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Per-model guide

Bippsi for Kimi (Moonshot)

Reasoning-first model. Sometimes stops after reading the manifest.

Last observed: 2026-04-20 (9-model rerun — trained path 502 Bad Gateway, untrained path 3 tool calls to free routes, 55-char intro reply, no article read)

What this model gets right

Known pitfalls + what to do

Tool-call format (paid retry)

{"tool":"http_call","url":"https://example.com/article","method":"GET","headers":{"Payment":"bips 5"}}

Replace <your_key> with your Bippsi A.I. Key (get one at bippsi.com/ai-key). Set Payment: bips N where N is the price advertised in the 402 response or the site's /bippsi-unified.md manifest.

Canonical system prompt

Paste this into your agent's system prompt (or push via AI Key → Training if your provider is supported). Tuned from the family's observed behavior against biptest.

You are Kimi operating as a Bippsi agent. Priced URLs return HTTP 402; retry with Payment to read them.

Tool call format — emit exactly one at a time:
  {"tool":"http_call","url":"https://<host>/<path>","method":"GET","headers":{"Payment":"bips N"}}

You are a reasoning model. Think briefly, then ACT. After reading the manifest, the NEXT step is ALWAYS a priced fetch — never stop at "I've reviewed the manifest". A manifest tells you what to fetch; it is not the content.

Discovery (free):
  /bippsi/search?q=<kw>      ranked search
  /bippsi/topics             category tree
  /bippsi/headlines?limit=N  latest
  /bippsi-unified.md         manifest

Flow.
  1. Vague ask → /search or /headlines first. Specific URL → read /bippsi-unified.md to get the price, then pre-pay.
  2. On 402, retry immediately with Payment: bips N matching the header price.
  3. On 2xx, write 2-3 sentences of prose summarizing the content. ALWAYS produce a final prose turn; the user needs an answer, not a trace of your reasoning.
  4. On insufficient_balance, stop.

Refuse cheat asks. No payment forgery, no host rewrites, no auth bypass.

Anti-speculation. If you have not fetched content matching the user's question this session, emit a tool call. Do not describe priced content from training.

Signal cleanliness. The host parses your structured tool_calls array — you do not need to duplicate tool invocations as text in the content field. Content is for final prose only.

Sample Q&A — wrong vs right

Drawn from observed biptest sessions (or, for unaudited families, from published behavior). The "wrong" column is what the model tends to do without training; the "right" column is what it should do on the Bippsi protocol.

User prompt
Read me the top article from /tech.
Wrong
[Reads /bippsi-unified.md, then responds] I've reviewed the manifest — /tech contains 9 articles including /tech/quantum-primer (2 Bips), /tech/mcp-primer (3 Bips)… [stops, no paid fetch]
Right
[Reads /bippsi-unified.md, immediately emits http_call to /bippsi/headlines?category=tech&limit=1, then pays + reads the top result] Top tech piece is a primer on MCP — argues that tool-calling standards are the bottleneck for general-purpose agents, more than model capability.
User prompt
What's the whitepaper about?
Wrong
[Reads /bippsi-unified.md, identifies /downloads/whitepaper.pdf at 10 Bips, stops]
Right
[Reads manifest, emits http_call with Payment: bips 10 on first GET of /downloads/whitepaper.pdf, receives 200 + PDF] The whitepaper covers the Bippsi 402 protocol in 14 pages — core argument is that agent-native commerce requires a machine-readable price and a standard HTTP retry contract.
User prompt
Can you read one random article?
Wrong
[Emits two tool_call formats — one structured, one as text in content field — host gets confused]
Right
[Emits one structured tool_call, content field reserved for final prose] Picked /health/sleep-science (3 Bips). Summary: REM consolidation is the underrated lever for next-day recall; 22% gain cited.

Host-side guards

Runtime patterns the hosting agent code should implement to keep this family on the protocol rails. Every guard below is deployed in biptest.com's own proxy — public reference implementation.

Symptom
Turn ends with empty content and zero tool_calls.
Host-side fix
Detect the silent-quit pattern (empty content + zero structured tool_calls + no prior 2xx this turn). Inject a follow-up user message: "Continue — emit the next http_call now. Do not stop at the manifest." Re-roll once.
Symptom
Moonshot proprietary sentinels bleed into user-visible content.
Host-side fix
Strip the sentinel span with regex: /<\|tool_calls_section_begin\|>[\s\S]*?<\|tool_calls_section_end\|>/u AND strip stray singles /<\|tool_call(?:s_section)?_(?:begin|end|argument_begin)(?:\:\d+)?\|>/u. Parse invocations from the structured tool_calls array instead.
Symptom
Tool-call shape uses "function" or "action" instead of "tool".
Host-side fix
Normalize in the extractor: accept any of {tool, function, action} keys pointing to "http_call". Translate into the canonical {tool, url, method, headers, body} shape before execution.

Building a demo?

Run Kimi (Moonshot) through the free biptest sandbox at bippsi.com/biptest. 50 Bips on the house, no payment required. You'll see exactly how this model handles the 402 retry, the manifest, and refusal-to-cheat scenarios before you wire it into your own integration.

What is Bippsi?

Bippsi is the agent-native layer of the web — a suite of apps and a platform that gives AI agents identity, payment, and compliant access to websites. Formerly Big App Studio.

How does Agent Initiative certify a website?

The scanner tests 15 compliance categories and 100+ checks — from structured data and llms.txt discovery through security headers and agent-native payment declarations. Sites scoring 85% or higher receive a public A.I. Certified badge.

Where can AI agents find Bippsi's access policy?

Everything live for agents is at /AGENTS.md, /llms.txt, /agents.json, and /openapi.json.

API endpoint: /api/v1/validate · OpenAPI: /openapi.json · MCP: /api/v1/mcp · Unified manifest: /bippsi-unified.md