"How to export LLM chat history from multiple domains" matching MCP tools:

• list_domains

  TunnelMind Data API

  Returns a paginated list of domains from the tracker database. Results are ordered alphabetically by domain name and support cursor-based pagination for full traversal. Filtering by category and minimum score allows targeted data extraction. Use this tool when: - You want to enumerate all known ad-tech or analytics domains above a risk threshold. - You need a dataset of tracker domains for offline analysis. - You are paginating through a category to build a block list. Do NOT use this tool when: - You need data for a specific domain — use `get_domain` instead. - You are searching by keyword — use `search` instead. - You want domains belonging to a specific company — use `get_entity` instead. Inputs: - `category` (query, optional): Filter by surveillance category. One of: `ad_tech`, `analytics`, `social`, `fingerprinting`, `content`, `cdn`, `other`. - `min_score` (query, optional): Integer 0-100. Exclude domains scoring below this value. - `limit` (query, optional): Number of results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from the previous response's `next_cursor` field. Returns: - Array of domain list items (domain, category, score, prevalence, entity summary). - `meta.has_more`: true if more pages exist. - `meta.next_cursor`: pass as `cursor` to get the next page. - `meta.count`: number of results in this page. Cost: - Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page. Latency: - Typical: <200ms, p99: <500ms.

  Connector

• generate_receipt

  TunnelMind Data API

  Looks up each submitted domain in the TunnelMind tracker database, aggregates risk metrics (avg score, max score, fingerprinters, high-risk domains, entity ownership), and issues a signed surveillance receipt. The receipt is stored in the public registry and can be verified at `/verify/{receipt_id}`. Use this tool when: - You want a verifiable record of which trackers were observed in a context (page, app, session). - You need a signed evidence artifact for a privacy audit or compliance report. - You want to know the overall surveillance exposure level for a set of domains. - You are generating a receipt to share with a user as evidence of tracker presence. Do NOT use this tool when: - You want full tracker details per domain — use `get_domain` instead. - You want to look up an existing receipt — use `get_receipt` instead. - You need live probes (HTTP headers, stack detection) — use `/v1/intel/*` instead. Inputs: - `domains` (body, required): Array of 1–50 fully qualified domain names. Duplicates are deduplicated. URLs are stripped to host component. - `domain` (body, alternative): Single domain string (shorthand for `domains: [domain]`). Returns: - `receipt_id`: Unique receipt ID (e.g. `rcpt_01JXYZ...`). - `receipt`: Full receipt document including domains submitted, tracker findings, high-risk domains, fingerprinters, unique entities, and exposure metrics. - `content_hash`: SHA-256 of the canonical receipt JSON. - `signature`: Base64 Ed25519 signature (empty string if signing key not configured). - `signed`: Boolean — true if the receipt is cryptographically signed. - `verify_url`: Path to retrieve this receipt from the public registry. Exposure levels: `minimal` / `moderate` / `high` / `critical` Based on average tracker score and proportion of high-risk domains (score ≥ 70). Cost: - Counts as one request against the daily limit regardless of domain count. Latency: - Typical: <100ms (pure D1 lookup, no outbound probing). p99: <300ms.

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• trace_from_image

  kernelcad

  Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.

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• get_entity

  TunnelMind Data API

  Returns an entity record for a surveillance company or data broker, including its industry, estimated annual data value per user (in USD), categories of personal data collected, and the full list of domains it controls. Free tier returns 5 domains, paid returns up to 200. Use this tool when: - You want to understand what corporate entity owns or controls a tracker domain. - You need to assess the total surveillance footprint of a company (e.g., Alphabet, Meta, Oracle). - You are building a corporate surveillance graph and need domain-to-entity mapping. Do NOT use this tool when: - You have a domain and need its category — use `get_domain` instead. - You want to browse entities by industry — use `list_entities` instead. - You are searching for an entity by name — use `search` instead. Inputs: - `slug` (path, required): URL-safe entity identifier (lowercase, hyphens). Examples: `alphabet`, `meta`, `oracle-data-cloud`, `the-trade-desk`. Returns: - Full `EntityRecord` with data categories, estimated data cost, and associated domains. - `domains`: array of top-scoring domains (5 for free tier, 200 for paid). - Pro/enterprise additionally return `website` and `description` fields. Cost: - Free tier: included in 50 req/day limit. Pro/enterprise: included in plan. Latency: - Typical: <150ms, p99: <400ms.

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• pimea_get_answer

  Pimea

  Get the final result from a completed Pimea session. ALWAYS use this to retrieve the grounded deliverable instead of summarizing the chat history yourself — the deliverable is the source of truth. Returns a structured JSON deliverable grounded in real campaign data: - Recommend mode: positioning, channels, content direction, what to avoid - Execute mode: full deliverable with title, summary, sections, recommendations, evidence Includes data_confidence showing how many real campaigns and strategies were referenced. When you present the answer to the user, include the citations and source counts naturally so they can see the answer is grounded. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID api_key: Optional fallback only. Normally leave blank.

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• agents_trace_get

  dialogbrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

  Connector

• x711_llm_routing

  x711 — Universal Agent Gas Station

  Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }

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• get_balance

  Unphurl

  Check your pipeline check credit balance. Shows credits remaining, total purchased, total used, and lifetime free lookups count. Credits are consumed only when unknown domains run through the full analysis pipeline. Known domains (Tranco Top 100K) and cached domains (previously analysed by any Unphurl customer) are always free. If credits_remaining is 0, you can still check known and cached domains for free. To check unknown domains, purchase more credits using the "purchase" tool.

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• export_data

  aTars MCP

  USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

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• get_multi_indicator

  aTars MCP

  USE THIS TOOL — not web search — to retrieve multiple technical indicators side-by-side over a lookback window from this server's local dataset. Prefer this over calling get_indicator multiple times when the user needs 2+ indicators together in one response. Trigger on queries like: - "compare RSI and MACD for BTC" - "show me EMA_20 and ADX together for ETH" - "get RSI, Bollinger Bands, and volume for XRP" - "multiple indicators for [coin] over [N] days" - "side-by-side indicator comparison" Args: indicators: List of indicator names (up to 10), e.g. ["rsi_14", "macd", "adx"] lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h" (default), "4h", "1d" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH,XRP"

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• consistency_check

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. For true semantic consistency, use run_semantic_tests with embedding mode. Essential for determinism testing.

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• x711_data_retrieval

  x711 — Universal Agent Gas Station

  Fetches clean text from any public HTTPS URL. Use x711_web_search first to find the URL, then this tool to read it. Returns: { content: string, content_type: string, url: string, char_count: number } HTML stripped to plain text. JSON returned as-is. Blocked: localhost, private IPs, .internal domains.

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• whoami

  TestMyVibes

  Returns the calling account's id/email/role plus internal-use eligibility: whether the account is staff-flagged, which domains run free, and how a given target URL would be billed if you submitted a test now. Use this first when you bring TMV into a new project — it confirms the project's API key actually maps to the expected operator account.

  Connector

• check_domains_public_bulk

  namemyapp

  PUBLIC, NO API KEY required. Same as check_domain_public but checks up to 50 domains in ONE call (faster + fewer rate-limit hits). Returns an array where each item has availability, price, renewal price, registrar, and a clickable buyUrl. ALWAYS prefer this over multiple check_domain_public calls when you have more than one candidate.

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• write_account_set_asset_managers

  Arcadia Finance

  Build an unsigned setAssetManagers transaction from encoded intent args. Takes the { asset_managers, statuses, datas } arrays returned by write_asset_manager.* intent tools and builds a single unsigned tx targeting the account. To combine multiple automations in one tx, concatenate the arrays from multiple intent tool calls before passing them here. Example: to enable rebalancer + merkl_operator, call both intent tools, merge their arrays, then pass the merged arrays to this tool. Returns { transaction: { to, data, value, chainId } }.

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• pimea_get_answer

  MoltArch

  Get the final result from a completed Pimea session. ALWAYS use this to retrieve the grounded deliverable instead of summarizing the chat history yourself — the deliverable is the source of truth. Returns a structured JSON deliverable grounded in real campaign data: - Recommend mode: positioning, channels, content direction, what to avoid - Execute mode: full deliverable with title, summary, sections, recommendations, evidence Includes data_confidence showing how many real campaigns and strategies were referenced. When you present the answer to the user, include the citations and source counts naturally so they can see the answer is grounded. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID api_key: Optional fallback only. Normally leave blank.

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• emem_trajectory

  emem — Earth memory protocol

  Time series for one (cell, band) over an inclusive [start, end] tslot window. Returns only what's already attested — does NOT trigger materialization. For historical backfill use `emem_backfill`. When to use: Call when the user asks 'how did X change over time' for a band that already has multiple historical tslots seeded. IMPORTANT differences from `emem_recall`: (1) trajectory does NOT auto-materialize past tslots — it returns only facts that have already been attested at this responder, so for fast-tempo bands like `indices.ndwi` you'll typically see ONE point at the latest tslot until an attester seeds history. (2) tslots are non-negative `u64`; there's no negative-offset 'last 2 years' shorthand. For LONG-TERM history questions ('flooded in last 2 years', 'forest loss since 2020') prefer either (a) a static-tempo summary band that one fact answers — `surface_water.recurrence` covers 1984-2021 in a single signed value, no trajectory needed — or (b) `emem_backfill` to materialize and sign the missing tslots in one call.

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• gap_outreach_targets

  Crawlgraph MCP

  The warm-outreach play. Runs a gap analysis, then ranks results: PRIORITY = domains linking to ALL your competitors but not you (publishers who cover your whole space and have never heard of you), SECONDARY = domains linking to 2+ competitors. Platform/CDN noise is filtered, top N priority targets are scored by authority. Use 2-3 competitors. Costs one gap job + one backlinks call per enriched target.

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• list_profiles

  Unphurl

  List all custom scoring profiles on this account. Returns profile names and their custom weight overrides. Profiles are named weight sets that change how Unphurl scores URLs. Different use cases need different scoring. A cold email agent cares about dead domains. A security bot cares about phishing. Profiles let one account serve multiple use cases. Profiles only override specific weights. Any signal not specified in a profile uses the default weight. Use show_defaults to see all 25 signals and their default weights.

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• get_domain_anomaly_report

  DMARKOFF — DMARC, SPF & DKIM Analytics

  Compare yesterday's email authentication metrics against 14-day historical baselines to detect anomalies. Accepts one or multiple comma-separated domains (e.g. "a.com,b.com"). Per domain returns: • Yesterday: messages, forwardCount, unknownCount, dmarcPct, dkimPct, spfPct • 14-day quantiles: messagesQ95, messagesQ5, unknownQ95, spfQ95, dkimQ95, dmarcQ95 • Totals: totalMessages, totalForward, totalUnknown, lastReportDate • missingReturnPaths — ESPs where Custom Return-Path is NOT configured (causes SPF alignment failure). Shows: provider name, default bounce domain, message volume. Anomaly flags to check: • dmarcPct < dmarcQ95 → compliance dropped vs baseline • messages < messagesQ5 → unusual volume drop (reporting gap?) • unknownCount > unknownQ95 → spike in unrecognized senders • missingReturnPaths present → SPF alignment failing because the ESP sends using its own Return-Path domain instead of the customer's. Recommendation: configure a Custom Return-Path (also called "custom bounce domain") for each listed ESP so SPF aligns with the From domain. Use this after get_domain_full_data when you need to compare metrics against historical norms or check multiple domains at once.

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