monocrawl.com

Monocrawl is a social media scraping API that returns posts, profiles, comments, products, and reviews from 70+ platforms through one key and one unified schema. You can start with 150 free credits, then move to a monthly plan with optional subscriber top-ups. Billing is results-based: failed calls cost $0, and cached repeats cost zero credits. This guide covers what the pricing model means in practice and how to get from signup to your first successful call.

What Monocrawl actually provides

The product is one API in front of many platforms. Instead of writing and maintaining a separate integration for TikTok, Instagram, YouTube, X, Reddit, and the rest, you send requests to a single endpoint and receive a consistent response envelope.

Key facts from the site:

• 70+ platforms covered, including TikTok, Reddit, YouTube, Instagram, X, LinkedIn, Google Search, Amazon, eBay, Walmart, Etsy, Airbnb, Booking.com, TripAdvisor, GitHub, Spotify, App Store, Google Play, and Trustpilot.
• 650+ endpoints across REST and MCP.
• 2.1s median response in the vendor's own benchmark.
• One schema with shared fields where platforms match, while platform-specific data is kept intact.

That last point matters for pricing decisions: you are not paying per platform integration, you are paying per unit of data returned.

How the pricing model works

Monocrawl does not publish a flat per-seat or per-month number in the material available here. What it does state clearly is the shape of the billing:

Two consequences follow from this:

1. Your cost scales with successful data retrieval, not with attempts. If you are building a monitor that polls frequently, the cache behavior is the single biggest lever on your bill.
2. You cannot estimate a monthly figure from this page alone. The per-credit rate and plan tiers live on the pricing page, so check monocrawl.com/pricing before you commit.

The site also links a price-tracking use case, which is worth reading if your workload is product/price monitoring rather than social listening — the request patterns differ.

Getting started: the actual steps

1. Create an account and claim the free credits

Sign up on monocrawl.com. The 150 free credits are the starting balance, so you can validate the API against your real use case before any payment.

2. Choose your connection method

Monocrawl supports two paths:

• REST — for standard application code, scripts, and backend jobs.
• MCP — for AI agents. The site describes this as "connect once, your agent finds the endpoints and pulls the public-web data it needs."

If you are wiring an agent, MCP removes the step of hand-picking endpoints. If you are building a conventional pipeline, REST is the direct route.

3. Authenticate with your API key

All access goes through one key. The site's framing is "one key and one unified schema," so you are not juggling per-platform credentials.

4. Make a first call and read the response envelope

The documented example is a profile lookup. A TikTok-style response returns platform-native fields:

{
  "unique_id": "northwind",
  "verified": true,
  "follower_count": 84200,
  "video_count": 312
}

An Instagram-style response returns different native fields (edge_followed_by, posts_count, and so on). Through Monocrawl, both come back normalized:

handle: northwind
verified: true
followers: 84,200
platform: tiktok
credits_used: 1
cached: false

So your integration code can be written once:

const { handle, verified, followers } = response.data;

Expected result: a normalized object plus metadata telling you which platform it came from, how many credits it consumed, and whether it was served from cache. That cached flag is how you verify the zero-credit repeat behavior on your own account.

5. Verify before you scale

Run the same request twice. If the second response shows cached: true and credits_used: 0, the cache is working as described and your polling design can lean on it. Then deliberately trigger a bad request to confirm failed calls don't deduct credits.

6. Move to a monthly plan when the free credits run out

Once 150 credits are exhausted, continue on a monthly plan. If you hit the ceiling mid-cycle, subscriber top-ups are available rather than forcing an immediate tier change.

Choosing between Monocrawl and per-platform scraping

Use the same dimensions to decide:

Pick Monocrawl if you need several platforms, want normalized output for agents or dashboards, and would rather pay per result than maintain scrapers. Pick separate scraping if you need exactly one platform with highly custom extraction, or you have strict data-residency requirements that rule out a third-party API.

Common sticking points

• Assuming the free tier is unlimited. It is 150 credits, not a permanent free plan.
• Not checking the pricing page for rates. The billing model is documented; the per-credit price is not in this material.
• Ignoring the cache flag. If you don't check cached, you can't tell whether your polling strategy is actually saving credits.
• Treating normalized fields as complete. Platform-specific data is kept as returned, so some fields still require per-platform handling.
• Skipping the MCP path for agent workloads. If an AI agent is the consumer, MCP avoids hardcoding endpoint choices.

OpenAPI-generated API documentation is reference documentation that is produced automatically from an OpenAPI description file rather than written by hand. You write (or generate) a machine-readable specification of your API — endpoints, parameters, request bodies, responses, schemas, and auth — and a documentation tool reads that file and renders a browsable, often interactive reference site. The spec becomes the single source of truth; the docs become a build artifact.

This differs from manually written docs in one fundamental way: with hand-written docs, the prose is the source of truth and the API is described separately. With spec-driven docs, the API description is the source, and every page, table, and code sample is derived from it.

How the workflow actually runs

A typical spec-driven documentation pipeline has five stages:

1. Author or generate the spec. You either write an OpenAPI document by hand (YAML or JSON), or generate it from code annotations, framework metadata, or a design-first editor. Design-first means the spec is written before implementation; code-first means it is extracted from existing code.
2. Validate and lint. The spec is checked against the OpenAPI schema and against style rules — consistent naming, required descriptions, no undocumented 4xx responses, no orphaned schemas.
3. Bundle and transform. Multi-file specs are combined, $ref pointers are resolved, and the document is optionally split into per-tag or per-version outputs.
4. Render. A documentation tool converts the spec into HTML: an endpoint list, a sidebar of operations, parameter tables, response schemas, and a "try it" console.
5. Publish and version. The rendered site is deployed, and each API version gets its own snapshot so consumers can read docs matching the version they call.

Steps 2 through 5 are usually automated in CI. If the spec fails validation, the docs build fails — which is the point.

Spec-driven vs. hand-written documentation

The practical conclusion most teams reach: generate the reference, write the guides. Reference material is repetitive and mechanical, which is exactly what generation is good at. Conceptual explanations, migration notes, and tutorials carry judgment that a spec cannot express.

What you get out of the box

Generated reference pages commonly include:

• An operation list grouped by tag or path, with HTTP method and path.
• Parameter tables showing name, location (path, query, header, cookie), type, required flag, and description.
• Request and response schemas rendered as expandable trees, including nested objects and arrays.
• Authentication details pulled from the securitySchemes section.
• Interactive request consoles that let a reader send a real call from the browser.
• Generated code samples in several languages, derived from the same schemas.
• Multiple output formats, such as a static site, a single HTML file, or a mock server.

Because all of these come from one document, changing a field name in the spec updates the parameter table, the schema tree, and every code sample at once.

Where spec-driven documentation breaks down

Generation is not free. The trade-offs are real:

Spec quality becomes documentation quality. A field with no description produces a table row with an empty cell. A vague summary produces a vague heading. Tools can enforce presence of descriptions via linting, but they cannot enforce that the description is useful.

Customization has limits. If you need a page that does not map to an OpenAPI concept — a conceptual overview, a pricing explanation, a comparison of two endpoints — you write it outside the generator and link to it.

Not everything is expressible. Webhooks, streaming responses, long-polling behavior, and complex multi-step flows are awkward or impossible to describe fully in OpenAPI. Those need prose.

The spec can go stale. If the spec is maintained separately from the implementation, it drifts just like hand-written docs. The mitigation is to generate the spec from code, or to test the implementation against the spec in CI.

Interactive consoles need care. A "try it" button that hits a production API with real credentials is a security and rate-limit problem. Point it at a sandbox, or disable it.

Deciding whether to adopt it

Adopt spec-driven reference documentation if most of these are true:

• Your API has more than a handful of endpoints, or changes frequently.
• You ship client SDKs or code samples in more than one language.
• Multiple teams consume the API and need a consistent, always-current reference.
• You already have, or are willing to maintain, an OpenAPI description.

Stay with hand-written docs, or a hybrid, if:

• Your API is small and stable, and the reference fits on one page.
• Your documentation is mostly conceptual and contains little endpoint-level detail.
• You cannot commit to keeping the spec in sync with the implementation.

A reasonable middle path: generate the reference from the spec, and hand-write the getting-started guide, authentication walkthrough, and error-handling page. Link the two directions so readers can move from concept to endpoint and back.

A minimal starting checklist

1. Produce one valid OpenAPI document for a single API version.
2. Add a linter with rules for descriptions, operation IDs, and error responses.
3. Wire the docs build into CI so a failing spec fails the build.
4. Render the reference and review it as a reader, not as the author.
5. Write the two or three conceptual pages the generator cannot produce.
6. Version the published docs alongside the API version.

The core idea is simple: describe the API once, in a format both machines and humans can read, and let the reference documentation fall out of that description. Everything else — tooling, hosting, interactivity — is a detail on top of that decision.

Monocrawl replaces per-platform scraping with one API and one unified schema across 70+ platforms, so you write the integration once instead of maintaining a separate client for TikTok, Instagram, YouTube, X, Reddit, and the rest. It fits teams that need public social, review, and marketplace data at breadth and want to spend engineering time on their product rather than on keeping scrapers alive. If you only ever need one platform and a handful of fields, a single direct integration can still be the simpler choice.

The core difference: one integration vs. one per platform

Scraping each platform separately means owning a distinct set of problems for every source: different auth, different rate limits, different response shapes, and different breakage schedules. Monocrawl's pitch is that you connect once and get a consistent interface.

The site frames this as "Less integration work. More building." — the value is concentrated in the integration and maintenance layer, not in any single endpoint.

What the unified schema actually looks like

Monocrawl's example shows the same profile request returning a normalized shape while preserving the original platform fields.

Without Monocrawl, the same concept has different names and nesting per platform:

TikTok /profile
{ "unique_id": "northwind", "verified": true,
  "follower_count": 84200, "video_count": 312 }

Instagram /profile
{ "username": "northwind", "is_verified": true,
  "edge_followed_by": { "count": 1260000 }, "posts_count": 1480 }

With Monocrawl, one request returns shared fields plus the platform-specific data intact:

Monocrawl /profile
handle      northwind
verified    true
followers   84,200
video_count 312        ← platform-specific, kept as returned
platform    tiktok
credits_used 1
cached      false

Your consuming code becomes a single destructure:

const { handle, verified, followers } = response.data;

The practical gain is that adding a new platform doesn't mean adding a new parser — but you should still expect to handle platform-specific fields when you need detail beyond the shared ones.

Cost model: you pay for results, not attempts

Two billing behaviors stand out in the source material and matter when comparing against self-managed scraping:

• $0 for failed calls — you pay for results, so a failed request doesn't consume credits.
• Zero credits for cached repeats — requesting the same data again from cache is free.

Self-hosted scraping has the opposite profile: failed requests still cost you proxy bandwidth, compute, and engineering time. If your workload repeats the same lookups often, the cached-repeat rule is the more consequential of the two. Note that the site lists monthly plans, optional subscriber top-ups, and 150 free credits to start; check the pricing page for current numbers rather than assuming a tier.

Where each approach fits

Choose Monocrawl when:

• You need more than a couple of platforms, or expect to add them.
• You want one schema and one key rather than N integrations.
• Your workload repeats queries and benefits from cached repeats.
• You're feeding AI agents — the site offers MCP so an agent can discover endpoints and pull data itself.

Choose per-platform scraping when:

• You need exactly one platform and a narrow field set.
• You require data or endpoints outside the 70+ covered platforms.
• You want full control over request timing and raw responses with no intermediary.

What to verify before committing

The comparison hinges on coverage and schema fit, so test against your actual fields rather than the demo values. The site's example numbers are illustrative ("Example values · no live requests"), and it reports a 2.1s median response "in our benchmark" — treat that as a vendor figure to validate on your own endpoints. Start with the 150 free credits, run a representative sample across the platforms you care about, and confirm that the platform-specific fields you depend on survive the unified envelope before you migrate off any existing integration.

Monocrawl is a social media scraping API that returns public data from 70+ platforms through one key and one unified schema. It is built for developers and AI agents that need profiles, posts, comments, reviews, or price data without writing a separate integration for every platform. You can start with 150 free credits, and failed calls are not charged.

What Monocrawl actually provides

The core product is a single REST and MCP API. Instead of learning TikTok's field names, then Instagram's, then Reddit's, you call one endpoint and get a consistent envelope back.

• One API, one schema — shared fields where platforms match (for example handle, verified, followers), with platform-specific data kept intact rather than flattened away.
• 70+ platforms — including TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Reddit, Google Search, Amazon, eBay, Walmart, Etsy, Airbnb, Booking.com, TripAdvisor, GitHub, Spotify, App Store, Google Play, and Trustpilot.
• 650+ endpoints across REST and MCP.
• Data types — profiles, posts, comments, reviews, and prices.
• MCP support — connect an agent once and it can discover endpoints and pull the public-web data it needs.

The site reports a 2.1s median response time in its own benchmark, zero credits charged for failed calls, and zero credits for cached repeats.

Who it is for

Monocrawl targets two groups:

1. Developers building integrations, dashboards, or data pipelines who want to avoid maintaining dozens of scrapers.
2. AI agents and agent builders who need a stable, discoverable data source rather than brittle per-site scraping.

If you only need one platform and already have a working scraper, the value is lower. The benefit scales with how many platforms and how much schema-normalization work you would otherwise do yourself.

Common use cases

The site lists several concrete applications:

A price-tracking use case is also documented, which fits the price and product data coverage.

How the unified schema works in practice

The clearest way to understand the product is the before/after the site shows. Without Monocrawl, the same profile request returns different shapes per platform:

TikTok /profile
{ "unique_id": "northwind", "verified": true,
  "follower_count": 84200, "video_count": 312 }

Instagram /profile
{ "username": "northwind", "is_verified": true,
  "edge_followed_by": { "count": 1260000 }, "posts_count": 1480 }

With Monocrawl, one call returns normalized fields plus the original platform data:

Monocrawl /profile
handle      northwind
verified    true
followers   84,200
video_count 312          ← platform-specific, kept as returned
platform    tiktok
credits_used 1
cached      false

So your integration code becomes a single destructure — const { handle, verified, followers } = response.data; — instead of a per-platform mapping layer.

Pricing and access

The site references monthly plans, optional subscriber top-ups, and credit packs on its pricing page. It states 150 free credits to start, $0 for failed calls, and zero credits for cached repeats. Specific plan prices are not stated in the available material, so check the pricing page for current numbers before committing.

What to check before adopting

• Platform coverage for your specific target — confirm your platform is in the 70+ list, since the exact set matters more than the count.
• Endpoint availability — 650+ endpoints sounds broad, but verify the specific data type you need (for example, comments on a given platform) exists.
• Credit consumption per call — the example shows credits_used: 1 for a profile call, but cost likely varies by endpoint. Confirm this against your expected volume.
• MCP fit — if you are building an agent, test the MCP connection path first, since that is the primary agent workflow.

If your work involves pulling public data from many platforms into one pipeline, Monocrawl is designed to remove the per-platform integration work. If you need a single source or already have reliable scrapers, the case is weaker.

Monocrawl is a social data API that covers 70+ platforms through one key and one unified schema, and its documented use cases are brand monitoring, competitor tracking, customer intelligence, and feeding public-web data to AI agents. It fits teams that need public posts, profiles, comments, reviews, or listings from many sources without building and maintaining a separate integration per platform. It is not a fit if you only need one platform, or if your data needs are private, logged-in, or non-public.

Brand monitoring and social listening

Monocrawl's brand monitoring use case collects every mention of your brand across social platforms, reviews, and forums, then delivers alerts to your team. The site lists Reddit, X, Trustpilot, and Rightmove among the sources, and states that every alert links back to its source.

A concrete example from the page: a watched listing on Rightmove triggers a "Price −4.8%" alert. That shows the monitor is not limited to text mentions — it can also watch listings and price changes.

Choose this if you want mentions routed to a team rather than pulled on demand. If you only need a one-time export of posts, the raw API endpoints are the simpler path.

Competitor tracking

The competitor tracking use case follows competitors' posts, prices, and engagement, and pulls their moves into your own comparisons. The site points to its price and product tracking page for this.

This is the use case to pick when your question is "what are they doing and how is it performing" rather than "what are people saying about us." It shares the same platform coverage as brand monitoring, so the difference is mainly what you watch (competitor accounts and listings) and what you compare against.

Customer intelligence

Customer intelligence gathers the reviews behind complaints, requests, and buying signals so you can decide on evidence. The site frames this as finding the reviews that explain why customers complain or what they are asking for.

Use this when your input is review and feedback text and your output is a decision — product changes, support priorities, or demand signals. It overlaps with brand monitoring in sources but differs in intent: monitoring watches for mentions, customer intelligence mines reviews for patterns.

Agent-ready data via MCP

Monocrawl exposes an MCP connection so an AI agent can connect once, discover the endpoints it needs, and pull public-web data itself. The site describes this as "Your agent. Our data. In your own tools."

This use case matters if you are building an agent rather than a dashboard. Instead of hardcoding each platform call, the agent finds endpoints through MCP. If your workflow is a fixed pipeline with known endpoints, the REST API is the more direct route — the site reports 650+ endpoints across REST and MCP.

What the API returns, by use case

Across all four, the returned data uses one envelope: shared fields where platforms match, platform-specific fields kept as returned. The site's example shows a unified handle, verified, and followers, with video_count and platform preserved alongside.

Cost and access conditions to check before committing

The site states 150 free credits to start, $0 for failed calls, and zero credits for cached repeats. It also reports a 2.1s median response in its own benchmark. Monthly plans and optional subscriber top-ups exist, so pricing is subscription-based rather than purely pay-per-call — check the pricing page for current credit rates before sizing a project.

Two conditions to verify against your own requirements: coverage is public data only, and the platform list (TikTok, Instagram, YouTube, X, Reddit, LinkedIn, Google Search, Amazon, eBay, Walmart, Etsy, Airbnb, Booking.com, TripAdvisor, GitHub, Spotify, App Store, Google Play, Trustpilot, and others) should be checked against the specific platforms you need, since "70+" does not guarantee the one you care about is included.