Launching Espresso API & MCP — Curated Business Intelligence for AI Agents & Dashboards

Soumit S Rah — Tue, 30 Jun 2026 12:01:00 +0000

It is my pleasure to introduce Project Cafecito’s next product — Espresso API & MCP.

The API is part of the larger Espresso product suite, which also includes Espresso Publications: Editorials, X, Threads — our curated digests and deeper human-readable analysis built on the same high-signal intelligence layer. Think of it as concentrated shots of structured intelligence: event digests, synthesized signals, tags, and relationships — served fresh for dashboards, monitoring workflows, and AI agents.

Like everything we ship these days, it comes with a full MCP server so your agents can sip directly without burning tokens on JSON parsing.

How Events and Signals Are Produced

Events are generated from a wide range of public data sources — product price changes, stock market movements, SEC filings, earnings reports, product announcements, corporate partnerships, and more.

Signals are the abstraction layer on top. They combine multiple similar events across an extended timeline to identify event chains, causal relationships, and commonality between various entities.

What Espresso actually gives you

Clean market event and signal records with timestamps, and domain-specific fields like actions, events, impact_level, marco_context, cross_domain_impacts etc.
Semantic search with adjustable accuracy.
Tag filtering.
Cross event relationships.
Token-optimized MCP-friendly response.

Quick Start + Python Examples

import requests
import os

API_KEY = os.environ["CAFECITO_API_KEY"]
BASE_URL = "https://api.cafecito.tech"

headers = {"Authorization": f"Bearer {API_KEY}"}

Semantic search for signals (plain text for MCP/agents)

resp = requests.get(
    f"{BASE_URL}/espresso/signals",
    headers=headers,
    params={
        "q": "market volatility and interest rate impact",
        "acc": 0.75,
        "limit": 5,
        "response_type": "text"
    },
    timeout=30
)
resp.raise_for_status()
print(resp.text)

Filter signals by tags

resp = requests.get(
    f"{BASE_URL}/espresso/signals",
    headers=headers,
    params={
        "tags": ["usa", "policy_reform"],
        "from": "2026-05-01",
        "limit": 10
    },
    timeout=30
)
resp.raise_for_status()
print(resp.json())
Combine semantic + tags

resp = requests.get(
    f"{BASE_URL}/espresso/signals",
    headers=headers,
    params={
        "q": "regulatory changes affecting tech",
        "tags": ["regulation","openai"],
        "acc": 0.8,
        "limit": 8
    }
)
resp.raise_for_status()
print(resp.json())

Sample Response Body (Events)

[
     {
        "actions": [
            "2026-06-28 Firefighters died and injuries occurred at Wyoming-Utah border.",
            "2026-06-28 Large wildfire destroyed parts of ski resorts."
        ],
        "briefing": "On June 28, 2026, three U.S. firefighters died while battling rapidly spreading wildfires near the Colorado-Utah border; approximately 100 sq km burned. Temperatures reached 34°C with strong winds, prompting mass evacuations. The Snyder Fire merged with others, causing significant damage to infrastructure like ski resorts. Causes include severe drought and human factors. This incident reflects escalating regional wildfire risks driven by climate change.",
        "companies": [
            "us_federal",
            "us_state"
        ],
        "cross_domain_impacts": [
            "public_safety: Increased risk of civilian casualties.",
            "tourism: Disruption of winter sports facilities.",
            "environmental: Habitat loss in mountainous areas."
        ],
        "event_type": "wildfire_outbreak",
        "future_outlook": "Continued extreme fire seasons expected without mitigation efforts.",
        "id": "091726f8-421a-566d-9db8-339625f2ed9e",
        "impact_level": "high",
        "macro_context": "western_us_climate_crisis",
        "regions": [
            "colorado",
            "utah"
        ],
        "reported": "2026-06-28T22:49:07Z",
        "tags": [
            "wildfire",
            "climate_change",
            "us",
            "emergency_response"
        ]
    },    
    {
        "actions": [
            "2026-06-28 Governments of United States and Iran agree to cease fire",
            "2026-06-30 Bilateral talks scheduled in Oruz"
        ],
        "briefing": "On June 28, 2026, U.S.-Iran governments reached an emergency agreement halting military clashes amid regional tensions. The pause prevents broader war while resuming negotiations by June 30 focused on Ormuz Strait security. This diplomatic step averts immediate market shocks but depends on both sides lowering aggressive rhetoric for tangible outcomes.",
        "cross_domain_impacts": [
            "energy_markets: Reduced oil volatility expected",
            "security_framework: Gulf stability improved temporarily"
        ],
        "event_type": "diplomatic_ceasefire",
        "future_outlook": "Talks may stabilize but risk re-escalation without sustained engagement.",
        "id": "6b4562d2-0a5c-540f-a39e-f1d5cad4ee5f",
        "impact_level": "high",
        "macro_context": "middle_east_conflict_resolution",
        "products": [
            "petróleo"
        ],
        "regions": [
            "gulf",
            "ormuz"
        ],
        "reported": "2026-06-28T21:52:00Z",
        "tags": [
            "diplomacy",
            "gulf",
            "iran",
            "us",
            "fuel"
        ]
    }
]

Full documentation is available in the Developer Portal.

Who is this for?

If you’re building AI agents, executive dashboards, automated briefings, or RAG pipelines that need real business/market/policy context — Espresso is the noise-free high-signal layer you’ve been missing.

Current status

v0.1 is live and open. It’s early — we’re still pruning bugs and adding more signals — but it’s already useful enough that I’m using it internally.

Try Espresso here | Get your API key & full docs

So… what now?

Fire it up and start sipping. It's free for a while dude!

Drop a comment below and tell me:

What signals or use cases would make this 10x more useful?
Any brutal feedback?
Or just share what you’re building. I read every single reply (yes, even the mean ones).

Happy building. May your signals stay strong and your agents well-informed. ☕

PS: This is part of the growing Cafecito stack (Beans + Espresso + more brewing). If you liked the no-BS vibe from the Beans launch, you’ll enjoy what’s coming next.

See you in the comments.

Espresso vs Beans — When to Use Which

Beans API is your bare metal news and blogs API. You query it when you want raw content being published by different publishers, authors, and news sites. Great for RAG, full article retrieval, or when you need the original source material. Oh by the way, we just added a new route for tracking content propagation across publishers and mentions on social media. It’s good 💩, check it out.

Espresso API is the structured, curated market intelligence layer. It takes multiple information sources, cross-correlates them, and gives you the data that actually matters for business decision-making — without all the pointless crying, whining, bitching, and moaning noise.
In short:

Beans = the full firehose.
Espresso = the refined shot you actually drink.

I Built Beans — A Semantic News & Blogs API & MCP for AI Agents and RAG

Soumit S Rah — Mon, 16 Mar 2026 17:41:43 +0000

☕ After a whole lot of brewing, we are excited to finally launch Beans API & MCP from Project Cafecito.

News APIs are over-priced. Most of them lack vector search capability. Almost all of them lack new propagation timeline and related news finding. Beans is different. We built it to beat market price and designed it for developers working on AI agents.

What is Beans?

It's an aggregation + search service that pulls fresh content from 7,000+ (We are adding new sources every week) news and blog publishers every day. No crawling, no maintenance — just clean, structured data.

Key Features

Natural language semantic search — Ask in plain text using vector-powered queries (with tunable accuracy)
Intelligent filtering — By category, named entities, geography, content type, source, time range, and more
Trend scoring — Surface what's actually gaining traction based on real social shares, likes and comments
Built-in AI enrichment — Automatic extraction of entities, sentiment, topics, and regions
Story propagation mapping — Automatically connect related articles across publishers to track how narratives spread
Clean, consistent JSON output perfect for pipelines

Whether you're building AI agents, RAG systems, market intelligence tools, automation workflows, or smart dashboards — Beans gives you high-quality, enriched news data without the usual headaches.

Current Status

Free Tier v0.1 is now live and open for you to play with. It's still early — expect the brew to get stronger with your feedback.

Create a free API key and get crackin'.

We would love to get your feedback, bug reports, and feature requests. Drop a comment below:

What use case would you use this for?
What features would make it 10x more useful for your workflow?

Happy building! 🚀

DEV Community: Soumit S Rah