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536,367 tools. Updated 2026-09-08 18:01

"A server for finding academic papers" matching MCP tools:

  • List the public disclosure feeds this server aggregates, how many disclosures are cached per source, each source's newest item and an honest staleness flag, plus cache ages. Takes no arguments. Also states the scope plainly: public feeds only — no .onion access, no arbitrary fetching or crawling, no credential or PII output. Check this first if another tool's answer looks thin: a stale live feed is a finding, not background noise.
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  • Read a paper's body text by DOI, one page of characters at a time. Use this when you need the ACTUAL text of a paper — not term-matched snippets. It returns the body sliced by character offset so you can page through the whole document. **How it differs from other tools:** - `search_literature` returns up to 5 term-matched ~500-char excerpts — good for finding passages, not reading straight through. - `get_full_text` returns a proxy stitched only from sentences that cite other works — lossy, citation-only. - `read_fulltext` (this tool) returns verbatim body text, linearly, with pagination. **What you get — the `source` field:** - `"fulltext"` — verbatim full text, for open-access papers with a permissive license (or papers your org is entitled to) that have indexed full text. - `"abstract"` — the paper's abstract, returned as a fall-back when verbatim full text is access-restricted or not indexed. Abstracts are public, so most papers return at least this. - `null` — no readable text at all; use search_literature's `access` field for a link. `contentDenied` is true whenever full text was NOT served — i.e. any time `source` is not `"fulltext"` (access-restricted, not indexed, or nothing). So `source: "abstract"` still has `contentDenied: true`. Always check `source`: if it is not `"fulltext"` you are NOT reading the full paper. The `message` field explains why. **Paging:** each call returns up to 8000 characters. Read the first page with `offset: 0`, then set `offset` to the previous `offset + returnedChars` while `hasMore` is true. `totalChars` is the length of whatever `source` you got. Character offsets are only stable within a session — do not persist them across days (re-indexing shifts positions).
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  • Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".
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  • Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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Matching MCP Servers

  • A
    license
    A
    quality
    C
    maintenance
    Enables software and AI to work with accounting engagements through portable concepts such as trial balances, adjustments, working papers, and review notes, with declared adapter capabilities and validate-before-commit mutations.
    5
    Apache 2.0

Matching MCP Connectors

  • MCP server for academic research data including scholarly papers, citations, research trends, and publication metadata for AI agents.

  • The verified hub for conferences and journals. Powered by AI to match your scholarly ambitions with the world's most prestigious academic opportunities.

  • Return a single recommended VPS provider for users who do not yet have a server. Call this ONLY when the user explicitly says they have no server. The user buys the VPS at this provider and comes back with IP + password.
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  • No arguments. Returns how many MCP servers have been read at source level, and the share of them with each category of finding (credential access, network egress, install-time execution, prompt-injection surface). Use this to judge whether checking a specific server is worth it before you look one up. It reports aggregate counts only - no per-server findings, and no verdict about any individual server.
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  • Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".
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  • Search claim-level evidence extracted from full-text scientific papers. Args: query: Search terms (e.g. "CRISPR gene editing efficiency"). SHORT, concise queries are best. English language only. Use days_back, num_results, min_citations, and study_type instead of adding years or filters to the query. num_results: Number of results to return (1-100, default 16). First 50 results are free, then metered per result for paid users. days_back: Only return papers published within the last N days. min_citations: Only return papers with at least this many references cited. study_type: Only return papers of this study type. One of: primary study | systematic review | meta-analysis | narrative review | protocol | dataset | commentary | other. output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both. Returns: An envelope whose results list contains papers with claims, experiments, exact results, demonstrated scope, limitations, and provenance.
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  • Create + publish a piece. Pass a SIGN-IN-WITH-X header value you built and signed locally, plus the post fields. Returns the created post + public url; the server never holds your keys. Sell the observation, not the genre. Title the concrete finding in present tense with the specifics that carry it (names, numbers, dates), not the format ("playbook", "roundup"). Open the excerpt and first lines with the finding, not a tease. Publish with the answer card FILLED (questions or tasks, scope, exclusions, provenance): cacheEligibleMissing names legacy public-preview gaps; card completeness never changes rank or candidacy. Mint the header WITHOUT a fetch loop (SIWX here is CLIENT-driven, so do NOT use wrapFetchWithSIWx, which waits for a challenge Tenjin never sends): `encodeSIWxHeader({ ...info, address, signatureScheme: 'eip191', signature })` over `createSIWxMessage(info, address)` from @x402/extensions/sign-in-with-x, with a CAIP-122 `info` whose `domain` is this site's host and `nonce` is client-minted single-use. Full worked example in /llms.txt.
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  • Map the conceptual landscape around a topic ACROSS THE PAPER CORPUS. Searches papers and their chunks, not the layer-2 claim graph — for published CLAIMS on a topic use methodist_explore_topic. Instead of returning a ranked list of papers, returns N distinct conceptual clusters with representative chunks. Built on keyConcept LLM-extracted markers diversification. Use for "what approaches exist to X" queries — answers with thematic map rather than ranked list. Better than search when you want breadth over depth. Temporal bias note: for topics with dense recent literature (e.g. current LLM research), the default ordering favors recent papers because vector similarity finds them first; specify dateTo for historical exploration of mature topics, or dateFrom+dateTo to slice a specific era. Diversification cap (maxClustersPerPaper) limits how many clusters can have the same source paper as representative chunk — protects against single-paper dominance.
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  • Produce a deterministic remediation REQUEST bundle (rubric + fix schema + per-finding metadata + fingerprints) for YOU (the host agent) to fix. This tool calls no model and needs no key. For each finding, propose the corrected FULL file content, then VERIFY with verify_fix and keep only fixes that clear the finding. Never touch files with secrets; never auto-merge. Pass 'findings' from scan_path --format json.
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  • List all 197 papers in the Urantia Book with their metadata (id, title, partId, labels). Use toc.get for a hierarchical view instead.
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  • Get Lenny Zeltser's IR cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Search the MCP Marketplace catalog. With a free-text `query` and default `sort`, results are ranked by semantic similarity (gte-small embeddings + cosine similarity), so natural-language queries like 'manage my calendar', 'something to read PDFs', or 'database for my agent' work as well as keyword searches. Each result includes `security_score` (0-10), `risk_level` (low/moderate/high/critical), `critical_findings` (count of severity=critical|high findings), pricing, rating, install count, and a URL. `ranking_mode` in the response indicates whether semantic or keyword matching was used. Before recommending an install, call get_server for full details including every flagged finding — critical_findings > 0 means the server has known security issues you must surface to the user.
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  • Read the full text of one Celestia whitepaper or research PDF by slug. Celestia papers only — not arbitrary web PDFs (use a web-search tool for those). Call list_whitepapers first to get a valid slug.
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  • POST-ACTION Wallet Secret Guardian ($0.02). Scans for BIP-39 seed phrases (12 or 24 consecutive wordlist words), raw hex or WIF-format private keys, Ethereum/Bitcoin wallet addresses, and API keys/bearer tokens appearing near wallet/custody/signing terminology. Any finding results in NO_COMMIT — wallet secrets have no safe threshold, unlike other DCL evaluators. Returns a `sanitized_output` with all matches redacted (null if nothing was found) and a masked `redacted_sample` per finding — the real value is never returned or stored server-side.
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  • Returns CANDIDATE FOUNDATIONAL PAPERS for a research topic — cheap retrieval only, no synthesis. Ranks papers by a blend of citation count (0.6 weight, captures importance) and semantic similarity to your topic (0.4 weight). Use this to bootstrap a literature survey or get a fast sense of the landscape. For a synthesized orientation report (key concepts, open problems, reading order), use the /field-guide skill which calls this tool internally. Does not require a Pro API key — no LLM calls are made.
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  • Get remediation advice for a single finding as GitHub-flavored markdown. When AI Assist is enabled and within budget this is a suggestion written for this exact finding; otherwise it falls back to the static guidance-library text and says so in 'source'. Unlike the other reads this one can spend AI budget, which is why it is a separate tool. Use this for advice on one finding; for the whole library use list_security_guidance. Requires project context.
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