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alexgenovese

strata-mcp

by alexgenovese
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TypeScript
Hybrid

Strata (strata-mcp & strata CLI)

Multi-layer web search, Hindsight TEMPR ranking, deterministic token-budget packing (max_tokens), serverless PDF parsing, and JS SPA extraction for AI Agents.

License: MIT TypeScript Node MCP

Agent Self-Onboarding: Are you an AI coding agent (Claude Code, OpenCode, Cursor, Codex)? Read SKILL.md or llms.txt to mint a free 2,500-credit sandbox key and start querying in one command.

Strata 4-Layer Stratigraphic Architecture

Why Strata? (The 3 Structural Advantages Over Tavily, Exa & Firecrawl)

Teams building production AI agents today are forced to stitch together multiple expensive APIs—and pay a heavy 2x credit penalty every time an agent extracts relevant passages from search results.

Strata consolidates Search + Passage Highlights + PDF/Markdown Extraction + Site Mapping + Similarity Discovery into one deterministic engine accessible via 4 channels:

  1. MCP Server (stdio & Remote HTTP): 12 native tools for Claude Code, Cursor, OpenCode, and Windsurf.

  2. Zero-Token Bash CLI (npx strata ...): Saves ~3,000 system-prompt tokens per turn compared to loading MCP schemas at rest.

  3. 1-Line Drop-In Tavily Replacement (POST /compat/tavily/search): Accepts standard Tavily SDK payloads (api_key, query, search_depth: "advanced") while charging 1 flat credit instead of 2.

  4. REST API (POST /v1/search, /v1/extract, /v1/map, /v1/similar): Built for LangChain, Mastra, LlamaIndex, and n8n workflows.

Related MCP server: MyWebSearch MCP

1. Cost & Credit Efficiency Comparison (Verified October 2026)

Tavily and Firecrawl double your credit usage (2 credits) whenever your agent requests search with extracted passages (search_depth: "advanced"). Because Strata runs URL-aware Okapi BM25 and Hindsight TEMPR ranking on our own CPU layer, passage highlights are included for 1 flat credit.

Monthly Cost Comparison for 100,000 Agent Searches

Monthly Agent Workload

Strata (1x Flat Credit)

Tavily API ($0.005–$0.008/cr)

Exa AI ($7/1k + Deep)

Firecrawl (2 cr / search)

Your Savings with Strata

Free Tier Allowance

2,500 credits / mo

1,000 credits / mo

$10 credit (~1,428 searches)

1,000 credits (500 searches)

2.5× more free searches

25,000 Searches + Highlights

$19 / mo (Starter Plan)

$250 – $400 / mo (50k credits)

$175 / mo

$99 / mo (Standard Plan)

Save 81% – 95%

100,000 Searches + Highlights

$49 / mo (Team Plan)

$1,000 – $1,600 / mo (200k credits)

$700 / mo

$399 / mo (Growth Plan)

Save 88% – 96%

400,000 Searches + Highlights

$149 / mo (Scale Plan)

$4,000+ / mo (800k credits)

$2,800 / mo

$749 / mo (Scale Plan)

Save 80% – 96%

Sources verified 2026-10-15 (Grade A primary documentation): docs.tavily.com/documentation/api-credits, exa.ai/pricing, firecrawl.dev/pricing.md.

2. Live E2E Latency, Multilingual Precision & Token Efficiency

All benchmarks below are reproducible and versioned in benchmarks/search-quality/runs/2026-10-15-phase2-serper-tempr/live-results.json.

Live E2E Latency and Multilingual Precision@5 Comparison

Head-to-Head Benchmark Ledger

Task ID

Workload / Query

Tavily MCP

Strata v1.5 (Google SERP + TEMPR)

Key Engineering Takeaway

HTTP

Repeat Agent Query (POST /v1/search)Normalized SHA-256 L1/L2 SerpCache

1,200+ ms (billed every call)

2 ms (cache_hit: true, 0 upstream calls)

-99.8% latency when multiple agents or turns query the same topic.

s06

Hindsight TEMPR Token Budget (max_tokens: 600)Python asyncio TaskGroup exception handling

Unsupported (fixed top-K)

1,078 ms (docs.python.org at #1, 342 / 600 tokens packed)

Packs best-first into max_tokens, skipping oversized chunks to fit concise ones behind them (60–80% fewer LLM input tokens).

s01

Technical Search EN + 3-Page HighlightsPython asyncio TaskGroup exception handling documentation

1,940 ms (5/5, docs.python.org at #4)

1,926 ms (5/5, anyio #1, docs.python.org #2 with 2 BM25 chunks)

URL-aware BM25 + official docs.* boost keeps canonical documentation above forum noise.

s02

Academic Paper Search + PDF HighlightsReciprocal Rank Fusion Cormack Clarke Buettcher SIGIR 2009

1,196 ms (5/5)

1,929 ms (5/5, research.google & dl.acm.org in top 3)

Also extracts cormacksigir09-rrf.pdf (9,482 chars) in 64 ms via serverless unpdf.

s03

Multilingual Technical Docs (Italian)Google Search Central sitemap robots.txt documentazione italiano

0/5 (1,709 ms)Returned 5 K-Pop band (TXT) pages

5/5 (2,340 ms)#1 developers.google.com/.../robots/intro?hl=it

Explicit locale routing (gl: "it", hl: "it") + domain preference beats Tavily 5/5 vs 0/5.

s04

Negative Control (Invented Product)"Velrion QZ-9274" "official documentation"

5 False Positives (1,462 ms, unrelated manuals)

0 Results (777 ms)status: "empty"

Clean abstention prevents downstream LLM hallucinations on non-existent entities.

s05

Real-Time Google News (tbs: qdr:w)Model Context Protocol AI agents (category: "news")

N/A

747 ms (5/5 dated news articles)

Sub-800ms fresh news retrieval via /news + tbs time windows.

How Hindsight TEMPR Saves 60–80% of Your LLM Input Tokens

Adapted from vectorize-io/hindsight, Strata replaces naive character truncation with three deterministic scoring stages:

  1. URL-Aware Okapi BM25 (k1 = 1.5, b = 0.75): Tokenizes title (2x weight), URL host/path slugs (docs, python, asyncio), and body text, applying a 1.35x boost to official documentation hosts (docs.*, developers.*, *.readthedocs.io).

  2. Multiplicative Recency × Proof-Count Boost: $$\text{final_score} = \text{score} \times \bigl(1 + 0.2(\text{recency} - 0.5)\bigr) \times \bigl(1 + 0.1(\text{proof_norm} - 0.5)\bigr)$$ where $\text{proof_norm} = \text{clamp}(0.5 + \ln(\text{providers.length})/10, 0, 1)$ rewards URLs corroborated by multiple search engines without letting a fresh but irrelevant page leapfrog a high-relevance match.

  3. Token Budget Packing (max_tokens): Packs extracted passages best-first into your exact context window budget (max_tokens: 600), skipping an oversized chunk to fit higher-density shorter chunks ranked behind it.

3. Quickstart (10 Seconds)

Step 1: Mint a Free Sandbox API Key (2,500 Credits/Month, No Card)

curl -s -X POST https://api.strata.sh/v1/keys/free \
  -H "Content-Type: application/json" \
  -d '{"label": "github-quickstart"}'

Step 2: Connect via Your Preferred Channel

Channel A — MCP Server (Claude Code, Cursor, OpenCode, Windsurf)

Add to ~/.config/opencode/opencode.jsonc or claude_desktop_config.json:

{
  "mcpServers": {
    "strata": {
      "command": "npx",
      "args": ["-y", "strata-mcp"],
      "env": {
        "STRATA_API_KEY": "st_free_YOUR_API_KEY",
        "STRATA_API_URL": "https://api.strata.sh"
      }
    }
  }
}

Channel B — Zero-Token Bash CLI (0 System-Prompt Overhead)

export STRATA_API_KEY="st_free_YOUR_API_KEY"
export STRATA_API_URL="https://api.strata.sh"

# 1. Search with Hindsight TEMPR token budget cap
npx strata-mcp search "Python asyncio TaskGroup exception handling" --limit 5 --max-tokens 600

# 2. Extract HTML, /llms.txt, or PDF files
npx strata-mcp extract "https://docs.python.org/3/library/asyncio-task.html"

# 3. Map a documentation site (<sitemapindex> + 4-worker concurrent BFS)
npx strata-mcp map "https://modelcontextprotocol.io" --limit 20

# 4. Find externally similar pages (Entity-Overlap tanh + BM25)
npx strata-mcp similar "https://docs.python.org/3/library/asyncio-task.html" --limit 5

# 5. Calculate monthly workload savings vs Tavily, Exa & Firecrawl
npx strata-mcp cost --searches 25000 --extracts 5000 --research 500

Channel C — 1-Line Drop-In Replacement for Tavily SDKs (POST /compat/tavily/search)

Already using Tavily? Change only the endpoint URL to https://api.strata.sh/compat/tavily/search and pay 1 credit instead of 2 on search_depth: "advanced":

curl -X POST https://api.strata.sh/compat/tavily/search \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "st_free_YOUR_API_KEY",
    "query": "Google Search Central robots.txt documentazione italiano",
    "search_depth": "advanced",
    "max_results": 5
  }'

4. All 12 MCP Tools & Credit Weights

MCP Tool

REST Endpoint

Credit Cost

Description

growth_search_cascade

POST /v1/search

1 credit

Multi-engine search, RRF fusion, BM25/semantic reranking, Hindsight TEMPR boosts, 1.8s highlights &amp; max_tokens packing.

growth_search_social

POST /v1/search

1 credit

Site-scoped search across X (x.com), Reddit, GitHub, and Hacker News.

growth_find_similar

POST /v1/similar

1 credit

Seed URL profiling + lexical cosine + BM25 + Entity-Overlap tanh + /v1/embeddings.

growth_map

POST /v1/map

1 credit

/robots.txt + /sitemap.xml (<sitemapindex>) + 4-worker BFS + Crawl4AI JS SPA fallback.

growth_extract_content

POST /v1/extract

1 credit

Single-URL extraction across HTML, /llms.txt, .md, and .pdf (unpdf) with 7-day SHA-256 cache.

growth_extract_structured

Local / MCP

1 credit

DOM table &amp; row extraction with deterministic where filtering (price > 100 && name ~ /^pro/i).

growth_analyze_content_gap

Local / MCP

1 credit

Deterministic SERP domain-diversity score (0–100) for SEO/AEO topic analysis.

growth_get_latest

Local / MCP

1 credit

Reads the cached discovery report from disk with zero network calls.

growth_extract_batch

Local / MCP

2–3 credits

Parallel extraction for up to 20 URLs (2 cr $\le 10$ URLs, 3 cr $11\text{–}20$ URLs) with BM25 passage selection.

growth_research

Local / MCP

3 cr (mini) / 5 cr (pro)

2-hop adaptive research: parallel Hop-1 sub-queries, emergent term extraction for Hop 2, RRF fusion &amp; cited report.

growth_run_discovery

Local / MCP

3 credits

Queries 10 authoritative AI/engineering sources and saves latest-report.json.

growth_crawl

Local / MCP

5 credits

Combines growth_map discovery with 3-worker parallel content extraction and highlight selection.

5. Self-Hosted Local Mode (Optional Docker Stack)

If you prefer to run Strata 100% locally with deedy5/ddgs (Rust primp TLS impersonation) and Crawl4AI:

git clone https://github.com/alexgenovese/strata.git
cd strata
npm install && npm run build
docker compose up -d
STRATA_MODE=local npm test

License

MIT © Alex Genovese

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