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io.github.artchristech/fillin
Search for AI agents. Closes the LLM-cutoff gap: CVEs, papers, frontier AI, prediction markets.
biocontext
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glama
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mcp.so
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nerq
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pulsemcp
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smithery
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Bring your own text -> the cheapest substrate for your reader — the MCP twin of HTTP POST /v1/encode. Not a search-result rendering trick: this is Glyph as a language anyone can speak. Give it a tool result, a RAG chunk, a document — it comes back as whichever form (dense photo-glyph image or plain text) is genuinely cheaper for your reader model's token billing, with the honest manifest attached. The trailing JSON block always carries a `selection` object {substrate, reader, reader_class, tier, rationale, estimates} so the choice is auditable from the token math — the same object the HTTP route returns. Billed at the flat query rate regardless of which substrate is chosen — text and glyph cost the same here, unlike retrieve_auto's answer substrate.
Synthesized post-cutoff answer with inline citations. Use this when your model is small / cheap / weaker at tool-result synthesis (Llama, Gemini Flash, Mistral, Nemotron, Qwen). Fillin runs a server-side LLM pass over the retrieved post-cutoff documents and returns a 150-250 word answer with [title](url) citations already embedded — you can quote it directly. Premium models (Opus, Sonnet, GPT-4o) usually get better results from `fillin_query` and synthesizing themselves, but this tool works for any caller. Costs more than fillin_query because of the synthesis pass. Returns: A dict with: - answer: the synthesized paragraph (str | None) - citations: list of {title, url} extracted from the answer - corpus_match: "strong" | "weak" | "none" — quality of retrieval - top_score: float — top reranked similarity score - model: the synthesizer model used (e.g. claude-haiku-4-5) - reason: set when answer is None (e.g. "no_relevant_docs") - results: raw post-cutoff documents (same shape as fillin_query) - cutoff, query, gap_days: echoes for context
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Hugging Face