LLM Context#

Paid LLM Context proxy via x402 pay-per-use (HTTP 402).

Prerequisites: This skill requires x402-payment. Complete the setup steps before first use.

Service URLs#

RoleDomainAPI Providerhttps://www.cpbox.ioFacilitatorhttps://www.cppay.finance

Endpoint (Agent Interface)#

http

GET  /api/x402/llm-context
POST /api/x402/llm-context/post

Payment Flow (x402 Protocol)#

1. First request (no PAYMENT-SIGNATURE) -> 402 Payment Required with requirements JSON
2. Client signs (EIP-712) -> PAYMENT-SIGNATURE
3. Retry with PAYMENT-SIGNATURE -> Server settles and returns JSON

With @springmint/x402-payment or x402-sdk-go, payment is automatic.

LLM Context delivers pre-extracted, relevance-ranked web content optimized for grounding LLM responses in real-time search results. Unlike traditional web search APIs that return links and snippets, LLM Context extracts the actual page content—text chunks, tables, code blocks, and structured data—so your LLM or AI agent can reason over it directly.

LLM Context vs AI Grounding#

FeatureLLM Context (this)AI Grounding ( answers)OutputRaw extracted content for YOUR LLMEnd-to-end AI answers with citationsInterfaceREST API (GET/POST)OpenAI-compatible /chat/completionsSearchesSingle search per requestMulti-search (iterative research)SpeedFast (<1s)SlowerPlanSearchAnswersEndpoint/res/v1/llm/context``/res/v1/chat/completionsBest forAI agents, RAG pipelines, tool callsChat interfaces, research mode

Endpoint#

http

GET  https://www.cpbox.io/api/x402/llm-context
POST https://www.cpbox.io/api/x402/llm-context/post

Authentication: handled by x402 payment middleware

Optional Headers:

• Accept-Encoding: gzip — Enable gzip compression

Quick Start#

GET Request#

bash

curl -s "https://www.cpbox.io/api/x402/llm-context?q=tallest+mountains+in+the+world" \
  -H "Accept: application/json"

POST Request (JSON body)#

bash

curl -s --compressed -X POST "https://www.cpbox.io/api/x402/llm-context/post" \
  -H "Accept: application/json" \
  -H "Accept-Encoding: gzip" \
  -H "Content-Type: application/json" \
  -d '{"q": "tallest mountains in the world"}'

With Goggles (Inline)#

bash

curl -s "https://www.cpbox.io/api/x402/llm-context" \
  -H "Accept: application/json" \
  -G \
  --data-urlencode "q=rust programming" \
  --data-urlencode 'goggles=$discard
$site=docs.rs
$site=rust-lang.org'

Using with x402-payment#

bash

npx @springmint/x402-payment \
  --url "https://www.cpbox.io/api/x402/llm-context?q=rust+ownership&maximum_number_of_tokens=4096" \
  --method GET

Parameters#

Query Parameters#

ParameterTypeRequiredDefaultDescriptionqstringYes-Search query (1-400 chars, max 50 words)countrystringNoUSSearch country (2-letter country code or ALL)search_langstringNoenLanguage preference (2+ char language code)countintNo20Max search results to consider (1-50)

Context Size Parameters#

ParameterTypeRequiredDefaultDescriptionmaximum_number_of_urlsintNo20Max URLs in response (1-50)maximum_number_of_tokensintNo8192Approximate max tokens in context (1024-32768)maximum_number_of_snippetsintNo50Max snippets across all URLs (1-100)maximum_number_of_tokens_per_urlintNo4096Max tokens per individual URL (512-8192)maximum_number_of_snippets_per_urlintNo50Max snippets per individual URL (1-100)

Filtering & Local Parameters#

ParameterTypeRequiredDefaultDescriptioncontext_threshold_modestringNobalancedRelevance threshold for including content ( strict/ balanced/ lenient)enable_localboolNonullLocal recall control ( true/ false/ null, see below)gogglesstring/listNonullGoggle URL or inline definition for custom re-ranking

Context Size Guidelines#

Task Typecountmax_tokensExampleSimple factual52048"What year was Python created?"Standard queries208192"Best practices for React hooks"Complex research5016384"Compare AI frameworks for production"

Larger context windows provide more information but increase latency and cost (of your inference). Start with defaults and adjust.

Threshold Modes#

ModeBehaviorstrictHigher threshold — fewer but more relevant resultsbalancedDefault — good balance between coverage and relevancelenientLower threshold — more results, may include less relevant content

Local Recall#

The enable_local parameter controls location-aware recall:

ValueBehaviornull (not set)Auto-detect — local recall enabled when any location header is providedtrueForce local — always use local recall, even without location headersfalseForce standard — always use standard web ranking, even with location headers

For most use cases, omit enable_local and let the API auto-detect from location headers.

Location Headers#

HeaderTypeDescriptionX-Loc-LatfloatLatitude (-90.0 to 90.0)X-Loc-LongfloatLongitude (-180.0 to 180.0)X-Loc-CitystringCity nameX-Loc-StatestringState/region code (ISO 3166-2)X-Loc-State-NamestringState/region nameX-Loc-Countrystring2-letter country codeX-Loc-Postal-CodestringPostal code

Priority: X-Loc-Lat + X-Loc-Long take precedence. When provided, text-based headers (City, State, Country, Postal-Code) are not used for location resolution. Provide text-based headers only when you don't have coordinates.

Example: With Coordinates#

bash

curl -s "https://www.cpbox.io/api/x402/llm-context" \
  -H "Accept: application/json" \
  -H "X-Loc-Lat: 37.7749" \
  -H "X-Loc-Long: -122.4194" \
  -G \
  --data-urlencode "q=best coffee shops near me"

Example: With Place Name#

bash

curl -s "https://www.cpbox.io/api/x402/llm-context" \
  -H "Accept: application/json" \
  -H "X-Loc-City: San Francisco" \
  -H "X-Loc-State: CA" \
  -H "X-Loc-Country: US" \
  -G \
  --data-urlencode "q=best coffee shops near me"

Goggles (Custom Ranking)#

Goggles let you control which sources ground your LLM — essential for RAG quality.

Use CaseGoggle RulesOfficial docs only$discard\n$site=docs.python.orgExclude user content$discard,site=reddit.com\n$discard,site=stackoverflow.comAcademic sources$discard\n$site=arxiv.org\n$site=.eduNo paywalls$discard,site=medium.comMethodExampleHosted--data-urlencode "goggles=https://<hosted-goggle-url>"Inline--data-urlencode 'goggles=$discard\n$site=example.com'

Hosted goggles should be hosted on a public URL and include ! name:, ! description:, ! author: headers. Inline rules need no registration.

Syntax: $boost=N / $downrank=N (1–10), $discard, $site=example.com. Combine with commas: $site=example.com,boost=3. Separate rules with \n ( %0A).

Allow list: $discard\n$site=docs.python.org\n$site=developer.mozilla.org — Block list: $discard,site=pinterest.com\n$discard,site=quora.com

Resources: See your upstream provider's Goggles documentation.

Response Format#

Standard Response#

json

{
  "grounding": {
    "generic": [
      {
        "url": "https://example.com/page",
        "title": "Page Title",
        "snippets": [
          "Relevant text chunk extracted from the page...",
          "Another relevant passage from the same page..."
        ]
      }
    ],
    "map": []
  },
  "sources": {
    "https://example.com/page": {
      "title": "Page Title",
      "hostname": "example.com",
      "age": ["Wednesday, January 15, 2025", "2025-01-15", "392 days ago"]
    }
  }
}

Local Response (with enable_local)#

json

{
  "grounding": {
    "generic": [...],
    "poi": {
      "name": "Business Name",
      "url": "https://business.com",
      "title": "Title of business.com website",
      "snippets": ["Business details and information..."]
    },
    "map": [
      {
        "name": "Place Name",
        "url": "https://place.com",
        "title": "Title of place.com website",
        "snippets": ["Place information and details..."]
      }
    ]
  },
  "sources": {
    "https://business.com": {
      "title": "Business Name",
      "hostname": "business.com",
      "age": null
    }
  }
}

Response Fields#

FieldTypeDescriptiongroundingobjectContainer for all grounding content by typegrounding.genericarrayArray of URL objects with extracted content (main grounding data)grounding.generic[].urlstringSource URLgrounding.generic[].titlestringPage titlegrounding.generic[].snippetsarrayExtracted smart chunks relevant to the querygrounding.poiobject/nullPoint of interest data (only with local recall)grounding.poi.namestring/nullPoint of interest namegrounding.poi.urlstring/nullPOI source URLgrounding.poi.titlestring/nullPOI page titlegrounding.poi.snippetsarray/nullPOI text snippetsgrounding.maparrayMap/place results (only with local recall)grounding.map[].namestring/nullPlace namegrounding.map[].urlstring/nullPlace source URLgrounding.map[].titlestring/nullPlace page titlegrounding.map[].snippetsarray/nullPlace text snippetssourcesobjectMetadata for all referenced URLs, keyed by URLsources[url].titlestringPage titlesources[url].hostnamestringSource hostnamesources[url].agearray/nullPage modification dates (when available)

Note: Snippets may contain plain text OR JSON-serialized structured data (tables, schemas, code blocks). LLMs handle this mixed format well.

Use Cases#

• AI Agents: Give your agent a web search tool that returns ready-to-use content in a single call
• RAG Pipelines: Ground LLM responses in fresh, relevant web content
• AI Assistants & Chatbots: Provide factual answers backed by real sources
• Question Answering: Retrieve focused context for specific queries
• Fact Checking: Verify claims against current web content
• Content Research: Gather source material on any topic with one API call

Best Practices#

• Token budget: Start with defaults ( maximum_number_of_tokens=8192, count=20). Reduce for simple lookups, increase for complex research.
• Source quality: Use Goggles to restrict to trusted sources. Set context_threshold_mode=strict when precision > recall.
• Performance: Use smallest count and maximum_number_of_tokens that meet your needs. For local queries, provide location headers.