Langfuse (PyPI) MCP Server
langfuse-mcp-server
PyPI
v0.1.0
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
Langfuse MCP server with built-in analytics, multi-project routing, and Google OAuth. Token percentiles, accuracy metrics, failure detection, cost breakdowns, session analytics, latency analysis, context breach scanning — plus a hosted-remote deployment story.
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 5 = 95. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 1
medium Untrusted input can drive an external actionMTC-FLOW-005
Untrusted-input tools ([fetch_traces, fetch_trace, fetch_observations, fetch_observation, fetch_sessions, fetch_scores]) co-exist with external-action tools ([create_comment]). A prompt injection could cause unwanted external actions, though no direct sensitive-data leak path was found.
Evidence: untrusted [fetch_traces, fetch_trace, fetch_observations, fetch_observation, fetch_sessions, fetch_scores] → sinks [create_comment]
Fix: Require confirmation for state-changing/egress actions triggered after processing untrusted content.
Location: flow fetch_traces → create_comment
Tools 58
Each tool and what it can reach — statically extracted from the published source.
create_commentnetwork egressfetch_observationingests untrusted inputfetch_observationsingests untrusted inputfetch_scoresingests untrusted inputfetch_sessionsingests untrusted inputfetch_traceingests untrusted inputfetch_tracesingests untrusted inputaggregate_by_groupno sensitive capabilityanalyze_latencyno sensitive capabilityanalyze_sessionsno sensitive capability
Show 48 more tools ↓
compute_accuracyno sensitive capabilitycompute_token_percentilesno sensitive capabilitycreate_annotation_queueno sensitive capabilitycreate_annotation_queue_assignmentno sensitive capabilitycreate_annotation_queue_itemno sensitive capabilitycreate_chat_promptno sensitive capabilitycreate_datasetno sensitive capabilitycreate_dataset_itemno sensitive capabilitycreate_text_promptno sensitive capabilitydelete_annotation_queue_assignmentno sensitive capabilitydelete_annotation_queue_itemno sensitive capabilitydelete_dataset_itemno sensitive capabilitydetect_context_breachesno sensitive capabilitydetect_failuresno sensitive capabilitydiff_tracesno sensitive capabilityestimate_costsno sensitive capabilityfind_exceptionsno sensitive capabilityfind_slow_tracesno sensitive capabilityget_annotation_queueno sensitive capabilityget_annotation_queue_itemno sensitive capabilityget_commentno sensitive capabilityget_daily_metricsno sensitive capabilityget_data_schemano sensitive capabilityget_datasetno sensitive capabilityget_dataset_itemno sensitive capabilityget_error_countno sensitive capabilityget_exception_detailsno sensitive capabilityget_modelno sensitive capabilityget_promptno sensitive capabilityget_prompt_unresolvedno sensitive capabilityget_score_v2no sensitive capabilityget_session_detailsno sensitive capabilityget_user_sessionsno sensitive capabilitylist_annotation_queue_itemsno sensitive capabilitylist_annotation_queuesno sensitive capabilitylist_commentsno sensitive capabilitylist_dataset_itemsno sensitive capabilitylist_datasetsno sensitive capabilitylist_modelsno sensitive capabilitylist_projectsno sensitive capabilitylist_promptsno sensitive capabilitylist_scores_v2no sensitive capabilitylist_user_queriesno sensitive capabilitylist_usersno sensitive capabilityscore_tracesno sensitive capabilitysearch_trace_contentno sensitive capabilityupdate_annotation_queue_itemno sensitive capabilityupdate_prompt_labelsno sensitive capability
Toxic flows 1
Cross-tool combinations that form a data-exfiltration primitive (untrusted input → sensitive source → external sink).
What this scan could not see
- Tools were statically extracted from the published source (58 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v0.1.0 latest |
A 95/100 | 1 | 1.13.0 | 2026-08-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
npx mcptrustchecker scan langfuse-mcp-server --online --registry pypiOther implementations of Langfuse 4
Independent packages implementing the same tool, scanned with the same engine. Compare all 5 side by side →
Langfuse MCP server for accessing and analyzing telemetry data via natural language
MCP server that exposes the Langfuse REST API as tools — query traces, observations, sessions, scores, prompts, datasets, and metrics from any MCP client.
MCP server for Langfuse analytics and cost monitoring with readonly/readwrite modes via CLI flags for secure npx deployment
JurisLM Langfuse MCP Server — Prompt Management + Traces/Observations 查詢
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