Aunoo Skills — Tools & Recipes

Claude Desktop

Easiest (newer builds): Settings → Connectors → Add custom connector, paste the MCP URL, and sign in to Aunoo when prompted — OAuth, no token to copy.

https://agentic.aunoo.ai/mcp

Or via config file (any build; needs Node.js for the mcp-remote bridge). Add to claude_desktop_config.json, then fully quit & relaunch:

{
  "mcpServers": {
    "aunoo": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://agentic.aunoo.ai/mcp",
        "--header",
        "Authorization: Bearer aunoo_\u2026"
      ]
    }
  }
}

macOS ~/Library/Application Support/Claude/claude_desktop_config.json · Windows %APPDATA%\Claude\claude_desktop_config.json

Claude Code

Run this in your shell (add --scope user to make it available in every project):

claude mcp add aunoo https://agentic.aunoo.ai/mcp --transport http --header 'Authorization: Bearer aunoo_…'

ChatGPT — Custom GPT

Create a Custom GPT → Configure → Actions → import the OpenAPI schema, then set Authentication → API Key → Bearer.

Build a situation report

Turn a breaking event into a sourced, structured brief using the Aunoo tools. Frame the topic, gather and corroborate the event, then draft in an analyst register.

1. Frame the topic. Pull the baseline before reading event detail: get_world_state (current entities & situation), forecast_topic_trajectory (lifecycle phase + growth — is the story climbing or cresting?), forecast_sentiment_inflection (mood + change-point risk), and get_consensus (cross-source agreement, evidence quality, optimistic/pessimistic outliers, milestone forecast).
2. Gather the event. search_news for the event; get_article_full on the 3–5 most relevant items; fetch_related_to_article on the strongest to map the coverage cluster. Find and read the primary source (company statement, regulator notice) and build a timeline from datelines.
3. Balance the sources. Re-pull with get_topic_articles using sampling_pipeline="high_factuality" (or bias_neutral) and confirm each load-bearing claim appears across multiple regions and outlets. Tag each claim primary / multi-source / single-source.
4. Draft in an analyst register. Bottom line → what happened → key facts → timeline → the actor's position → implications → significance → consensus & forecast → outlook → source assessment + confidence → sources. Separate what is cited from what is inferred — state causation only when a source asserts it; otherwise label the link a reported sequence.
5. Verify. Re-query for updates, challenge the strongest claims, and confirm the primary source. Give an explicit confidence rating (High / Moderate / Low) and note what would raise it. Use Aunoo's sentiment/consensus figures verbatim, and cite the article URLs Aunoo returns.

Prefer to let your assistant drive? Fetch a recipe with prompts/get: forecast_brief (forward-looking brief), consensus_analysis (converge/diverge across coverage), or deep_dive (multi-angle research synthesis).

analyze_sentiment_trends

Aggregate sentiment trends over time

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "analyze_sentiment_trends", "arguments": {"topic_id": 1}}}'

forecast_sentiment_inflection

Forecast whether a topic's sentiment is about to turn — momentum, the most recent change-point (date + direction + size), and an inflection-risk flag with a projected sign-flip date. Computed (CUSUM change-point + slope), not summarized. Use it to tell whether the mood is steady, shifting, or about to cross from positive to negative (or vice versa).

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "forecast_sentiment_inflection", "arguments": {"topic_id": 1}}}'

forecast_topic_trajectory

Forecast a topic's lifecycle phase from its daily article-volume curve — emerging / accelerating / cresting / declining / dormant — with growth rate and, when a curve fits, a projected peak date and ceiling. Computed (logistic fit + momentum), not summarized. Use it to tell whether a story is still climbing or already past its peak.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "forecast_topic_trajectory", "arguments": {"topic_id": 1}}}'

get_consensus

Return Aunoo's cached cross-source consensus analysis for a topic — 3-5 categories where coverage converges, with key insights and source breakdown. Refreshed by the foresight worker; falls back to the consensus_analysis recipe when cache is missing.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_consensus", "arguments": {"topic_id": 1}}}'

get_foresight_component

Get Aunoo's ACTUAL in-app chart for a topic as a self-contained HTML document (same server-rendered SVG as the app + public share page: the S-curve with scenarios plotted on it, the H1/H2/H3 columns, consensus cards). kind='horizons' (Future Horizons) or kind='consensus' (Consensus & Outliers); pass a topic_id. Drop the returned `html` VERBATIM into an HTML artifact to show the exact same chart as the Aunoo UI — do not rebuild it. Pair with the future_horizons / consensus_outliers recipes.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_foresight_component", "arguments": {"topic_id": 1}}}'

get_horizons

Return Aunoo's cached Three-Horizons foresight scenarios for a topic — current developments (H1), emerging shifts (H2), longer-horizon possibilities (H3). Cached; refreshed by the foresight worker.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_horizons", "arguments": {"topic_id": 1}}}'

get_polarization_index

Measure how far apart the left and right are framing a topic — a 0–100 score for how separable left- vs right-leaning coverage is in embedding space, plus a trend (widening / narrowing / stable) vs. the prior window. Computed (linear-probe AUC), not summarized. Use it to spot stories fragmenting into incompatible narratives; a widening gap is an early flashpoint signal.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_polarization_index", "arguments": {"topic_id": 1}}}'

get_trust_signals

The five per-article Trust Signals Aunoo shows in the app — ① Source (outlet bias / factual record / credibility) ② Claims (per-claim verdicts when the article has been validated, else how many other outlets carry the story) ③ Origin (AI-generated-content detection) ④ Spread (how many outlets republished it and whether one owner carries most of them) ⑤ Owner (who ultimately owns the outlet). Read-only and free: it reports what has already been measured and starts no new analysis. A signal with tone 'neutral' and rating '—' was NOT measured — that is not a clean result. Takes one article_id or up to 25 article_ids. To fact-check an article that has no claim verdicts yet, call validate_article; to see who else ran the story, get_story_coverage.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_trust_signals", "arguments": {}}}'

get_trust_signals_for_urls

The same five Trust Signals as get_trust_signals, for articles named by URL rather than by Aunoo article id — up to 25 urls. A URL Aunoo holds gets all five stations. Any other URL gets ① Source and ⑤ Owner rated from the outlet domain (Media Bias/Fact Check record, beneficial-owner map) and neutral ② Claims ③ Origin ④ Spread, marked in_corpus=false. Neutral means NOT measured, not clean. Read-only and free. Use this when you hold an article from elsewhere; use get_trust_signals when you have an Aunoo article id.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_trust_signals_for_urls", "arguments": {}}}'

render_horizons_chart

Render the Three-Horizons foresight as a PNG chart — the same S-curve visual as the in-app Future Horizons page (current H1 declining, transition H2 cresting, future H3 rising) over a forward year axis. Returns an image the assistant can show directly. To EMBED the real chart in an HTML/JSX artifact, pass format='data_uri' — the response then includes image_data_uri, a data:image/png;base64 string you drop into <img src> (no need to recreate the chart). Use get_horizons for the scenario text.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "render_horizons_chart", "arguments": {"topic_id": 1}}}'

get_story_coverage

Coverage read for a news article: who else is running the SAME story, and Aunoo's verdict on how it's moving — reach (niche / regional / wide / viral), coordination (none / suspected / confirmed), and spread (organic / forced / mixed), with a plain-English headline. Pass url (preferred) or article_id. Returns: same_story_articles (the authoritative 'who else is running this' set — a vetted near-copy cluster, NOT raw similarity), coordination_events, a propagation_summary (recurrence / expectedness / ownership spread), and the verdict. The reach/coordination axes are computed from the same-story set, so they don't get inflated by topically-similar lookalikes; the related_articles field IS raw topical similarity and is labelled as such — don't read it as coverage. The verdict is populated for a url or a corpus-matched article; for a free-text/no-match input it's null but same_story_articles still returns. URL-based and topic-agnostic: works for ANY article, including topics OUTSIDE this workspace's configured topics. Sibling tools: validate_article for claim-level fact-check, social_story_reach for social amplification on the covered social platforms.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_story_coverage", "arguments": {}}}'

get_article_categories

Get category distribution for a topic

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_article_categories", "arguments": {"topic_id": 1}}}'

get_entity_wiki

Fetch the LLM-maintained wiki summary for a named entity within a topic — who they are, when they appeared, key events involving them. Use this when the user asks about a specific person, org, place, or concept tied to a topic.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_entity_wiki", "arguments": {"entity_name": "OpenAI", "topic_id": 1}}}'

get_world_state

Returns the platform's maintained record of current entities, topic state, and recent events. Prefer this over training-time knowledge for facts about current office-holders, organisational leadership, alliances, and ongoing situations.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_world_state", "arguments": {"topic_id": 1}}}'

search_news

Search for news articles (semantic + keyword fallback)

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "search_news", "arguments": {"query": "What's the latest on AI regulation?", "topic_id": 1}}}'

get_newsletter_job

Poll the status of a queued newsletter job. Returns status ('pending', 'running', 'succeeded', 'failed') plus the result payload (newsletter markdown + stats) when succeeded.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_newsletter_job", "arguments": {}}}'

list_newsletter_jobs

List the calling tenant's most recent newsletter jobs (default 10, max 50). Useful for resuming after a session break or finding an old job id.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "list_newsletter_jobs", "arguments": {}}}'

start_newsletter_job

Run Aunoo's server-side newsletter generator. Queues a job that executes the 5-stage pipeline (fetch → score → sections → deep-dive → writer) and produces a complete markdown draft. Returns job_id immediately; poll get_newsletter_job(job_id) until status='succeeded'. Typical runtime 2-5 minutes. **topic_id is required for a topic-scoped issue.** Pass a topic_id from your workspace scope (use list_capabilities to see topics_in_scope). Omitting topic_id is NOT 'pick one for me' — it runs the cross-topic 'Overall' newsletter spanning every Aunoo topic, which is usually NOT what the user asked for. topic_display alone is just the heading; it does not scope the article pool. Distinct from the 'newsletter' MCP recipe (drafting playbook YOU run) and the 'newsletter' sampling preset (article selection only).

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "start_newsletter_job", "arguments": {"topic_id": 1}}}'

list_capabilities

Return a chat-friendly summary of what this Aunoo connection can do — packs in scope, tool catalogue, named recipes, and a 'how to get started' hint. Call this when the user asks 'what can you do' / 'what's available' / 'how do I get started'.

No parameters.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "list_capabilities", "arguments": {}}}'

fetch_related_to_article

Return articles semantically similar to a given article id via pgvector KNN.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "fetch_related_to_article", "arguments": {}}}'

follow_up_query

Combine previous context with new query results

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "follow_up_query", "arguments": {"query": "What's the latest on AI regulation?", "topic_id": 1}}}'

get_article_full

Fetch a single article by id with full content (no 2000-char truncation). Scoped to the api key's allowed_topic_ids.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_article_full", "arguments": {}}}'

get_topic_articles

Fetch articles for a specific topic with optional sampling

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_topic_articles", "arguments": {"topic_id": 1}}}'

search_articles_by_keywords

Keyword search across title, content, and summary

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "search_articles_by_keywords", "arguments": {"topic_id": 1}}}'

semantic_search_and_analyze

Vector search for semantically similar articles

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "semantic_search_and_analyze", "arguments": {"query": "What's the latest on AI regulation?", "topic_id": 1}}}'

get_validation_job

Poll the status of a queued validation job. Returns status ('pending', 'running', 'succeeded', 'failed') plus the full validation report (verdict, confidence, claims, corroboration, …) in result when succeeded.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "get_validation_job", "arguments": {}}}'

start_validation_job

Queue an independent article fact-check as a background job and return a job_id immediately. Runs the same engine as validate_article but in the worker with no tool-window limit, so it completes reliably on large/complex articles (where validate_article can time out). Poll get_validation_job(job_id) every ~25 s until status='succeeded'. Typical runtime 1-2 minutes. Pass article_id (preferred) or url. URL-based: works for ANY article, including topics OUTSIDE this workspace's configured topics. Corroboration is corpus-bound — an off-topic story shows no corpus corroboration even if widely covered; read web_evidence for the open-web picture.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "start_validation_job", "arguments": {}}}'

validate_article

Run an independent fact-check on a news article: extracts claims, verifies each against source reputation, corroboration across outlets, external fact-check archives, and synthesises a verdict (e.g., 'corroborated', 'contested', 'unverifiable') with a confidence score. Runs inline with a ~25 s budget; subsequent calls on the same article hit the cache and return fast. For a large or claim-dense article that may exceed the tool window, prefer start_validation_job + get_validation_job (same engine, no time limit) — and if this tool returns a timeout, switch to the job tools rather than retrying. URL-based: works for ANY article, including topics OUTSIDE this workspace's configured topics (unlike the topic-scoped search tools). Note: corroboration is checked against Aunoo's indexed corpus, so an article on a topic Aunoo doesn't index shows zero corpus corroboration even when widely covered elsewhere — read the web_evidence field for open-web coverage, don't read empty corpus corroboration as 'uncorroborated'.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "validate_article", "arguments": {}}}'

deep_dive

Research-style multi-angle dive on a question. Walks the buyer's assistant through grounding via WORLD STATE, primary article retrieval, entity wikis, full-body fetch, and Aunoo's Deep response format (400-800 words, 3+ sources).

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "deep_dive", "arguments": {"query": "What's the latest on AI regulation?"}}}'

consensus_analysis

Identify 3-4 consensus categories where coverage converges + where it diverges. Cross-source synthesis for news consumers.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "consensus_analysis", "arguments": {}}}'

future_horizons

Standardized, VISUAL Three-Horizons foresight for a topic: pulls get_horizons + the canonical S-curve, then renders Aunoo's future-horizons component as an artifact (HTML/text fallback). Use this to SHOW horizons, not just describe them.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "future_horizons", "arguments": {}}}'

consensus_outliers

Standardized, VISUAL Consensus & Outliers for a topic: pulls get_consensus, then renders Aunoo's consensus component (sentiment mix + agreement + minority outliers) as an artifact (HTML/text fallback). Use this to SHOW consensus, not just describe it.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "consensus_outliers", "arguments": {}}}'

foresight_report

Build ONE cohesive, on-brand report artifact for a topic — orchestrates the full foresight set: the Three-Horizons chart (Aunoo SVG) + scenarios, the computed volume / sentiment / polarization signals, and Consensus & Outliers, assembled into a single React/HTML report. Use this for a COMPLETE report; the per-surface recipes (future_horizons, consensus_outliers) each cover one section.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "foresight_report", "arguments": {}}}'

newsletter

Multi-step playbook for drafting a topic-scoped newsletter in Aunoo's house voice. Plans 5-8 sections, deep-dives one, assembles markdown with inline citations. Distinct from the 'newsletter' SAMPLING preset on get_topic_articles (which only selects articles), and from start_newsletter_job (server-side generator that produces the final draft). Use this recipe when YOU (the assistant) are doing the drafting; use start_newsletter_job when you want Aunoo's generator to produce the draft.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "newsletter", "arguments": {}}}'

correspondent_brief

Short-form briefing (200-300 words) in the voice of a named Aunoo correspondent persona. Pulls fresh articles matching the persona's signal, evaluates each against the persona's mandate, surfaces top matches with excerpts.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "correspondent_brief", "arguments": {}}}'

forecast_brief

Forward-looking outlook for a topic built from Aunoo's three COMPUTED forecast signals: volume trajectory (lifecycle phase + projected peak), sentiment inflection (is the mood turning, with a projected flip date), and narrative polarization (are left and right diverging). Grounds each in current coverage and synthesises a short observational brief. Distinct from get_horizons (scenario narratives) and consensus_analysis (current convergence) — this reads the trend lines and says where they point.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "forecast_brief", "arguments": {}}}'

investigation

Rigorous, evidence-first investigation + fact-check of a claim, article, or URL. Grounds via WORLD STATE, gathers primary sources, then runs Aunoo's claims validator — including the correct async path (validate_article inline; on timeout, start_validation_job + poll get_validation_job) — weighs provenance (independent corroboration vs syndication), and delivers a verdict with confidence and caveats. Distinct from deep_dive (synthesis, no fact-check) — this one adjudicates.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "investigation", "arguments": {"subject": "…"}}}'

story_reach

Trace how a story spread on the covered social platforms and how it was received, and present it as a structured reach card (Spread → Top amplifiers → Reception). Wraps social_story_reach (quick) or start_social_reach_job + get_social_reach_job (deep: follower-weighted reach, fresh-account surge, coordination cohorts, reply reception). Needs the Social module on this connection. Observational — reach/reception are evidence, not a per-account verdict.

curl -s https://agentic.aunoo.ai/mcp \
  -H 'Authorization: Bearer aunoo_…' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "prompts/get", "params": {"name": "story_reach", "arguments": {"subject": "…"}}}'