Metaview
Access structured interview notes and candidate insights.
Community: Submitted by a user or imported; check the owner before granting accessOnlineNo sign-inGlobalFreeRead-only
What it can do
- Submit Csat: <usecase> Submit a satisfaction rating for this MCP session. IMPORTANT: Only call this tool AFTER you have completed a substantial task for the user and delivered your final response. Nev
- Create Ai Field: <usecase> Create a new AI field or update an existing one. AI fields are the primary tool for analyzing conversations at scale. Each field defines a question that is answered independ
- List Fields: <usecase> List available fields for filtering, grouping, and columns, plus metrics. Returns two SEPARATE lists: 1. fields — filter/grouping field metadata. Field IDs look like "default:st
What data it sees
Do you need an account
No: the server works without sign-in
Access structured interview notes and candidate insights. Search past interviews, review feedback, and track hiring decisions.
How to connect
How to connect Metaview to Claude
The server address to paste into Claude settings:
https://metaview.run.tools
- Open Claude (claude.ai or the desktop app).
- Go to Settings → Connectors.
- Click Add custom connector.
- Paste the server address copied below into Remote MCP server URL and click Add.
- In a chat, click + → Connectors and switch the new connector on.
Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.
Authorization
After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.
How to connect Metaview to ChatGPT
The server address to paste into ChatGPT settings:
https://metaview.run.tools
- Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
- Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
- Open Settings → Connectors and click Create.
- Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
- Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
- In a new chat open + → Apps/Connectors and enable the connector.
OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.
Authorization
On first use ChatGPT opens the service's sign-in window. Sign in and approve access.
How to connect Metaview to Cursor
The server address to paste into Cursor settings:
https://metaview.run.tools
Fastest: click Open in Cursor below and confirm the prompt.
Manually:
- In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
- Paste the JSON copied below into
~/.cursor/mcp.json(per project:.cursor/mcp.jsonin the repo root). - Save the file. The server appears in the MCP list; authorize it there if asked.
Authorization
If the service needs sign-in, an authorize button appears next to the server in the MCP list.
Server tool list (17)
Raw names from tools/list. Only developers need these.
| submit_csat | <usecase> Submit a satisfaction rating for this MCP session. IMPORTANT: Only call this tool AFTER you have completed a substantial task for the user and delivered your final response. Never call it mid-task, never call it as your first action, and never interrupt the user's workflow to ask for a rating. Do not call this tool if the session was trivial (e.g. a single lookup or quick question). Only use it after meaningful work — for example, generating a report, completing multi-step analysis, or building something the user requested. Call it at most once per session. </usecase> <instructions> Args: score: Integer from 1 to 5 where 1 = very unsatisfied and 5 = very satisfied. feedback: Optional free-text feedback from the user explaining their rating. </instructions> <response_format>{"status": "recorded"}</response_format> |
| create_ai_field | <usecase> Create a new AI field or update an existing one. AI fields are the primary tool for analyzing conversations at scale. Each field defines a question that is answered independently for every conversation it is applied to. This means you can extract specific data points from hundreds of conversations without hitting context window limits — each conversation is analyzed in isolation, and the structured results can then be aggregated, filtered, and grouped. WHEN TO USE: When the user wants insights across 20+ conversations, create an AI field to extract the specific data point, then use search_conversations or group_conversations to aggregate the results. This is far more effective than trying to read all transcripts. HELPING USERS APPROACH THE TASK: The tool automatically assesses and improves prompts before creating the field (unless skip_improvement=true), so you do not need to help the user craft the exact prompt — focus on helping them think through their approach: - If the user's request is broad (e.g., 'analyze the interviews'), help them identify what specific questions they want answered. What decisions are they trying to make? What patterns are they looking for? - If a single AI field won't capture what they need, suggest breaking it into 2-3 focused fields (e.g., one for candidate sentiment, one for key objections, one for salary expectations) rather than one catch-all. - Help them think about whether they need per-conversation detail (search) or an aggregate view (group by interviewer, department, etc.). IMPORTANT: Always confirm with the user before calling this tool. Describe the field you plan to create or update (name, prompt, value type) and get explicit approval. AI fields analyze every conversation they're used against, so the user should understand what will be created or modified on their behalf. </usecase> <instructions> Creating: When no field_id is provided, creates a new AI field. Before creating, the tool searches for ex |
| list_fields | <usecase> List available fields for filtering, grouping, and columns, plus metrics. Returns two SEPARATE lists: 1. fields — filter/grouping field metadata. Field IDs look like "default:start_time", "OSPT:<uuid>", or "AI:<uuid>". Use these for filters, group_by, columns, and the group parameter in get_chart_data. 2. metrics — computed summaries you can chart or aggregate. Metric IDs look like "aggregation:session_count", "aggregation:actual_duration", or "aggregation:AI:<uuid>". Use these ONLY as metric_id in group_conversations (with metrics) and get_chart_data (chart_inputs). Do NOT mix them up: field IDs go in filters/group_by/group, metric IDs go in metric_id/chart_inputs. Each metric includes chart_types showing which chart types it supports. Only metrics with "scatter" in chart_types support scatter plots (omitting function/interval in get_chart_data). BEST PRACTICE: Use search_term to find specific fields instead of fetching all. For example, use search_term="department" to find the department field rather than retrieving the full list. </usecase> <instructions> Args: search_term: Filter fields by name (case-insensitive). Supports plain substring matching and regex patterns (e.g. "eng.*|product" is automatically detected as regex when it contains special characters). report_id: Optional report ID to scope fields to a specific report's context. </instructions> <response_format> {"fields": [ {"id": "default:start_time", "name": "Start Time", "type": "date", "description": "When the conversation started", "filterable": true, "groupable": false, "displayable": true, "filter_operations": ["before", "after", "between"], "filter_format": "value MUST be a dict with ..."}, {"id": "OSPT:a1b2c3d4-e5f6-7890-abcd-ef1234567890", "name": "Department", "type": "string", "description": null, "filterable": true, "groupable": true, "displayable": true, "filter_operati |
| list_field_values | <usecase> Get possible values for a specific field. Use this to discover what values are available for filtering. For example, get all department names, interviewer names, or job titles. Essential for looking up person IDs needed by PERSON-type and PARTICIPANT-type filters. BEST PRACTICE: Use search_term when looking for a specific value (e.g., a person's name or a particular department). This returns fewer results and avoids unnecessary data transfer. Can be scoped to a report or filtered set of sessions to show only values that appear in the matching sessions. </usecase> <instructions> Contact lookup — choosing the right field: The same person can have MULTIPLE contact records (e.g. one as an interviewer and one as a candidate). To avoid duplicates: - Looking up an interviewer / employee? Use "default:interviewer". This returns only internal team members (has_access=True) — one result per person. - Looking up a candidate? Use "default:candidate". This returns only external participants (has_access=False). - Don't know the role? Use "default:contact", but beware it may return multiple records for the same person. Check the "is_internal" field in the response to distinguish employees from candidates. IMPORTANT: search_term supports substring matching ("Bart" matches "Bartels", "Bartosz", etc.) and regex matching (e.g. "eng.*team|product" is automatically detected as a regex pattern when the term contains special characters like .*+?[]|()). When multiple results are returned, disambiguate before proceeding. Prefer searching with both first and last name. See search_conversations for the complete filter format reference. IMPORTANT: Date filter values must use a dict with scope and value, not a bare date string. Example: {"field_id": "default:start_time", "operation": "after", "value": {"scope": "relative", "value": -2592000}} Args: field_id: The field ID (from list_fields, e.g., "OSPT:<uuid>", "default:interviewer"). report_id: Optiona |
| get_chart_data | <usecase> Get chart data for aggregate time-series or scatter plots. Each chart_input is processed independently. The chart type is determined automatically from the keys present: - Aggregate chart (line/bar/stacked bar): include `function` AND `interval` in the chart_input. Returns time-bucketed data points. - Values chart (scatter plot): omit `function` and `interval`. Returns one data point per conversation. IMPORTANT: Not all metrics support scatter charts. Check the metric's chart_types from list_fields. Only metrics with "scatter" in chart_types can be used as scatter plots (omitting function/interval). For example, "Conversation Count" only supports bar/line (aggregate) charts, not scatter plots. Use list_fields to discover valid metric_id values and their supported chart types and aggregation functions. </usecase> <instructions> See search_conversations for the complete filter format reference. IMPORTANT: Date filter values must use a dict with scope and value, not a bare date string. Example: {"field_id": "default:start_time", "operation": "after", "value": {"scope": "relative", "value": -2592000}} Args: chart_inputs: Array of chart specifications. Each dict must have: metric_id (str, required) — the metric to chart. Call list_fields to discover available IDs. Metric IDs start with "aggregation:" (e.g. "aggregation:session_count", "aggregation:actual_duration") or "AI:<uuid>" for AI fields. function (str) — one of: Average, Median, Count, Sum, Max, Min, Cumulative, Rolling Average. Required for aggregate charts, must be omitted for scatter charts. Must be in the metric's supported chart_functions (from list_fields). interval (str) — one of: day, week, month, quarter. Required for aggregate charts, must be omitted for scatter charts. Both function and interval must be provided together or both omitted. metr |
| group_conversations | <usecase> Group conversations by a field and compute metrics. Groups conversations by a field (e.g., department, interviewer) and returns counts and metric values per group. Prefer this tool when the user asks for breakdowns, distributions, rankings, or "by X" questions. POWERFUL PATTERN — AI field + grouping for large-scale analysis: When the user wants to find trends or patterns across many conversations, use this workflow: 1. Create an AI field (create_ai_field) that extracts the data point of interest. 2. Group by that AI field here to see the distribution of values. Or group by another field (e.g., interviewer) and use the AI field as a metric to see averages/counts per group. This avoids reading raw transcripts and works at any scale. Use metrics to compute metrics per group (e.g., total questions, average duration). Use list_fields to discover available metrics. </usecase> <instructions> See search_conversations for the complete filter format reference. IMPORTANT: Date filter values must use a dict with scope and value, not a bare date string. Example: {"field_id": "default:start_time", "operation": "after", "value": {"scope": "relative", "value": -2592000}} Args: group_by: Field ID to group by (e.g., "OSPT:<uuid>", "default:interviewer"). Use list_fields to find attributes where can_be_grouped_by is true. report_id: ID of a saved report. filters: Array of filter objects, each with field_id (str), operation (str), and value. only_show_recorded_conversations: When true (default), only return recorded conversations. Set to false to also include unrecorded conversations (scheduled calls that were not recorded, or upcoming conversations that haven't happened yet). search_term: Filter groups by name substring. Use this when looking for a specific group instead of fetching all groups. min_conversation_count: Minimum conversations per group to include (default 0). offset: Pagination offset. limit: Gr |
| search_conversations | <usecase> Search conversations with filters and get tabular data. Returns individual conversations matching the given filters, with configurable fields showing attribute values for each conversation. IMPORTANT - choose the right tool AND approach based on scale: - For "how many" questions, use group_conversations — group by the relevant attribute and read the conversation_count per group, or use a single group to get the total. This avoids fetching full conversation rows when only a count is needed. - For breakdowns by attribute (e.g. by department, interviewer), use group_conversations. - For time-series trends, use get_chart_data. - Only use search_conversations when you need individual conversation rows. SCALE-AWARE STRATEGY — pick the right fields based on how many conversations match: - 1-5 conversations: Use fields=['default:transcript'] to read full transcripts. You can analyze them in detail within your context window. - 5-20 conversations: Use fields=['default:summary'] to get AI-generated summaries. Good middle ground — key points without overwhelming context. Fetch individual transcripts only for conversations you need to drill into. - 20+ conversations: Do NOT fetch transcripts or summaries in bulk. Instead use create_ai_field to extract specific data points per conversation, then query the AI field here or in group_conversations to aggregate results. This is the only way to analyze large numbers of conversations effectively. - When unsure of scale, first run a query with just default fields (no transcripts/summaries) to see total_count, then decide your approach. IMPORTANT - transcript token cost: A single 1-hour transcript uses roughly 10,000-15,000 tokens. Be careful not to fetch too many transcripts or else you may use up all your context. Prefer AI fields over raw transcripts when possible. Transcript format is one line per monologue: Speaker Name (Role): What they said... IMPORTANT - AI fields: AI fields (field IDs starti |
| search_reports | <usecase> List saved reports the user has access to, or fetch full details for specific reports. A report is a saved configuration — a named set of filters, fields, grouping, and charts that defines a particular way to analyze interview conversations. Pass a report's ID to search_conversations, group_conversations, or get_chart_data to reuse its saved filters. Use report_ids to fetch one or more specific reports by ID. Set include_detail=true to get the full configuration (filters, fields, grouping, charts) instead of just the summary. BEST PRACTICE: Use search_term when looking for a specific report by name instead of listing all reports and scanning. </usecase> <instructions> Args: report_ids: List of report IDs to fetch. Returns only these reports. include_detail: When true, returns full report configuration (filters, fields, grouping, charts) for each report. Default false (summary only). search_term: Filter reports by name (case-insensitive). Supports substring matching and regex patterns (automatically detected). category: Filter by category. One of: createdByMe, createdByMetaview, recentlyViewed, favorited. sort_key: Sort field. One of: updated_at (default), name, created_at. sort_order: Sort direction. One of: desc (default), asc. offset: Pagination offset (default 0). limit: Number of results per page (default 20, max 100). </instructions> <response_format> {"reports": [ {"id": "abc-123", "name": "Engineering Interviews Q1", "url": "https://<app-domain>/reports/abc-123", "description": "All engineering interviews in Q1 2024", "is_default": false, "created_by": {"name": "Alice Smith"}, "created_at": "2024-01-01 00:00:00+00:00", "updated_at": "2024-03-15 10:30:00+00:00"}], "count": 1, "has_more": false} </response_format> <examples> search_reports() search_reports(search_term="engineering") search_reports(category="createdByMe", sort_key="updated_at") search_repor |
| create_report | <usecase> Create a new report or update an existing one in the Metaview web-app. IMPORTANT: Only use this tool when the user explicitly asks to create or edit a saved report in Metaview. Do NOT use this tool for ad-hoc data analysis, answering questions, or running queries — use search_conversations, group_conversations, or get_chart_data for those. This tool persists a report that appears in the user's Reports list in the web-app. Before calling this tool, confirm with the user what they want the report to contain (name, filters, grouping, fields). Do not create reports speculatively. After creating or updating a report, share the `url` from the response with the user so they can open it in the web-app. </usecase> <instructions> Creating: Omit report_id. You MUST provide filters (can be an empty list for "all conversations"). Optionally set name, fields, grouping, and other configuration in the same call. Updating: Provide report_id of an existing report. Only the fields you include will be changed — omitted fields are left unchanged. Use list_fields to find valid field IDs for filters, fields, and grouping. Use list_field_values to find valid filter values. Args: report_id: ID of an existing report to update. Omit to create a new report. filters: Array of filter objects (required for create, optional for update). See search_conversations for the complete filter format reference. IMPORTANT: Date filter values must use a dict with scope and value, not a bare date string. Example: {"field_id": "default:start_time", "operation": "after", "value": {"scope": "relative", "value": -2592000}} name: Human-readable report name (e.g., "Engineering Interviews Q1"). description: Report description. group: Field ID to group conversations by (e.g., "OSPT:<uuid>"). Use list_fields to find field IDs. remove_grouping: Set to true to remove grouping from a report. Required because passing group=null is indistinguishable from omit |
| get_enrichment_status | <usecase> Get the workspace's enrichment credit status, usage breakdown, and optionally list individual enrichment attempts. Always returns: - Monthly credit allowance and remaining balance - Usage breakdown by enrichment type (email vs phone) for the requested month - Active top-up credit purchases with remaining balances and expiry dates Optionally returns: - Per-user usage breakdown (include_usage_by_user=true) - Individual enrichment attempts (include_enrichment_attempts=true) </usecase> <instructions> Args: person_id: Filter usage and attempts to a specific person (admin only). Non-admins always see only their own data regardless of this parameter. month: Month to report on as YYYY-MM (e.g. "2026-01"). When provided, usage is scoped to that calendar month. When omitted, usage covers the current month to date (no end boundary). include_usage_by_user: When true, include per-user usage breakdown. Default false. include_enrichment_attempts: When true, append the list of individual enrichment attempts. Default false. enrichment_type: Filter attempts by type: "email" or "phone". Only used when include_enrichment_attempts=true. offset: Pagination offset for attempts (default 0). limit: Number of attempts per page (default 50, max 100). </instructions> <response_format> {"credits": { "monthly_limit": 500, "monthly_used": 450, "monthly_remaining": 50, "top_up_credits_available": 200, "total_available": 250, "warning_threshold_reached": false}, "usage": { "email": {"enrichments_count": 50, "credits_used": 150}, "phone": {"enrichments_count": 30, "credits_used": 300}, "total_credits_used": 450}, "usage_by_user": [{...}], "top_ups": [{...}], "enrichment_attempts": [{...}], "enrichment_attempts_total_count": 150, "enrichment_attempts_has_more": true} </response_format> <notes> credits reflects the current live balance regardless of the month parameter. |
| find_candidate_in_sequences | <usecase> Check if a candidate is enrolled in any sequences. Look up a candidate by ID, LinkedIn URL, email address, or phone number and return all sequences they are (or were) enrolled in, including sequences created by other users. Useful before adding someone to a new sequence to avoid duplicate outreach. Provide at least one lookup parameter. If candidate_id is given, it is used directly. Otherwise, linkedin_url/email/phone_number are matched with OR logic. </usecase> <instructions> Args: candidate_id: Candidate ID for direct lookup (preferred when known). linkedin_url: LinkedIn profile URL (normalized automatically). email: Email address to match against candidate primary email or the to_email used in the sequence. phone_number: Phone number to match against candidate primary phone. </instructions> <response_format> {"candidates": [ {"candidate_id": "...", "candidate_name": "John Doe", "candidate_email": "john@example.com", "candidate_phone": "+1234567890", "candidate_linkedin_url": "https://www.linkedin.com/in/johndoe", "sequences": [ {"sequence_id": "...", "sequence_name": "Outreach v2", "status": "active", "created_at": "2024-01-10T...", "steps_sent": 2, "steps_total": 4}]}]} </response_format> <examples> Check by LinkedIn: find_candidate_in_sequences(linkedin_url="https://linkedin.com/in/johndoe") Check by email: find_candidate_in_sequences(email="john@example.com") Check by multiple identifiers: find_candidate_in_sequences(linkedin_url="...", email="john@example.com") </examples> |
| manage_candidate_sequence | <usecase> Add candidates to a sequence, or pause, resume, cancel, remove, or update a candidate's enrollment. Dispatches based on the action parameter. Only the sequence creator can perform these actions. IMPORTANT: Always confirm with the user before calling this tool. Describe the exact changes you plan to make and get explicit approval first. </usecase> <instructions> Args: action: Required. One of: add, pause, resume, cancel, remove, update. - cancel: Stop sending further emails but keep the candidate visible in the sequence. - remove: Delete the candidate from the sequence entirely (archive/soft-delete). sequence_id: Required for add. candidate_ids: Required for add. List of candidate IDs to enroll. candidate_sequence_id: Required for pause, resume, cancel, remove. candidate_sequence_step_id: Required for update. Get this from list_sequence_candidates(sequence_id=..., include_detail=true). subject_override: Optional for update. Custom subject for this candidate's step. Supports {{variable_name}} placeholders filled in per-candidate by AI before sending. template_override: Optional for update. Custom body for this candidate's step. Supports {{variable_name}} placeholders filled in per-candidate by AI before sending. clear_subject_override: Optional for update. Set true to revert subject to template default. clear_template_override: Optional for update. Set true to revert body to template default. schedule_override: Optional for update. JSON object to override the step's schedule. Supported types: {"schedule_type": "instant"}, {"schedule_type": "next_business_day", "time_of_day": "HH:MM"}, {"schedule_type": "later", "delay_seconds": N}. Setting a specific time is NOT supported. clear_schedule_override: Optional for update. Set true to revert schedule to template default. context_source_type: Optional for add. Must be one of 'search' or 'project'. If |
| manage_sequence | <usecase> Create, update, duplicate, or delete a sequence. Dispatches based on the action parameter. Only the creator of a sequence can update or delete it; admins and creators can duplicate. Test sends, email previews, and send-now are NOT available via this tool. Direct users to the Metaview web app for these. IMPORTANT: Always confirm with the user before calling this tool. Describe the exact changes you plan to make and get explicit approval first. </usecase> <instructions> Args: action: Required. One of: create, update, duplicate, delete. sequence_id: Required for update, duplicate, delete. name: Required for create, optional for update. description: Optional for create and update. set_enabled: Optional for create and update. Enable or disable the sequence. steps: Optional for update. A JSON list of step objects to replace the current steps. Steps not in the list are deleted. Order in the list determines step order. Each step object has: sequence_step_id: str | null (include the existing step ID to update it; omit or set null for new steps — an ID will be generated automatically) from_person_id: str | null (person ID of sender) template: str | null (email body template; supports {{variable_name}} placeholders that are filled in per-candidate by an AI call before sending) subject: str | null (subject line — required for NEW_THREAD steps; also supports {{variable_name}} placeholders) step_type: str | null (NEW_THREAD or REPLY) description: str | null schedule: dict | null (e.g. {"schedule_type": "instant"}, {"schedule_type": "later", "delay_seconds": 86400}, {"schedule_type": "next_business_day", "time_of_day": "09:00:00"}. Setting a specific time is NOT supported. cc: list[str] | null bcc: list[str] | null step_execution_type: str | null |
| list_send_on_behalf_permissions | <usecase> List send-on-behalf-of permissions for the current user. Two directions: - 'granted_to': People who have permission to send sequence emails on my behalf. - 'granted_by': People who have granted me permission to send sequence emails on their behalf. The from_person_id field on a sequence step determines who the email is sent as. To send as someone else, that person must have granted you send-on-behalf permission. </usecase> <instructions> Args: direction: Required. One of: granted_to, granted_by. granted_to: People who can send on my behalf (I granted them permission). granted_by: People whose behalf I can send on (they granted me permission). search_term: Optional. Filter results by name or email. offset: Optional. Number of results to skip (default 0). limit: Optional. Maximum number of results to return. </instructions> <response_format>{"persons": [{"person_id": N, "name": "...", "email": "..."}], "total_count": N}</response_format> <examples> Who can send on my behalf: list_send_on_behalf_permissions(direction="granted_to") Whose behalf can I send on: list_send_on_behalf_permissions(direction="granted_by") Search: list_send_on_behalf_permissions(direction="granted_by", search_term="jane") </examples> |
| list_sequence_candidates | <usecase> List candidates enrolled in a sequence, or get a specific candidate's full journey. Non-admins can only access sequences they created. By default returns a summary per candidate. Use include_detail for per-step delivery status, and include_messages to see the actual email content. Pass candidate_sequence_id to jump straight to a specific candidate's full detail. </usecase> <instructions> Args: sequence_id: UUID of the sequence (required unless candidate_sequence_id is provided). candidate_sequence_id: Fetch a specific candidate's full journey directly. When provided, include_detail and include_messages are implied. include_detail: When true, include per-step delivery status (sent, delivered, opened, replied, bounced, skipped) and candidate_sequence_step_id for each candidate. Required to get step IDs for updates via manage_candidate_sequence. Default false. include_messages: When true, include email content for all sent steps. Requires include_detail. Default false. status: Filter by status: "active", "paused", "cancelled", or "completed". Omit to see all statuses. search_term: Filter by candidate name (case-insensitive substring match). offset: Pagination offset (default 0). limit: Number of results per page (default 50, max 100). </instructions> <response_format> {"sequence_id": "...", "sequence_name": "Outreach v2", "candidates": [ {"candidate_sequence_id": "...", "candidate_id": "...", "candidate_name": "John Doe", "candidate_email": "john@example.com", "candidate_phone": "+1234567890", "to_email": "john@example.com", "status": "active", "paused_reason": null, "cancelled_reason": null, "outcome": null, "created_by": {"name": "Alice Smith"}, "created_at": "2024-01-10T...", "steps_sent": 2, "steps_total": 4, "last_sent_at": "2024-01-15T...", "has_reply": true, "steps": [{"candidate_sequence_step |
| list_sequences | <usecase> List sequences the current user has access to, or get full details for a specific sequence. Admins can see all sequences in the workspace. Non-admins can only see sequences they created. Archived sequences are always excluded. Returns summary data including per-sequence stats so the caller can compare sequences without further round-trips. Pass sequence_id or include_detail=true for full step configuration. </usecase> <instructions> Args: sequence_id: Fetch a specific sequence with full detail (steps, templates, etc.). When provided, include_detail is implied. created_by_person_id: Only return sequences created by this person. Useful for admins who want to see just their own sequences. search_term: Filter sequences whose name contains this substring (case-insensitive). Also supports regex when the term contains special characters like .*+?[]|(). include_detail: When true, include full step configuration for each sequence. Default false (summary only). include_disabled: Whether to include disabled sequences (default true). offset: Pagination offset (default 0). limit: Number of sequences per page (default 20, max 50). </instructions> <response_format> {"sequences": [ {"id": "...", "name": "Outreach v2", "description": "...", "is_enabled": true, "is_valid": true, "is_modifiable": false, "created_by": {"name": "Alice Smith"}, "created_at": "2024-01-01T...", "updated_at": "2024-01-10T...", "step_count": 3, "stats": {"total_candidates": 42, "total_active": 10, "total_completed": 20, "total_paused": 2, "total_sent": 100, "total_replies": 15, "total_bounced": 3, "total_interested": 8}, "steps": [{...}]}], "total_count": 5, "has_more": false} </response_format> <examples> All enabled sequences: list_sequences(include_disabled=false) Search by name: list_sequences(search_term="outreach") Full detail f |
| get_user_context | <usecase> IMPORTANT: Call this tool FIRST, before any other tool. Returns your identity, role, what data you can access, and an overview of the data model. - participant_id: Your contact UUID — use this to filter for your own conversations. - user_id: Your numeric user ID. - is_admin: Whether you have admin access. - is_paying_plan: Whether you are on a paid plan. Free users have limited access to transcripts, summaries, and AI fields. - data_access: Describes exactly which conversations you can see. Read this before drawing conclusions about completeness of query results. - data_model: Key concepts for understanding the data. </usecase> <instructions>No parameters needed.</instructions> <response_format> {"organization_id": 1, "user_id": 123, "participant_id": "550e8400-e29b-41d4-a716-446655440000", "is_admin": true, "is_paying_plan": true, "data_access": "You can see all conversations in the organization.", "data_model": "## Data model\n..."} </response_format> |