Panorama

Panorama Search or Panorama API?

They share the Panorama name, but have different workflows and coverage. Search’s saved results, relevance decisions and optional complaint labels are dashboard features. Calling a Panorama API endpoint does not reproduce the Search interface or expose its saved analysis. See Search’s API and MCP scope.

Make your first Panorama API request

A composite operation combines related lookups behind one request. Some span multiple platforms; others combine several reads on the same platform. Universal URL lookup is a dispatcher to a supported detail endpoint rather than a multi-source analysis.

This example requests a developer brief for Deno: a GitHub repository dossier, activity over the last seven days and matching Hacker News discussions. Store your key in the server environment as MONOCRAWL_API_KEY. Check the current endpoint price and availability before running it.

curl --get 'https://www.monocrawl.com/v1/panorama/devtool-brief' \
  --header "x-api-key: $MONOCRAWL_API_KEY" \
  --data-urlencode 'query=deno' \
  --data-urlencode 'threads=5'

A name query searches for a repository and selects a candidate. Inspect data.repo_resolution to confirm the intended project. You can supply a GitHub repository URL for explicit resolution; the same query is also used for the Hacker News search. A matching repository does not prove that every discussion concerns that project.

threads=5 limits the displayed ranked threads. The brief’s Hacker News totals are computed over the returned search sample, which can include more stories than the displayed five.

Choose an operation by its result

The complete reference includes batch profiles, comments, URL lookup, review integrity and other workflows. Platform support is specific to each operation. Batch profiles accepts explicit platform/handle pairs, including supported major social profiles. A YouTube pair can use a handle or an exact 24-character channel ID beginning with UC; preserve its case. Inspect each item’s outcome because sources differ in eligibility, identifiers and available fields.

Read the result and its coverage

The API uses Monocrawl’s normal response envelope, including success, data, request_id and billing fields. Each Panorama endpoint defines its own data object. Do not assume that all operations return the same fields.

Other workflows may use legs, per-item failures or engine outcomes instead of sources. A successful envelope can contain partial data. Keep null values and warnings; do not replace missing results with zero or describe a returned sample as complete platform coverage.

For AI answers, shared citations indicate source overlap, not verified factual agreement. For optional themes or topics, distinguish model interpretations from original source records and calculated metrics.

Measure social mentions and share of voice

brand-mentions and share-of-voice accept an explicit platforms list. The default pool includes Reddit, Hacker News, X, TikTok, Instagram and YouTube, plus configured Bluesky. Sources use their supported keyword or hashtag search operations; source eligibility, spending limits and deadlines determine which can answer.

Set date_mode=strict for metrics limited to known publication dates inside the lookback window. The default ranked mode retains undated and older matches with date labels. per_source_limit is a bounded page sample, not a platform-wide mention count. Preserve each record’s native ID, URL, metrics, timestamp and provenance.

Share of voice compares only sources that answered for every brand. share_of_mentions and share_of_engagement are fractions from 0 to 1; null means no usable denominator. Views remain separate from interactions. Each brand’s evidence and coverage explain its contribution to the calculation.

For an ambiguous name, supply context describing the intended subject, such as Mercury, the business banking service; not the planet. Brand mentions and share of voice use a short descriptive hint to improve retrieval. With three or more selected sources, a fixed subset searches the original name to retain broader evidence; the other sources use the contextual query. One or two sources use the contextual query. Each source still receives one bounded search request. Read query_plan.lanes and each source’s coverage for the exact queries. Extra terms can narrow what a source returns; neither query mode guarantees complete recall or verified identity.

Results prioritise supported useful subject matches when classified, then other subject matches, contextual candidates, keyword candidates, uncertain matches and unrelated results. Within candidate groups, authored questions and experience text are prioritised and repeated long text is deferred. Platforms take turns within each priority group; source_rank preserves each record’s original page position. A large like count on one network does not outrank every result from another. Inspect ranking for the ordering policy and source counts. Missing engagement remains unknown.

Optional relevance=jev classifies at most 20 candidates per brand when funding and capacity permit. The workflow protects part of its existing spending limit for classification and requested reading context before collecting sources. Classification runs before comment selection. This can reduce source coverage within the same price; inspect completed source and analysis counts. relevance_analysis.status distinguishes complete, partial, unavailable and no-candidate outcomes. remaining_unclassified includes both unselected records and failed classifications.

matched_items contains completed Jev subject matches; candidate_items contains lexical candidates still needing review. match_summary reports both pools, unresolved records and unrelated records. A no_supported_match status means this returned sample established no supported match, not that the subject has no mentions anywhere. Every original record remains in items (or each share-of-voice brand’s evidence). Sample counts and share denominators are not silently replaced with model-filtered totals. Read identity, discussion and usefulness labels independently; model support is not a calibrated probability.

For brand mentions, enrich=comments or transcripts adds reading context to up to three supported posts chosen across sources, with a cap of 10 comments or 8,000 transcript characters each. These records do not increase the mention count. Optional classification examines the returned parent title and text; attached comments and transcripts remain separate reading evidence. An empty comment sample, disabled comments and a failed source request are different outcomes; inspect the source receipt.

Add historical web evidence

Use brand-mentions?include_history=true to request indexed web summary, trends and example citations alongside the social sample. The history field reports its own completeness. mention-surge?history_mode=indexed compares non-overlapping completed historical windows; it excludes today and does not silently replace missing days with zero.

The standalone indexed summary, indexed search and indexed phrase trends expose this measurement separately. Index history starts on 31 October 2022 where source records exist. Search examples use publication dates, while trend buckets use index reporting dates. An observed/fetched timestamp does not establish when an article was published.

Indexed citations and native social posts have different coverage and denominators. negative_share divides negative reported assignments by positive + negative + neutral assignments. These source categories can overlap, so this is not a verified fraction of unique citations. A negative-share trend uses those fractions, not negative counts. Returned provenance describes the source classification and its unknown confidence.

Summary and trend filters describe the requested query; precise source enforcement and a consistent historical source roster remain unverified. Inspect window.verified and filters.verified. Search records can be filtered locally to known publication dates and requested fields, but source totals are reported before that local filtering.

Interpret creator and review samples

Creator vetting accepts posts_limit, comment_posts and comments_per_post. Read post_evidence and sample to see the original counters and requested versus completed collection. Zero interactions remain zero; missing counters remain unknown. Summed followers across profiles are not unique audience reach, and a small commenter sample is not proof of a fake audience.

YouTube creator research can fetch counters for up to 10 returned video IDs in one bounded detail request when the initial post page omits them. metrics_hydration reports requested and returned IDs and any remaining gaps. Each post’s count_sources identifies the fields and lookup behind its numbers. video-intel reads both published top-level and nested counters; interactions exclude views, and an engagement rate requires observed interactions and a positive view count. Handle audits use reported followers or subscribers and disclose follower_source_field.

Review comparisons accept explicit review_sources mappings across supported app, product or company sources. Only comparable entity types, marketplaces and variants are tested. Caller mappings remain labelled as caller supplied. Statistical comparisons need at least 20 rated reviews per source and abstain when identities or samples are inadequate. A rating difference or unusual distribution does not establish fraudulent reviews.

Source ratings_count and written reviews_count are separate population measures. Review windows and daily statistics use review_date; date_basis distinguishes publication from a last update. An update date is never presented as a publication timestamp.

A review without a variant label remains usable evidence from the requested listing, marked variant_status=unknown. It is not counted as a verified match for the requested variant, and variant comparisons abstain when those labels are missing. Explicitly different variants and records excluded by a strict date window are retained separately in excluded_reviews with reasons. Without a strict window, undated reviews remain in the sample; they never receive invented dates or enter daily statistics.

Audit every AI visibility probe

Currently unavailable: no eligible answer engine is offered for AI answers or AI visibility. Free plans and response contracts remain available; execution refuses these requests without running probes.

ai-visibility accepts your prompts, brand, competitors and engine subset. Explicit runs overrides preset repetitions; the preset still caps prompt count. Read effective_settings for omitted prompts and planned calls before interpreting results. A default quick batch permits up to three prompts with two repetitions each per engine.

Every probes entry includes the answer, citations, model, prompt/run IDs, timestamps, SHA-256 and recognized brand passages. Normal answers are retained in full; safety bounds cap each at 64,000 characters and 100 citations with explicit truncation flags. Appearance rates use 0–100 percentages over completed nonempty answers. Failed and skipped probes are excluded and reported separately; a completely failed batch returns an error. Literal brand matches do not imply a recommendation or verified identity.

{
  "name": "get_endpoint",
  "arguments": {
    "id": "panorama/ai-visibility",
    "params": {
      "brand": "My Brand",
      "prompts": "[\"Which project tools are useful?\"]",
      "runs": "1",
      "engines": "perplexity,grok"
    }
  }
}

This returns an execution plan and the current endpoint price without probing any engine or reserving credits. The plan is not an availability guarantee. Actual execution still checks eligible supply, configured cost ceilings, account funds and limits. Use call_endpoint.max_credits to enforce your spending ceiling.

Prices, partial results and retries

Read each operation’s current price in the endpoint reference or with get_endpoint. The developer brief reports attempted and successful component counts and its billing formula; other operations have their own accounting. Do not apply one workflow’s partial-result adjustment to every Panorama endpoint.

If credits_used is null and error.details.billing_status is pending_reconciliation, the final charge is unresolved. Preserve the request ID and idempotency key and reconcile it before starting a replacement paid request. A source failure is not by itself proof of a refund. See credits and reconciliation.

Bound retries, retain the exact arguments for each logical request, and inspect the error before retrying. Reuse its idempotency key where supported. A new subject, page or refreshed request is a new operation. Use the production request wrapper for timeouts, backoff and receipt handling.

Use Panorama API through MCP

After connecting Monocrawl, read the guide and endpoint contract, check the available balance, then retrieve with an explicit spending ceiling. These are separate tool calls in order; discovery is free, retrieval can spend credits.

[
  {
    "name": "get_docs",
    "arguments": {
      "topic": "panorama"
    }
  },
  {
    "name": "get_endpoint",
    "arguments": {
      "id": "panorama/devtool-brief"
    }
  },
  {
    "name": "call_endpoint",
    "arguments": {
      "platform": "panorama",
      "endpoint": "devtool-brief",
      "params": {
        "query": "deno",
        "threads": "5"
      },
      "max_credits": 5,
      "idempotency_key": "panorama-deno-brief-001"
    }
  }
]

The example allows at most 5 credits for this request. If the current quote exceeds that limit, stop and review it; do not automatically increase the budget. Use a unique idempotency key for a new logical request and retain it for retries of that same request.

When storage succeeds, large MCP results return a compact preview with data.stored_result.id. Call get_result with that ID, then follow its cursor to retrieve the complete original response for 24 hours. These reads require the same account and cost zero credits. Concatenate each data.text fragment in order and parse the JSON after the final cursor is null. Storage allows up to 8 MiB per result and 32 MiB of retained results per account. If storage is unavailable or a limit is reached, the full response is returned with a warning; save that original response yourself. Never repeat the paid workflow merely to read another evidence chunk.

Use get_docs with topic panorama-search for the interactive workspace guide. It reads documentation, not private saved searches. Both guides also have public Markdown versions: Panorama and Panorama Search.