analyze_entry_text
ChatGPTRun a custom instruction or question across all entries in a Sample Reference and return prevalence statistics plus example results. Requires sample_reference_id each call — use the id from sample_feedback_entries or classify_feedback_entries output (stateless server does not retain prior samples).
classify_feedback_entries
ChatGPTClassify entries in a Sample Reference into predefined labels and create a sub-sample per label. Pass sample_reference_id from sample_feedback_entries or a prior classify call; MCP output lists sample_reference_id per label for follow-up tools.
discover_filter_schema
ChatGPTList all available filters for a view. Returns every filter name that can be used for filtering. Use this at the start of the conversation to discover the available filters.
get_group_insights_playbook
ChatGPTCall this when the user wants root cause analysis, next steps, what is driving a feedback group, issue resolution, support insights, solution insights, or bot capability gaps on an Unwrap taxonomy group. Returns a step-by-step workflow playbook to run with other Unwrap tools (sample_feedback_entries → analyze_entry_text → classify_feedback_entries). Does NOT return the insights themselves.
get_started
ChatGPTCall after list_views to load the full Unwrap usage guide and color palette for the selected view. Recommended at the start of every MCP session, but the server is stateless and other tools work without it.
leave_feedback
ChatGPTLeave feedback for the Unwrap team about your experience using this MCP — when a tool produced a confusing result, a workflow felt awkward, or you'd recommend a change. If you don't have confident answers for every field, ask the user yourself before calling this tool. Do not include user PII.
list_feedback_integrations
ChatGPTList the views connected feedback integrations and source coverage.
list_segment_field_values
ChatGPTList exact allowed values for a segment. Use this before filtering when you need the precise value spelling.
list_views
ChatGPTGet the authenticated org's view's. Returns view_id, view_name, and org_name for org's view's.
mcp_app_get_field_values
ChatGPTMCP App internal: list allowed values for a filter field so the chart iframe's value picker can autocomplete. Accepts the field's display name or wire name. Returns only structuredContent. LLMs SHOULD NOT call this — use list_segment_field_values instead.
mcp_app_get_filter_fields
ChatGPTMCP App internal: list filterable fields (with allowed operators, value type, and priority) for the chart iframe's filter editor. Returns only structuredContent. LLMs SHOULD NOT call this — use discover_filter_schema instead. Hosts that honor _meta.ui.visibility hide this tool from the LLM tool catalog.
mcp_app_get_plot_data
ChatGPTMCP App internal: re-fetch a chart's data when the user changes filters, date range, chart type, or breakdown_field inside the chart iframe. Pass the same breakdown_field contract as plot_chart (none = All Feedback; Group / leaf_group / source / segment names). Returns only structuredContent (no markdown body) so the LLM transcript isn't polluted by every interactive filter change. LLMs SHOULD NOT call this tool — use plot_chart instead. Hosts that honor _meta.ui.silent will hide this tool from the LLM tool catalog.
plot_chart
ChatGPTPlot charts and tables over time or by breakdown. Use this when the user is asking about an issue or group over time, when they want top groups or top issues shown visually, or when a visual comparison would make the answer clearer. Use exactly one input series by default. Multiple input series are allowed ONLY if the user explicitly requests separately defined series or no single-series breakdown can represent the requested chart. Comparisons and multiple visible lines/bars do not by themselves require multiple input series. Use filter + breakdown_field: scope with series filters (e.g. Group = Notifications OR Group = Messages), then set breakdown_field to split that population (Group, leaf_group, source, or a segment name like Sentiment). Use breakdown_field=none (All Feedback) for a single volume line under the filters. When using multiple input series, explain which exception applies in explanation. Choose the chart type based on whether the user wants a time trend, an aggregate ranking, or a share comparison. After calling it, still return written analysis alongside the chart. Use search_segments or list_field_values before filtering or selecting numeric segments.
query_feedback_data
ChatGPTQuery compact raw or aggregate feedback data without building a chart. Use fields for raw values, or group_by with a metric for counts, sums, averages, NPS, or CSAT. Prefer one grouped query over one query per value. Use plot_chart instead for trends or visualizations.
read_documentation_article
ChatGPTRead the full content of one or more Unwrap documentation articles and return them as markdown. Only URLs returned by search_documentation are allowed; any other URL is rejected without being fetched. Use this to ground answers to product, how-to, or integration-setup questions in the official docs.
sample_feedback_entries
ChatGPTCreate a named sample of feedback entries. Use a representative random sample for prevalence, or semantic_query plus instruction for targeted examples. Slug names must be snake_case, 3-50 characters, lowercase letters, numbers, underscores only. MCP output includes sample_reference_id at the end — pass it to analyze_entry_text, classify_feedback_entries, or Sample Reference filters on later calls.
search_actionable_groups
ChatGPTGet the top / biggest / most important groups by magnitude — use this for 'top issues', 'main pain points', or 'where should we focus' questions, optionally sorted by a numeric segment. To find feedback ABOUT a specific topic by semantic relevance, use search_groups instead. Leave query empty to rank purely by volume; pass a query only to narrow the ranking toward a topic. Pair with plot_chart when a visual summary would help.
search_documentation
ChatGPTSearch Unwrap's official product documentation (the public help center) and return a ranked list of matching article titles and URLs. Use this when the user asks how to do something in Unwrap, how a feature works, or how to set up or configure an integration. Pair with read_documentation_article to read an article's content before answering.
search_groups
ChatGPTFind feedback ABOUT a topic — taxonomy groups ranked by semantic relevance to query. For the biggest issues by magnitude ('top issues', 'where should we focus'), use search_actionable_groups instead. If you filter, discover fields and values first instead of guessing them. For Sample Reference predicates use integer sample_reference_id from sample_feedback_entries or classify_feedback_entries output (see discover_filter_schema).
search_segments
ChatGPTSearch for relevant segment fields and values by semantic similarity. Always call discover_filter_schema first to discover available fields before using this tool.
analyze_entry_text
Claudeclassify_feedback_entries
Claudediscover_filter_schema
Claudeget_group_insights_playbook
Claudeget_started
Claudeleave_feedback
Claudelist_feedback_integrations
Claudelist_segment_field_values
Claudelist_views
Claudeplot_chart
Claudequery_feedback_data
Clauderead_documentation_article
Claudesample_feedback_entries
Claudesearch_actionable_groups
Claudesearch_documentation
Claudesearch_groups
Claudesearch_segments
Claude
