"WhyLabs AI Observability Platform" matching MCP tools:

• langfuse_healthA

  iTechSmart MCP ServerB

  Check the health status of the Langfuse LLM observability platform and retrieve the organization name.

  MIT

• query_observability_logsB

  Adios MCPB

  Retrieve workload logs for a team from the observability index, using filters for time range, source, level, and text queries to find relevant events.

  MIT

• langfuse_healthA

  iTechSmart MCP ServerB

  Checks the health of the Langfuse LLM observability platform, returning an OK status and organization name to confirm system availability.

  MIT

• list_servicesA

  OCI Pricing MCP Server — BR forkB

  Retrieve all Oracle Cloud Infrastructure services with pricing categories, or filter by compute, storage, database, networking, Kubernetes, AI/ML, or observability.

  Apache 2.0

• igrid_metricsA

  igrid-sce-mcp-toolA

  Retrieve token and cost observability metrics for the iGrid AI assistant to monitor usage and spending.

  -

• guides.listA

  AI Design Blueprint DoctrineA

  List application guides that apply Blueprint principles to engineering challenges such as security, evaluation, and observability. Use to discover available guides before exploring details.

  MIT

• _delimit_obs_implA

  delimitA

  Fetch metrics, search logs, view health status, or manage alert rules from a single observability endpoint. Choose the action by name to read data or configure thresholds in one request.

  MIT

  Destructive

• delimit_obs_statusA

  delimitA

  Get a one-call observability health rollup: overall status, per-service indicators, and timestamp. Use for session-start green checks, dashboards, and pre-deploy smoke tests.

  MIT

• assess_signalA

  Off-Nadir DeltaA

  Run an AI remote-sensing deep-dive assessment to turn any event signal into an actionable collection plan: recommended sensors, collection window, and a deterministic imagery handoff (exact bbox and event date) for pre/post scene search.

  Apache 2.0

  Destructive

• generate_captionA

  @posteverywhere/mcpA

  Generate 1-5 AI caption variants for social media posts on platforms like Instagram, LinkedIn, X, and more. Customize tone, length, hashtags, and emojis while respecting each platform's character limits.

  MIT

• find_logging_issuesA

  freeplay-mcpA

  Analyze recent completions to detect missing logged fields and improve observability instrumentation.

  Apache 2.0

• search_creators_nlsA

  creatordb-mcp-serverA

  Search for creators across YouTube, Instagram, and TikTok using natural language queries. AI selects the platform and applies relevant filters.

  MIT

• brand_lintA

  pendpostA

  Check captions against editable brand rules to catch platform hygiene problems (length caps, broken links, ALL-CAPS, hashtag count) and AI-writing tells. Errors block publishing; warnings advise. Run before proposing captions.

  MIT

• get_technical_specsA

  echo3s-ioA

  Retrieve Echo3s technical specifications—supported formats, AI processing details, output quality, credit system, and platform info.

  MIT

• listFeaturesA

  PingOne Advanced Identity Cloud MCP ServerB

  Retrieve a unified list of all features in PingOne Advanced Identity Cloud with their install status, including IDM features and platform features like AI Agents.

  Apache 2.0

• detect_platformA

  OpeddA

  Identifies the content platform behind a URL, returning platform, confidence, and onboarding workflow for historical backfill and forward stream.

  MIT

• sassy_persona_observabilityA

  SassyMCPB

  Get the cross-system observability guide: learn which introspection tools are available, what they return, and the recommended first-call sequence.

  Academic Free v1.1

• discover_related_conversationsA

  pūrmemoA

  Find related conversations across all AI platforms using semantic clustering. Search natural language to discover forgotten discussions from ChatGPT, Claude, and Gemini.

  MIT

• research_brand_lookupA

  tracetify-mcpA

  Check how AI assistants mention a brand: get platform mentions, counts, and associated entities to see if AI recommends it. Answers 'does AI recommend us?' with cached results free.

  MIT

• sassy_persona_fullA

  SassyMCPB

  Load the complete operating bundle—style, decisions, practices, observability, capabilities, and user context—on first connection. Establish full context for SassyMCP sessions.

  Academic Free v1.1

• get_usage_breakdownA

  Smallest MCP ServerA

  View credit consumption by feature and product scope, including usage across voice AI, voice models, and platform services.

  MIT