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Maha Context Compiler
x402
Maha Context Compiler is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.035001/call, status unknown (last checked 2026-10-17).
Ranks and packs public or synthetic text passages using keyword/BM25 scoring, returning scored excerpts with source IDs, hashes, and model-neutral token count estimates.
Price / call$0.035001
Success—
StatusUnknown
How this service works
Rank and pack supplied public/synthetic passages with keyword or BM25 scoring, source IDs and hashes. Token counts are model-neutral estimates, not billing or factual verification. Public/synthetic nonpersonal input only; 32 KiB input, 65 KB output. Result retention: 24 hours; minimal receipt metadata: 90 days. No model calls or factual certification.
Output
A ranked list of the supplied passages ordered by BM25/keyword score, each annotated with its source ID, a content hash, and a model-neutral token count estimate. Results are retained for 24 hours; minimal receipt metadata is kept for 90 days. No factual verification or model inference is performed.
Example use cases
RAG pipeline context packing
“I've got 20 retrieved public document chunks for my RAG pipeline and I need them ranked by BM25 score with hashes attached so I can pick the top ones that fit within my context window — run them through the context compiler with public dataClass.”
Synthetic data relevance ranking
“I generated a batch of synthetic training passages and need to rank them by keyword relevance, get token count estimates for each, and attach source IDs before I pass them downstream — please compress and score them with dataClass synthetic and nonpersonalMetadata true.”
Pre-inference context budget check
“Before I send these 30 public news excerpts to the model, can you rank them by BM25 score and give me token estimates so I know which ones to drop to stay within my context budget? Use public dataClass and confirm no personal data is included.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/context/legacy/context-compression?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to rank and pack a set of public or synthetic text passages by BM25/keyword relevance before feeding them into an LLM context window, and you want source hashes and model-neutral token estimates without making any model calls. It is not appropriate for personal data, factual verification, semantic embedding-based ranking, or inputs larger than 32 KiB.
Side effects
- Input exceeds 32 KiB limit — request rejected
- Output would exceed 65 KB — truncation or rejection
- dataClass not set to 'public' or 'synthetic' — validation error
- nonpersonalMetadata not set to true — request rejected
- Malformed request object — 400-level error
- Service unavailable — no result returned within retention window
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request | object | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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