Langfuse MCP vs MCP Server
A side-by-side look at the Langfuse MCP and MCP Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Langfuse MCP for monitoring work as a local process; choose MCP Server for monitoring over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Langfuse MCP when
- You need dedicated capabilities in the Monitoring domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_HOST, LANGFUSE_MCP_READ_ONLY. - You want tools like
list_annotation_queues, create_annotation_queue, get_annotation_queue.
Choose MCP Server when
- You need dedicated capabilities in the Monitoring domain.
- You prefer a remote HTTP/SSE transport.
- Its auth model fits you: API key required.
- You want tools like
thinkneo_check_spend, thinkneo_complete, thinkneo_read_memory.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Monitoring | Monitoring |
| Quality signal | 65/100 · Great | 56/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | API key required | API key required |
| Pricing | Free | Freemium |
| Required env vars | LANGFUSE_PUBLIC_KEYLANGFUSE_SECRET_KEYLANGFUSE_HOSTLANGFUSE_MCP_READ_ONLY | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 113 | 3 |
| Directory activity | 6 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 48 | 68 |
| Full listing | View Langfuse MCP | View MCP Server |
Tools & capabilities
Langfuse MCP tools 48
list_annotation_queuesList annotation queues with pagination.create_annotation_queueCreate an annotation queue.get_annotation_queueGet a single annotation queue by ID.list_annotation_queue_itemsList items in an annotation queue.get_annotation_queue_itemGet a specific annotation queue item by queue and item ID.create_annotation_queue_itemCreate an annotation queue item.update_annotation_queue_itemUpdate the status of an annotation queue item.delete_annotation_queue_itemDelete an annotation queue item.create_annotation_queue_assignmentAssign a user to an annotation queue.delete_annotation_queue_assignmentUnassign a user from an annotation queue.list_datasetsList all datasets in the project with pagination. Returns metadata about datasets including name, description, item count, and timestamps. Args: ctx: Context object containing lifespan context with Langfuse client page: Page number for pagination (starts at 1) limit: Maximum items per page (max 100) Returns: A dictionary containing: - data: List of dataset metadata objects - metadata: Pagination info (page, limit, total)get_datasetGet a specific dataset by name. Retrieves dataset details including metadata and item count. Args: ctx: Context object containing lifespan context with Langfuse client name: The name of the dataset to fetch Returns: A dictionary containing dataset details: - id: Unique dataset identifier - name: Dataset name - description: Dataset description - metadata: Custom metadata - items: List of dataset items (if included by the API) - runs: List of dataset runs (if included by the API)- +36 more on the listing page
MCP Server tools 68
thinkneo_check_spendCheck AI spend summary for a workspace, team, or project. Returns real cost breakdown by provider, model, and time period from the ThinkNEO AI gateway.thinkneo_completeRun a governed LLM completion through the ThinkNEO AI gateway. The request is authorized, classified, and policy-checked against your workspace governance BEFORE any provider is called — a blocked prompt never reaches the model. Tenant/workspace are derived from your API key.thinkneo_read_memoryRead Claude Code project memory files. Without arguments, returns the MEMORY.md index listing all available memories. With a filename argument, returns the full content of that specific memory file. Use this to access project context, user preferences, feedback, and reference notes persisted across Claude Code sessions.thinkneo_write_memoryWrite or update a Claude Code project memory file (.md). Persists project context, user preferences, feedback, and reference notes across Claude Code sessions. Filename must end in .md with lowercase alphanumeric characters. Path traversal is blocked. Requires authentication.Use this to persist project context, user preferences, feedback, and reference notes across Claude Code sessions. The filename must end in .md and contain only lowercase letters, digits, underscores, and hyphens (e.g. 'user_fabio.md', 'project_new_feature.md'). Path traversal is blocked.thinkneo_evaluate_guardrailEvaluate a prompt or text against ThinkNEO guardrail policies before sending it to an AI provider. Returns risk assessment, violations found, and recommendations. Requires authentication.thinkneo_checkFree-tier prompt safety check. Analyzes text for prompt injection patterns and PII (credit card numbers, Brazilian CPF, US SSN, email, phone, passwords). Returns a safety assessment with specific warnings. No authentication required.thinkneo_check_policyCheck AI governance policies including model access, budget limits, data controls, and agent governance from the ThinkNEO gateway.thinkneo_get_budget_statusCheck AI budget status including spend vs limit, forecast, and chargeback data from the ThinkNEO gateway.thinkneo_list_alertsList active alerts for budget, policy, SLA, and security from the ThinkNEO gateway.thinkneo_get_compliance_statusGet compliance status including framework coverage (EU AI Act, ISO 42001, NIST AI RMF, SOC 2) and governance assessments from the ThinkNEO gateway.thinkneo_provider_statusGet real-time health and performance status of AI providers routed through the ThinkNEO gateway. Shows latency, error rates, and availability. No authentication required.thinkneo_schedule_demoSchedule a demo or discovery call with the ThinkNEO team. Collects contact information and preferences. No authentication required.- +56 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Langfuse MCP
{
"mcpServers": {
"avivsinai-langfuse-mcp": {
"command": "uvx",
"args": [
"langfuse-mcp"
],
"env": {
"LANGFUSE_PUBLIC_KEY": "YOUR_LANGFUSE_PUBLIC_KEY_HERE",
"LANGFUSE_SECRET_KEY": "YOUR_LANGFUSE_SECRET_KEY_HERE",
"LANGFUSE_HOST": "YOUR_LANGFUSE_HOST_HERE",
"LANGFUSE_MCP_READ_ONLY": "YOUR_LANGFUSE_MCP_READ_ONLY_HERE"
}
}
}
}
MCP Server
{
"mcpServers": {
"thinkneoai-mcp-server": {
"url": "https://mcp.thinkneo.ai/mcp"
}
}
}
Frequently asked questions
Langfuse MCP is categorized under Monitoring and uses a local stdio subprocess. MCP Server belongs to Monitoring and uses a remote HTTP/SSE transport. Pick Langfuse MCP for monitoring work and MCP Server for monitoring.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Langfuse MCP is listed as free. Auth: api key required. MCP Server is listed as freemium — a free tier with paid plans. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add avivsinai-langfuse-mcp and thinkneoai-mcp-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Langfuse MCP has a quality signal of 65/100 (Great) with 6 views, 0 installs and 113 GitHub stars. MCP Server has 56/100 (Good) with 2 views, 0 installs and 3 GitHub stars.