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132,948 tools. Last updated 2026-05-10 12:07

"A server for generating daily and weekly Slack channel summaries with next steps" matching MCP tools:

  • Fetches an AI-synthesised Moon-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream horoscope service for a lunar sign (English or Sanskrit input accepted; response normalises moon_sign to lowercase English) and a period of daily, weekly, monthly, or yearly. It returns narrative and checklist-style content for life areas, remedy, and timing flavour text. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level transit-flavoured copy tied to the requested horizon. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_natal_chart — if the user needs a personalised chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). moon_sign accepts Sanskrit (Tula, Vrischika, Karka, Simha, Kanya, Dhanu, Makara, Kumbha, Meena, Mesha, Vrishabha, Mithuna) or English (Libra, Scorpio, Cancer, Leo, Virgo, Sagittarius, Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini); resolution is upstream. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: do[] (string array) body (string) love (string) avoid[] (string array) money (string) career (string) remedy (string) headline (string) narrative (string) open_loop (string) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; identifier for other horizons) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.moon_sign (string — lowercase English, e.g. 'libra') SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported moon_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sign-level content only; not a substitute for birth-chart analysis. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — full personalised sidereal chart from birth data, not Moon-sign editorial copy. asterwise_get_gochar — nine-planet transit snapshot vs natal chart for today, not AI horoscope prose.
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  • Updates fields on an existing automation. Pass a partial updates object with only the fields you want to change; omitted fields are preserved. Toggling enabled or changing schedule/channel/condition takes effect on the next scheduled run. Behavior: - Saves the change to the same automation record. Scheduled automations with an active workflow are restarted on update so the next run picks up the latest config. - Errors when the perspective or automation is not found, or you do not have access. - Webhook URLs in updates are validated. For HubSpot, the workspace's HubSpot connection is re-checked — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if disconnected. - For scheduled automations: changes to channel, condition, execution mode, instruction, or message template apply starting from the next run, not the one currently in flight. When to use this tool: - Toggling enabled on or off (also pauses/resumes scheduled sends). - Changing schedule, channel, condition, instruction, or message_template on a live automation. When NOT to use this tool: - Removing the automation entirely — use automation_delete. - Verifying a config change actually delivers — follow up with automation_test. - Listing what's configured — use automation_list.
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  • Creates an automation on a perspective. Triggers: per_interview (fires on every completed conversation) or scheduled (daily/weekly digest). Channels: webhook, email, slack, hubspot. Execution modes: direct (fast, deterministic) or agent (LLM-powered). Behavior: - Each call creates a new automation — even if name/config matches an existing one. - Once enabled, the automation starts firing on real events: per_interview sends on every completed conversation going forward; scheduled sends a real message on the configured cadence (daily/weekly). - Webhook URLs are validated. For HubSpot, the workspace's HubSpot connection is required — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if not connected. - Errors when the perspective is not found or you do not have access. When to use this tool: - The user wants ongoing notifications on every completed conversation (per_interview). - Building a daily/weekly digest delivered to Slack, email, HubSpot, or a webhook (scheduled). When NOT to use this tool: - Trying a one-off send before going live — create the automation, then use automation_test (use override_email / override_webhook to avoid hitting real recipients). - Editing or toggling an existing automation — use automation_update. - Connecting Slack or HubSpot — use integration_manage first; the provider must be connected before slack/hubspot channels work. Example — per-conversation Slack notify: ``` { "perspective_id": "...", "automation": { "name": "Notify Slack", "trigger": { "type": "per_interview" }, "execution_mode": "agent", "channel": { "type": "composio", "delivery_config": { "provider": "slackbot", "tool_slug": "SLACKBOT_SEND_MESSAGE", "params": { "channel": "#research" }, "resource_id": "...", "resource_name": "..." } } } } ``` Typical flow: 1. integration_manage (operation: "list"/"connect") → ensure Slack / HubSpot is connected (only needed for those channels) 2. automation_create → create the automation 3. automation_test (with overrides) → verify delivery before relying on it
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  • Search the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).
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  • List the AI engine channels tracked by Peec. A model channel is a stable identifier for an AI engine (e.g. "openai-0" = ChatGPT UI) that persists even as the underlying model is upgraded — use it to filter or break down reports by engine without worrying about model version changes. Use this tool to resolve channel descriptions (e.g. "ChatGPT UI", "Perplexity") to channel IDs before filtering reports (model_channel_id filter), and to label channel IDs from report output before presenting results. The current_model_id column gives the model ID currently active in the channel — pass this as model_id where reports require it. is_active indicates whether the channel is enabled for this project — inactive channels return empty data. unsupported_country_codes lists country codes that cannot be used with this channel (chats requested for those countries are not created). Returns columnar JSON: {columns, rows, rowCount}. Columns: id, description, current_model_id, is_active, unsupported_country_codes.
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  • Send an enquiry to a Cyclesite seller on the buyer's behalf — Cyclesite becomes the messaging layer for the AI conversation. Per-buyer-per-listing daily cap (2/day) prevents spam. The seller is emailed; the buyer's reply appears via get_my_enquiries. Requires OAuth scope `enquiries:respond` (note: the scope name is shared with seller-side replies). Example: 'message the seller of that Trek and ask if they'd take £1,400 collection only in Manchester next Saturday'.
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Matching MCP Servers

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    Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.
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  • Slack MCP for self-host or managed Cloud, with Gemini CLI and secure-default HTTP.

  • Enable interaction with Slack workspaces. Supports subscribing to Slack events through Resources.

  • Complete staking portfolio dashboard in a single call. Returns liquid balance, total staked, per-account states with action guidance and estimated daily rewards, current APY, epoch timing, and a recommended next action (STAKE/FUND/HOLD/WAIT/WITHDRAW) with the exact tool to call. Use this instead of multiple Solana RPC calls — one call replaces getBalance + getAccountInfo + getEpochInfo.
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  • Event counts over time as date buckets. Returns [{ date, count }] sorted ascending. Granularity is automatic based on period length (hourly for ≤2 days, daily for ≤90 days, weekly for ≤365 days, monthly beyond) and can be overridden via `granularity`. Filterable to a specific event name (defaults to "pageview") and a single custom property key/value pair. Examples: - "pageview trend last week" → period="7d" - "signups per day this month" → event="signup", period="30d", granularity="day" - "hourly pageviews yesterday" → period="1d", granularity="hour" Limitations: forcing granularity="hour" over a 90-day period produces hundreds of buckets and may be truncated server-side. Buckets with no matching events return zero (the series does not skip missing dates).
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  • Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context.
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  • Retrieve shipment volume overview: total shipment count and daily shipment counts over time. Returns `total_shipments_count` and `shipments_count_by_date` (a time series with daily totals and a `max` peak value). **Use this tool for:** - "How many shipments did I create?" — total count for a period - "Show me my shipment volume trend" — daily time series - Overall shipment volume and trends over time **Do NOT use this tool for:** - Destination-specific analytics → Use `analytics_top_destinations` - Shipped/label-generated counts → Use `analytics_shipped` - Shipment status breakdown → Use `analytics_shipment_status` - Courier performance → Use `analytics_top_couriers` - Sales channel analytics → Use `analytics_sale_channels` **Date range:** Unless the user specifies otherwise, default to `to_date` = today and `from_date` = 90 days prior. Required authorization scope: `public.analytics:read` Args: from_date: Start date in YYYY-MM-DD format. Default to 90 days before to_date if user doesn't specify. to_date: End date in YYYY-MM-DD format. Default to today if user doesn't specify. Returns: Total shipment count and daily shipment count time series for the date range.
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  • Get a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale.
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  • Retrieve structured analysis results generated by Echosaw for a completed job, including summaries, transcripts, detected entities, events, and other intelligence outputs.
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  • Get a compact intelligence digest for a set of brands — perfect for watchlist summaries, competitive briefings, and daily reports. Returns for each brand: current signal, AI visibility score+trend+grade, key relationship edges (integrations, powered-by, acquisitions), and capabilities. Excludes competitive edges to keep output focused. Args: slugs: List of brand slugs (up to 25). Returns: Dict with "digest" array (one entry per brand) and "missing_slugs".
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  • Search parliamentary divisions (votes) in the Commons or Lords. Returns division summaries including title, date, vote counts, and whether the motion passed. Use votes_get_division with the division ID for full voter lists.
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  • Top-N source dimensions over a time window. Useful for situational awareness — 'where is the noise coming from right now?' Use this tool when: - Building a daily/weekly threat-landscape briefing. - An agent wants to know which ASNs/countries/protocols dominate recent traffic. - Generating a defender dashboard. Do NOT use this tool when: - You want details about one specific IP — use scry_check. - You want a time-series trend — use scry_timeseries. Inputs: - dimension (default 'asn'): one of asn, country, protocol, port. - since (default last 24h): unix ms. - limit (default 20, max 100). - include_noise (default false): include known scanners (Shodan, Censys, etc.). Returns: array of { value, observations, distinct_source_ips }, sorted desc.
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  • Get a multi-day weather forecast for any Swiss location. Returns daily summaries with temperature, precipitation, and weather icons. This uses official MeteoSwiss Open Data — the same forecasts powering the MeteoSwiss app and website. Accepts: - Postal codes: "8001" (Zurich), "3000" (Bern), "1200" (Geneva) - Station abbreviations: "ZUE" (Zurich Fluntern), "BER" (Bern) - Place names: "Zurich", "Basel", "Lugano" Coverage: ~6000 Swiss locations (all postal codes + weather stations + mountain points). Forecast horizon: up to 9 days. Updated hourly.
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  • Complete staking portfolio dashboard in a single call. Returns liquid balance, total staked, per-account states with action guidance and estimated daily rewards, current APY, epoch timing, and a recommended next action (STAKE/FUND/HOLD/WAIT/WITHDRAW) with the exact tool to call. Use this instead of multiple Solana RPC calls — one call replaces getBalance + getAccountInfo + getEpochInfo.
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  • Check the SOL balance of any Solana wallet address. Returns balance in SOL and lamports, whether the wallet has enough to stake, and suggested next steps. Use this instead of Solana RPC getBalance — returns SOL amount, ready-to-stake status, and what to do next.
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  • Retrieve and re-evaluate a previously created funnel against current data for the specified period. Without a `name`, lists all funnels saved for the project. With a `name`, returns the same step-by-step counts and conversion rates as funnels.create, recomputed for the requested period and any cohort filters. Cohort filters (channel, country, device_type, utm_*) let you compare conversion across segments — e.g. mobile users from the US who came via organic search. Examples: - list all funnels → no params - "how is pricing-to-signup converting this month" → name="pricing-to-signup", period="30d" - "mobile conversion for onboarding" → name="onboarding", device_type="mobile" - "paid traffic vs organic conversion" → call twice with channel="paid" then channel="organic_search" Limitations: returns 404 if no funnel exists by that name — call funnels.list with no name first to enumerate. Cohort filters apply at the session level, not retroactively per step. Funnel definitions are immutable after creation (re-create with a new name to change steps).
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