Best LiteLLM MCP Server MCP Server Alternatives 2026
Updated June 202610 alternatives to LiteLLM MCP Server for your AI workflow. Compare features, pricing, and compatibility.
LiteLLM MCP Server
Open Source✓ OfficialLiteLLM is not a single MCP server so much as an MCP Gateway: the MCP layer inside the LiteLLM AI Gateway (proxy), which puts one fixed `/mcp` endpoint in front of every MCP server your organisation uses and controls access to them by key, team and organisation. Start with the install trap, because it is the reason most people land here. There is no separate `litellm-mcp-server` package — that name is not published on PyPI, so any guide telling you to `pip install litellm-mcp-server` is wrong and the command will fail. The gateway ships inside LiteLLM itself, so the correct install is `pip install 'litellm[proxy]'` (litellm 1.95.0 on PyPI), from the BerriAI/litellm repository. Backing servers are registered either in `config.yaml` under `mcp_servers:` or from the LiteLLM UI under MCP Servers → Add New MCP Server; persisting them to the database needs `STORE_MODEL_IN_DB=True` (or `general_settings.store_model_in_db: true`, optionally narrowed with `supported_db_objects: ["mcp"]` so only MCP objects are stored). All three transports are supported for the servers behind it — streamable HTTP, SSE and stdio — so a stdio entry with `command`, `args` and `env` (say `npx -y @circleci/mcp-server-circleci`) sits behind the same gateway endpoint as a hosted URL. Auth per backing server is declarative: `auth_type` accepts `none`, `api_key` (sends `X-API-Key`), `bearer_token`, `basic`, `authorization` (verbatim, no prefix), `token` (GitHub style), `oauth2` — which must declare an `oauth2_flow` of `authorization_code` for interactive PKCE sign-in or `client_credentials` for machine-to-machine — `oauth2_token_exchange` for RFC 8693 on-behalf-of, and `aws_sigv4` for MCP servers hosted on Bedrock AgentCore. `static_headers` covers servers that just want fixed headers on every request, `extra_headers` names headers to forward from the client, and `${VAR_NAME}` server variables can be scoped Instance (shared) or Per-user. On the client side you connect to `http://<your-proxy>:4000/mcp/` with an `x-litellm-api-key` header, choose a server group with `x-mcp-servers`, and pass per-server credentials as `x-mcp-{server_alias}-{header_name}` — `x-mcp-github-authorization: Bearer gho_…` alongside `x-mcp-zapier-x-api-key: sk-…` — so a single connection carries different auth for each backing server instead of sharing one token across all of them. Tool names are namespaced by server, and `litellm_settings.mcp_aliases` maps a short alias onto a server name so tools read `github_create_issue` rather than `github_mcp_server_create_issue`. A direct REST path, `/mcp-rest/tools/list` and `/mcp-rest/tools/call`, lets you list and call tools with curl and no LLM in the loop. One version note: from LiteLLM v1.80.18 the gateway speaks MCP protocol 2025-11-25 and rejects new server names that do not comply with SEP-986; existing non-compliant names only warn for now.
This MCP server is free and open-source. Check the GitHub repository for details.
Top LiteLLM MCP Server Alternatives
a single structured-reasoning tool that lets a model plan, revise and branch its own chain of thought instead of answering in one shot. Published by Anthropic as part of the official modelcontextprotocol/servers monorepo (89,000+ stars, actively maintained), it exposes exactly one tool — sequential_thinking — and that tool is the whole product. Each call carries a `thought` string plus bookkeeping fields: `thoughtNumber`, `totalThoughts`, and `nextThoughtNeeded`, which the model flips to false when it is done. The interesting fields are the optional ones. `isRevision` and `revisesThought` let the model go back and correct an earlier step rather than plowing ahead on a bad assumption; `branchFromThought` and `branchId` let it fork into an alternative line of reasoning and carry both forward; `needsMoreThoughts` lets it extend past its own original estimate when a problem turns out to be deeper than it looked. In practice you never call the tool by hand. You connect the server to an MCP host and ask a question that deserves more than one pass — plan a PostgreSQL 14 to 16 migration and revise if downtime exceeds five minutes, work out why a deploy only fails in production, compare three architectures and branch when an assumption breaks. You can tell it is working when the host inspector shows repeated sequential_thinking calls with a rising `thoughtNumber` rather than a single response. Install with `npx -y @modelcontextprotocol/server-sequential-thinking` — note the hyphenated package name, which differs from both the `sequentialthinking` directory in the repo and the Docker image `mcp/sequentialthinking`, a mismatch that breaks a lot of copied configs. A Docker image is published alongside the npm package, and the README carries one-click VS Code install buttons for both transports. Set `DISABLE_THOUGHT_LOGGING=true` if you do not want every thought written to the server log.
Free and open-source by Anthropic. Runs locally with no external API dependencies.
The Apify MCP server gives AI agents access to 6,000+ ready-made cloud scrapers, crawlers, and automation tools on the Apify Store — no infrastructure required. Connect to Apify Actors that extract data from social media platforms (Instagram, TikTok, LinkedIn), search engines (Google, Bing), e-commerce sites (Amazon, eBay), maps (Google Maps), and virtually any website. Each Actor runs in the cloud with managed proxies, browser fingerprinting, and anti-bot bypass built in. Use the Apify MCP server to query Actors by task, stream results directly into your AI context, run custom scraping Actors from your Apify account, and chain multiple data extraction steps in a single workflow. Supports tool filtering to expose only the Actors you need, and integrates with Apify's RAG web browser Actor for retrieval-augmented generation use cases.
The MCP server is free and open-source. Apify platform: Free tier with $5/mo platform credits. Starter: $49/mo. Scale: $499/mo. Enterprise: Custom pricing.
authenticated access to the whole GitHub platform — repositories, files, branches, issues, pull requests, Actions runs, security alerts, discussions and notifications — from Claude, Cursor, VS Code, Copilot CLI and any other MCP host. There is no npm package for this server, and that trips up most people who try to install it: `@github/mcp-server` is not published to the npm registry, so any `npx` line you find for it will fail. GitHub ships it three other ways. The easiest is the hosted remote server at https://api.githubcopilot.com/mcp/, which needs no install at all — point an HTTP-transport MCP client at that URL and log in with OAuth (VS Code 1.101+, Claude Desktop, Claude Code, Cursor and Windsurf all support this). The second is the official Docker image ghcr.io/github/github-mcp-server, which is what the copy-paste command on this page runs; on github.com it now performs a browser-based OAuth login on first use and keeps the token in memory only, which is why the published Docker configs map a fixed loopback callback port (-p 127.0.0.1:8085:8085 with GITHUB_OAUTH_CALLBACK_PORT=8085) so the container can receive the callback. Prefer a token? Set GITHUB_PERSONAL_ACCESS_TOKEN instead — it takes precedence over OAuth, and the minimum useful scopes are repo, read:org and read:packages. The third is the native Go binary from the repository's releases, which needs no fixed port for the OAuth flow. GitHub Enterprise Server has no hosted option: use the local server with --gh-host or GITHUB_HOST set to your instance (include the https:// scheme — it defaults to http://, which GHES rejects). Toolsets can be narrowed with GITHUB_TOOLSETS, and an insiders channel is available at /mcp/insiders or via the X-MCP-Insiders header.
The MCP server is free and open-source. GitHub: Free tier for public repos and limited private repos. Team: $4/user/mo. Enterprise: $21/user/mo.
a first-party MCP endpoint built into the GitLab instance itself — there is no package to install, because the server ships inside GitLab and answers at https://<your-gitlab>/api/v4/mcp (gitlab.com exposes the same path, so https://gitlab.com/api/v4/mcp works for SaaS projects). It landed as an experiment in GitLab 18.3 and moved to beta in 18.6. Authentication is the part that makes it different from every community GitLab server: it uses OAuth 2.0 Dynamic Client Registration, so the first time a client connects it registers itself as an OAuth application on your instance and is issued an access token — no personal access token pasted into a config file. Administrators who do not want one OAuth application per tool can pre-create a shared application instead. Three prerequisites are what actually block most first connections: GitLab Duo must be set to Always on or On by default, beta and experimental features must be enabled, and MCP access must be switched on at the group or instance level. The tool surface covers issues and merge requests (create_issue, get_issue, create_merge_request, get_merge_request, list_merge_requests, get_merge_request_commits, get_merge_request_diffs, get_merge_request_pipelines, create_merge_request_note, get_merge_request_notes), CI/CD (manage_pipeline for list/create/delete/retry/cancel, get_pipeline_jobs, get_job_log), work items (create_workitem_note, get_workitem_notes, link_work_items, get_saved_view_work_items), search (search across the instance, search_labels, semantic_code_search), list_wiki_pages, and attach_scan_profile. HTTP is the recommended transport — claude mcp add --transport http GitLab https://gitlab.com/api/v4/mcp — and clients that only speak stdio can wrap it with npx mcp-remote <url> on Node 20+. Send the X-Gitlab-Mcp-Server-Tool-Name-Prefix header if generic names like search collide with another connected server. If your instance predates 18.3 or Duo is not available to you, the community alternative most teams land on is zereight/gitlab-mcp (1,889 stars as of 2026-08-16, npm @zereight/mcp-gitlab), which authenticates with a plain personal access token and ships 217 tools — including merge_merge_request, approve_merge_request, execute_graphql and full CI/CD variable management, none of which the built-in server exposes — behind GITLAB_PERMISSION_MODE=readonly/modify and GITLAB_TOOLSETS/GITLAB_TOOLS filtering. One further change worth noting: MCP server access moved from GitLab Premium to GitLab Free in 19.2 and became a setting of its own.
The MCP server is free. GitLab: Free tier with 5 users. Premium: $29/user/mo. Ultimate: $99/user/mo.
AWS Labs maintains a monorepo of specialized, open-source MCP servers that bring AWS best practices directly into AI-assisted development workflows, spanning infrastructure, data, AI/ML, cost management, and healthcare/life-sciences domains. Rather than one monolithic server, the project ships dozens of focused servers you install individually depending on the task: the AWS Documentation MCP Server for real-time official docs and API references, dedicated servers for Terraform/CDK/CloudFormation infrastructure-as-code, container and serverless platforms (ECS, EKS, Lambda), SQL/NoSQL databases (DynamoDB, RDS, Aurora), search and analytics (OpenSearch), messaging (SQS/SNS), and cost/billing analysis. Most servers install via uvx with a package name like awslabs.aws-documentation-mcp-server, run locally over stdio, and use standard AWS credential chains (IAM roles, profiles, or access keys) rather than exposing raw account credentials to the model. AWS also now offers a managed, remote "AWS MCP Server" (in preview) that combines full API coverage with pre-built agent SOPs, syntactically validated API calls, and complete CloudTrail audit logging for teams that want centralized governance instead of running servers locally. The Getting Started with Kiro/Cursor/VS Code/Claude Code sections in the repo provide one-click install configs for each server, making it straightforward to wire up only the AWS services a given project actually touches.
The MCP server is free and open-source. AWS services use pay-as-you-go pricing. Free tier available for many services for 12 months. Costs vary by service usage.
Cloudflare ships two different things under this name. The mcp-server-cloudflare repo provides 16 remote, domain-specific MCP servers rather than one monolith — Documentation, Workers Bindings (storage/AI/compute primitives), Workers Builds, Observability (logs/analytics), Container sandboxes, Browser Rendering (fetch pages, convert to markdown, screenshots), Logpush health, AI Gateway (prompt/response search), AI Search, Audit Logs, DNS Analytics, Digital Experience Monitoring, Cloudflare One CASB, Radar, GraphQL analytics and the Agents SDK docs server, each on its own `*.mcp.cloudflare.com/mcp` hostname. Separately, the Cloudflare API MCP server at mcp.cloudflare.com/mcp (repo: cloudflare/mcp) exposes the whole 2,500+ endpoint Cloudflare API through just two tools, `search` and `execute`, using the Code Mode pattern — model-written JavaScript runs in an isolated Dynamic Worker, costing ~1,000 tokens of context against the ~1.17M an equivalent native-tool server would need. Pick a domain server when you want a readable, curated tool list for one product area; pick the API server for breadth or for endpoints nobody wrote a tool for. All endpoints are Streamable HTTP on `/mcp` and support the MCP 2026-07-28 spec; the historical `/sse` URLs remain as aliases for the same Streamable HTTP handler but no longer serve the deprecated HTTP+SSE transport, so clients pinned to SSE must switch. Auth is OAuth on connect, or a scoped Cloudflare API token as a bearer header for CI. Clients without native remote-MCP support bridge via `npx mcp-remote https://<subdomain>.mcp.cloudflare.com/mcp`.
The MCP server is free and open-source. Cloudflare: Generous free tier for Workers, KV, R2, D1. Workers Paid: $5/mo. Pro: $20/mo. Business: $200/mo.
Browserbase MCP and Stagehand MCP are the same server under two names, and this catalog lists both because people search for both — this page is the platform view, /servers/stagehand covers the natural-language automation layer that runs inside it. What Browserbase supplies is the browser itself: a headless Chrome session running in Browserbase's cloud rather than on the machine your agent is on, which is the reason to use it at all. The session is a real, addressable browser with its own residential-proxy and stealth configuration and a recorded replay you can watch afterwards, so an agent's browsing survives being run from a datacenter IP, and a failed run is debuggable after the fact instead of being a black box. Nothing renders on the developer's machine, so a long-running agent is not tied to a desktop staying awake. Browserbase's recommendation is the hosted server at https://mcp.browserbase.com/mcp over streamable HTTP — they operate it and absorb the model inference that the instruction-following tools require. Clients without HTTP transport connect via npx mcp-remote https://mcp.browserbase.com/mcp. Self-hosting installs @browserbasehq/mcp-server-browserbase from npm and needs BROWSERBASE_API_KEY and BROWSERBASE_PROJECT_ID from your dashboard. Six tools are exposed — start and end for session lifecycle, navigate, act, observe and extract — and they are documented in detail on the Stagehand entry, because the act/observe/extract trio is Stagehand's model rather than Browserbase's. One thing to know before self-hosting: the browserbase/mcp-server-browserbase repository was archived on 2026-07-20 and its README now says it is kept for historical reference and should not be read as representative of the current production service. The npm package still installs and the star count on this page belongs to that archived repo; the hosted endpoint is the path Browserbase actually maintains.
The MCP server is free and open-source. Browserbase: Free tier with limited browser sessions. Paid plans for higher volume. See official pricing for current rates.
Frequently Asked Questions
What are the best alternatives to LiteLLM MCP Server MCP Server?
The top alternatives to LiteLLM MCP Server MCP Server in 2026 include Fetch, Git, Memory, Sequential Thinking MCP Server, Apify MCP Server. Each offers similar functionality in the AI & ML category with different features, pricing, and compatibility.
Is there a free alternative to LiteLLM MCP Server MCP Server?
Yes, free alternatives to LiteLLM MCP Server include Fetch, Git, Memory. These offer free tiers or are completely open-source.
How do I choose between LiteLLM MCP Server and its alternatives?
When choosing between LiteLLM MCP Server and alternatives, consider: (1) Pricing — compare free tiers and paid plans, (2) Features — what specific capabilities you need, (3) Compatibility — which AI assistants (Claude, Cursor, VS Code) are supported, (4) Installation — npm, pip, docker, or other install methods.
Can I use multiple MCP servers at the same time?
Yes! MCP (Model Context Protocol) supports running multiple servers simultaneously. You can use LiteLLM MCP Server alongside other MCP servers to extend your AI assistant's capabilities across different services and tools.