Branchly
Manage AI agent content, prompts, and actions with built-in analytics and insights
Branchly connects to your AI agent to manage knowledge bases, configure prompts and tools, and analyze user interactions. Track sessions, sentiment, top searches, and which content gets cited most.
Try asking Muse: "Show me which help articles my customers are asking about most and how satisfied they are with the answers"
Source: Found in the official MCP Registry (io.branchly/branchly) · First listed September 25, 2026
Last 24 hours
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Not in Muse's Connectors list yet, but Muse can still use it. Copy the request below and paste it into Muse. Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.
https://api.branchly.io/mcpNot in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
Technical details: safety screening, tools, response time and recent checks
Response time at the last check: 1379ms. Worked in 100% of checks over 30 days.
Screening · September 25, 2026
Screened, no issues found
Screening looks for known threats and hidden instructions at the time of the check, and runs again weekly and whenever the tool list changes. It cannot see the server's code, so only connect what you need and review what Muse asks to do. How screening works.
Tools · 37
get_applicationReturn the full configuration of the authenticated application.list_nodesList nodes in the knowledge base. Optionally filter by vertex label(s), data source type(s), or data source IDs. Optionally sort by updated_at ('asc' or 'desc'). Provide 'query' and 'locale' to perform a full-text search across node content (requires both parameters). Returns pagcreate_nodeCreate a new content node in the knowledge base. Label defaults to 'content'.read_nodeRead a node by its ID. Returns full node details.update_nodeUpdate a content node by its ID. Only provided fields are updated. Title and text are locale dicts (e.g. {"de": "Titel"}) — provided keys are merged, others preserved. custom_metadata keys are likewise merged into the existing metadata.list_data_sourcesList data sources for the authenticated application. Optionally filter by data source type(s). Returns paginated results ordered by last update.create_data_sourceCreate a new data source for the authenticated application. The 'settings' object must match the given 'type'. Returns the created data source. Creating a data source does not sync any content — use run_data_source to start a sync.update_data_sourceUpdate a data source by its ID. Only provided fields will be updated (partial update). Returns the updated data source.run_data_sourceTrigger an asynchronous run (sync/crawl) of a data source by its ID. The run is queued as a background job. Returns the accepted status. Track progress with read_data_source_runs.read_data_source_runsList data source runs for the authenticated application, latest first. Optionally filter by data source ID or run status. Returns the latest runs (default 5) and the total matching count. Use this to track the progress of a run triggered via run_data_source.list_promptsList prompts for the authenticated application. Optionally filter by type, subtype, or active status. Returns paginated results ordered by active status then last update.create_promptCreate a new prompt version for the authenticated application. The new prompt is automatically set as active and the previously active prompt of the same type and subtype is deactivated. Use this whenever you want to change prompt text.update_promptActivate or deactivate a prompt by its ID. Use is_active=true to restore a previous prompt version — the currently active prompt of the same type and subtype is deactivated automatically. To change prompt text, use create_prompt instead.list_toolsList tools configured for the authenticated application. Optionally filter by active status. Returns all matching tools.read_toolRead a tool by its ID. Returns full tool details.create_toolCreate a new tool (AI action) for the authenticated application. Tool names must be unique per application, snake_case, max 64 chars. 'tool_config' and 'function_arguments' must match the given 'tool_type'. Returns the created tool.update_toolUpdate an existing tool by its ID. Fetches the current tool and applies partial updates to name, description, or active status.read_sessionsList sessions for application. Filter by interaction types (chat, search, navigation, form_submission, voice), embed types, answer types, tool IDs, or a full-text search query. Each session includes its full history, but document chunks and tool calls are returned as references (read_session_detailRead a single session with its full history in token-dense form. Returns all interactions (chat, navigation, search, form, voice). Document chunks and tool calls are returned as references (IDs + titles) - use read_node(vertex_id) or read_tool(tool_id) to fetch full content.get_active_sessions_over_timeTime series of active sessions, bucketed by day or week (auto-chosen from the time window). Each row breaks the total down by interaction type (chat / search / navigation / form_submission / voice). Gaps are zero-filled, so the series is safe to plot directly. Use this to spot spget_top_localesTop locales (BCP-47 style, e.g. 'de_DE', 'en_US') across chat, search, and navigation requests, ranked by occurrence count. Use this to understand which languages/regions are being served and where content gaps may exist.get_top_languagesTop detected natural languages of user CHAT questions, ranked by occurrence count. Differs from get_top_locales: locale reflects the embed/browser setting, language is detected from the actual query text. Use both to spot mismatches (e.g. German-speaking users hitting an English-get_top_devicesTop device categories (e.g. 'desktop', 'mobile', 'tablet') across all request types, ranked by occurrence count. Use this to understand the device mix of real users interacting with the embed.get_top_geographiesTop (country, region) combinations across all request types, ranked by occurrence count. Country is an ISO country code; region may be null when unavailable. Use this to understand geographic distribution of users.get_sentiment_distributionSentiment distribution of chat answers as counts of 'positive', 'negative', and 'neutral'. Optionally restrict to specific answer types. Use this as a quick quality signal — a rising 'negative' share usually warrants drilling into individual sessions via read_sessions.get_top_cited_sourcesKnowledge-base nodes most frequently cited in chat answers, ranked by citation count. Each row carries the node UUID (vertex_id), its title, optional source URL, and citation_count. Use this to understand which knowledge is actually load-bearing; pair with read_node(vertex_id) toget_top_clicked_urlsURLs users actually clicked from inside the embed (search results, citation links, follow-ups, etc.), ranked by click count. Optionally filter by click event type. Use this to see what users find useful enough to click through to.get_top_interaction_sourcesPage URLs the user was ON when they interacted with the embed (chat, search, navigation, form submission), ranked by occurrence count. Differs from get_top_clicked_urls: this is the ORIGIN page, not the destination. Use this to find which pages of the host site drive the most embget_top_searchesTop user search queries (normalized: lowercased, trimmed), ranked by occurrence count. Use this to discover dominant user intents and content gaps; pair with read_sessions(search_query=...) to inspect specific sessions.get_top_tagsTop tags attached to chat answers (auto-derived classifications), ranked by occurrence count. Optionally restrict to specific answer types. Use this for a quick topical breakdown of chat traffic.get_answer_type_distributionDistribution of chat answer types (e.g. 'answered', 'no_answer', 'tool_call', 'human_handoff'), ranked by occurrence count. Use this to monitor answer quality: a high 'no_answer' share signals content gaps; a high 'human_handoff' share signals where the bot is escalating.get_trending_classificationsTime series of trending classifications (topics OR intents inferred from chat content), one series per classification id. Each series item carries a period timestamp and count. Use this to see how topical or intent demand shifts over time. Choose 'topic' for subject-matter trendsread_chat_request_documentsRead the full document chunks retrieved for a single chat request (QA or SA). Returns chunk_id, vertex_id, title, full text, score, source, data source type, page metadata, and whether the chunk was cited in the final answer. Use this after read_session_detail to inspect the exacread_chat_request_tool_callsRead the full tool calls executed for a single chat request. Returns tool_call_id, tool_id, tool_name, tool_type, full arguments (JSON), and full content/response (JSON). Use this to inspect what the assistant invoked and how the tool responded. Ordered by timestamp ascending.read_search_request_resultsRead the full search results returned for a single instant-search request. Returns chunk_id, vertex_id, title, full text, and relevance score. Use this to inspect the exact results a user saw for their search query. Ordered by score descending.get_active_sessions_by_embedTime series of active sessions broken down by embed type (chat, chat_widget, navigator, search_interface, voice, api). Use this to understand which interfaces are driving usage.update_chat_request_analyticsUpdate analytics fields on a chat request: summary, tags, answer_type, sentiment, classification_topic_id, or classification_intent_id. Only provided fields are updated. Use this to annotate or reclassify chat interactions after the fact.Recent checks
Link
https://api.branchly.io/mcpQuestions about Branchly in Muse
How do I connect Branchly to Muse?
Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use Branchly to help me. It is a free service with an MCP server at https://api.branchly.io/mcp. It needs an API key from Branchly; ask me to enter it through your secure credential prompt. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.
Is Branchly working right now?
At the last check (September 28, 2026, 09:01 UTC) the endpoint was working, answering in 1379ms. Over the last 7 days it answered 100% of health checks. It is checked every 15 minutes.
Is Branchly safe to connect to Muse?
It was screened on September 25, 2026 with the result "screened, no issues found". Screening checks the domain against threat feeds and reads the tools for hidden instructions and requests for passwords or card numbers. It cannot see the server's code, so grant only the access you need.
What can Branchly do in Muse?
It exposes 37 tools, including get_application, list_nodes, create_node, read_node. For example, you could ask Muse: "Show me which help articles my customers are asking about most and how satisfied they are with the answers"
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