# Fillin MCP server

Search for AI agents. Closes the LLM-cutoff gap: CVEs, papers, frontier AI, prediction markets.

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-artchristech-fillin
- Website: https://fillin.glyphapi.dev

## Install
- Endpoint: https://fillin.glyphapi.dev/mcp
- Auth: Not captured

## Setup notes
- Remote endpoint: https://fillin.glyphapi.dev/mcp

## Tools
- fillin_query - 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}
     Endpoint: https://fillin.glyphapi.dev/mcp
- fillin_answer - 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
     Endpoint: https://fillin.glyphapi.dev/mcp
- fillin_stats - Get corpus stats — total docs, date range, freshness. Endpoint: https://fillin.glyphapi.dev/mcp
- fillin_health - Liveness + freshness — host, total docs, earliest, latest. No auth required. Endpoint: https://fillin.glyphapi.dev/mcp
- query_cves - 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.
     Endpoint: https://fillin.glyphapi.dev/mcp
- query_papers - 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.
     Endpoint: https://fillin.glyphapi.dev/mcp
- query_frontier - 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".
     Endpoint: https://fillin.glyphapi.dev/mcp
- query_markets - 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.
     Endpoint: https://fillin.glyphapi.dev/mcp

## Resources
Not captured

## Prompts
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## Metadata
- Owner: io.github.artchristech
- Version: 0.2.0
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: May 22, 2026
- Source: https://registry.modelcontextprotocol.io