers and judges differ across rows. The code-search baselines come from the August 1 run on the same queries. The agent-task result uses one repository and one agent model, so treat it as directional. Results, methodology, and limitations.
Data controls
Scoped access. Traceable sources. Configurable capture. Indexing sends eligible source contents to ContextStream for hosted search. Transcript saving and local Git metadata capture are on by default and can be turned off. Review data handling and controls before connecting sensitive projects.
Other installation options and troubleshooting
npm (Node.js 20+)
npx -y @contextstream/mcp-server@latest setup
The npm launcher downloads the native Rust binary for your platform and verifies its SHA-256 checksum. Pin an exact package version for production automation.
Hosted MCP without a local process
For clients supporting Streamable HTTP and OAuth, the hosted endpoint is:
https://mcp.contextstream.io/mcp?default_context_mode=fast
Supported clients lists one-line add commands for Claude Code, Codex, Gemini CLI, Qwen Code, Copilot CLI, and Droid, and one-click install for VS Code. Use the MCP documentation for other clients and stdio alternatives. A hosted connection alone does not sync your local checkout.
Preview or diagnose setup
contextstream-mcp setup --dry-run
contextstream-mcp doctor --scope=all --only-configured
Scripted and CI setup
For automation, wrap the install in pipefail so it fails when the download
fails, instead of reporting success after installing nothing:
bash -o pipefail -c 'curl -fsSL https://contextstream.io/scripts/mcp.sh | bash'
setup --yes reads the key from CONTEXTSTREAM_API_KEY or saved credentials,
and needs --workspace-id when the account has more than one workspace:
export CONTEXTSTREAM_API_KEY=... # or: printf %s "$KEY" | contextstream-mcp configure --api-key-stdin
contextstream-mcp setup --yes --editors=claude --workspace-id=<UUID> --project-path=.
Package Bouncer (opt-in)
A hook for Claude Code and Cursor that checks the packages an install command names
against their public registry before the command runs. It covers npm, pnpm, yarn, bun,
npx, pip, uv, uvx, pipx, poetry and cargo, including claude mcp add ... -- npx ....
- It denies a package the registry does not have, naming the popular package it looks like. An agent may have invented the name, and anyone can register it.
- It asks before a barely downloaded lookalike of a popular package.
- It allows everything else, including whenever a lookup fails or takes too long.
It skips projects and commands that use a private registry (.npmrc, pip.conf, uv or
poetry sources, --registry, --index-url). CONTEXTSTREAM_PACKAGE_BOUNCER=off
turns it off.
Claude Code: add this entry to PreToolUse in ~/.claude/settings.json, next to the
hooks setup installed. update-hooks leaves it alone.
{ "matcher": "Bash", "hooks": [{ "type": "command", "command": "contextstream-mcp hook package-bouncer", "timeout": 8 }] }
Cursor (~/.cursor/hooks.json):
{ "version": 1, "hooks": { "beforeShellExecution": [{ "command": "contextstream-mcp hook package-bouncer" }] } }
Open source and contributing
This repository contains the MIT-licensed Rust MCP server, client, editor setup, and release tooling. The hosted backend is separate and is not included here. See the architecture, release integrity, and latest release.
cargo build --locked -p mcp-server --bin contextstream-mcp
cargo test --locked --workspace
Use the pinned Rust toolchain. For contribution checks and commit sign-off, read CONTRIBUTING.md. Report vulnerabilities using SECURITY.md. See LICENSE, NOTICE, and GOVERNANCE.md for licensing, trademarks, and project governance.
Let humans be human.
Docs · Pricing · Integrations · Benchmarks
Optional deep project learning
Deep project learning starts off for new accounts. Plain contextstream-mcp setup --yes
preserves your current choice. To review consent, run
contextstream-mcp configure --account-learning on and confirm on the signed-in
privacy page. To withdraw, run contextstream-mcp configure --account-learning off.
Agents cannot consent for you. Doctor and init show status only. See
data handling and controls.
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.