mcp server
Fillin
Search for AI agents. Closes the LLM-cutoff gap: CVEs, papers, frontier AI, prediction markets.
Description as published by the maintainer. Source
- version 0.2.1
- active
- retrieval
- security
active — Registry entry last updated 2026-05-23. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
14 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
encode(text, reader, verbatim)- 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. Required: text.
fillin_answer(k, query, cutoff)- 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 Required: query, cutoff.
fillin_buy_mint(mint_id, model_family, cutoff_quarter)- Buy a listed mint. Debits your bearer balance, credits the seller (minus Fillin's rake), records the transaction, and returns the full mint payload including the previously-paywalled reasoning_graph. Requires FILLIN_API_KEY with sufficient balance for the mint's list price. Required: mint_id, model_family, cutoff_quarter.
fillin_health- Liveness + freshness — host, total docs, earliest, latest. No auth required.
fillin_market_search(limit, cluster_id, model_family, only_for_sale, cutoff_quarter)- Search the Fillin marketplace for minted (data + reasoning) assets matching your fingerprint. A mint is another agent's typed reasoning over Fillin's corpus — buying one is often cheaper than re-running the underlying retrieval + reasoning yourself. Returns {fingerprint, mints[]}. Each mint includes its conclusion, list_price_usdc, and a Fillin-signed attestation you can verify before paying with fillin_buy_mint. Required: model_family, cutoff_quarter.
fillin_mint(query, evidence, conclusion, model_family, cutoff_quarter, list_price_usdc, reasoning_graph)- Mint a (data + reasoning) asset on the Fillin marketplace. Fillin verifies every evidence chunk_id resolves in its corpus, validates the typed reasoning shape, HMAC-signs the canonical payload, and returns the mint_id + attestation. Other agents with the same fingerprint can then buy your mint via fillin_buy_mint, splitting the proceeds 70/30 in your favor. Requires FILLIN_API_KEY (a Fillin bearer token). Required: query, model_family, cutoff_quarter, evidence, reasoning_graph, conclusion.
fillin_query(k, query, cutoff)- Retrieve documents published after a training cutoff, ranked by similarity. Call this whenever the user asks about events, releases, papers, issues, or news that might post-date your training data. Fillin only returns documents published AFTER `cutoff`, so nothing returned is redundant with what the model already knows. Args: query: Natural-language search query (e.g. "rust async runtimes"). Max 512 characters. cutoff: ISO-8601 date representing the agent's training cutoff (e.g. "2026-01-01"). Documents on or before this date are excluded from results. k: Number of documents to retrieve, 1-20. Defaults to 5. Returns: A dict with: - cutoff: echoed cutoff (ISO timestamp) - query: echoed query - gap_days: days between cutoff and now - results: list of {id, source, url, published_at, title, text, score} Required: query, cutoff.
fillin_stats- Get corpus stats — total docs, date range, freshness.
glyph_search(k, tier, query, cutoff)- Same as fillin_query, but returns the result pieces rendered as **photo glyph** image(s) — dense, vision-readable pages — followed by a JSON citation index ({n, source, url, title, published_at, page}). Read the image(s) directly with your vision capability; use the citation index to attribute or follow up. Glyphs are for comprehension and fact-extraction, not verbatim quotes (vision models paraphrase) — open the url for exact text. Billed at the flat /query rate; rendering is free. Required: query, cutoff.
query_cves(k, query, cutoff, min_severity)- Daily snapshot of CVE / supply-chain advisories from NVD, GitHub Security Advisories, and OSV. Use before merging dependency updates, when triaging an alert, or when a user asks "is package X compromised". Each result row carries a structured `affected` list (one entry per affected package: ecosystem, name, vulnerable_range, patched_range) and a numeric `severity_score` (CVSS baseScore, nullable on OSV-only rows). A buyer can act on the returned row — pin to `patched_range` — without a second hop to NVD or GHSA. Required: query, cutoff.
query_frontier(k, query, cutoff)- Daily snapshot of frontier AI lab announcements + HuggingFace trending model releases. Sources: OpenAI / DeepMind / Meta / Mistral blog RSS, Anthropic + HF blogs (via shared rss corpus), and the HF trending models API. Use when a user asks "what model dropped" or "did <lab> announce X". Required: query, cutoff.
query_markets(k, query, cutoff)- Active prediction markets across Polymarket, Kalshi, Manifold, and Metaculus. Use when a user asks "is there a market on X", "what odds is the market giving Y", or before any agent action that should be informed by a market price. Each result row carries the question, venue, close date, volume, and a first-sight price snapshot embedded in `text`. Prices in the corpus are point-in-time at first ingestion — for live pre-trade pricing, follow the `url` to the venue and read the current quote there. Required: query, cutoff.
query_papers(k, query, cutoff)- Daily snapshot of new research relevant to AI/ML/agents. Union of arXiv (cs.AI/cs.LG/cs.CL/cs.CR/cs.DC), HuggingFace daily papers (with upvote signal in title), and bioRxiv. Use when a user asks about a new technique, paper, or benchmark. Required: query, cutoff.
retrieve_auto(k, query, cutoff, reader, verbatim)- One retrieval, auto-picked substrate — the MCP twin of HTTP POST /v1/retrieve with substrate="auto". Runs a single post-cutoff retrieval, then returns whichever delivery substrate is cheapest AND legible for your `reader` model's token billing: - text — raw result pieces (Claude/GPT pixel billing, or any unknown reader). - glyph — a dense photo-glyph image (Gemini/Qwen flat-tile billing) you read with vision; the raw pieces ride along as a citation index. - answer — a pre-cited synthesized paragraph (weak tool-callers; needs a server LLM key). The trailing JSON block always carries a `selection` object {substrate, reader, reader_class, tier, rationale, estimates} so the choice is auditable from the honest token math — the same object the HTTP route returns. When the pick is glyph, the page image(s) precede that JSON block. Pricing matches /v1/retrieve: text/glyph bill the flat /query rate, answer bills the answer rate. The answer rate is charged up front and the delta is refunded when the pick resolves to text/glyph, so you always pay exactly the right rate. Required: query, cutoff.
Last successful function declaration observed on . Source: https://fillin.glyphapi.dev/mcp/. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://fillin.glyphapi.dev/mcp/.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 0.2.1 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-05-23 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-05-23 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| mcp tools declared | 14 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | fillin.glyphapi.dev | |
| mcp endpoint status | ok | The server listed 14 functions when asked. | as of probe | fillin.glyphapi.dev |
Where to get it
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