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676,375 tools. Updated 2026-10-19 11:37

"Methods and Tools for Extracting Paper Summaries" matching MCP tools:

  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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    Destructive
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  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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  • Record YOUR verdict on a paper — why it matters for your work, when to use it, or why you ruled it out. One note per paper, upserted (writing again replaces it), so it is safe to call repeatedly. Requires SF_API_KEY. WHY IT MATTERS: this note is the only thing that survives between sessions. list_library returns note_text on every saved paper, so a verdict written now is what a future session reads INSTEAD of re-reading the paper and re-deriving the same conclusion. WRITE A JUDGMENT, NOT A SUMMARY — the paper already carries llm_summary and an abstract, so restating what the paper says adds nothing. Write what those cannot: how it bears on YOUR problem. Prefer a claim someone could later prove wrong ("needs a labeled trace log we don't have", "our baseline — beat this on the 7B setting") over an unfalsifiable verdict ("interesting", "not very relevant"), because a mechanism can be re-checked when circumstances change and a sentiment cannot. State the basis when it is thin: a verdict formed from the abstract alone deserves "(abstract only)", since fetch_fulltext defaults to ~800 characters of the results section rather than the whole paper. Pass action='get' to read the existing note before overwriting it — worth doing when a prior session may already have judged this paper. To correct a note, just write the corrected text (it replaces).
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  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.
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  • Read the full text of a paper in the user's own paper library (uploaded PDFs). Text is extracted and page-numbered ([Page N]). Use this to answer questions about a specific paper the user uploaded. Call search_my_papers first when you only know a topic/title, not the paper id. Long papers are truncated; re-call with start_page/end_page to continue reading.
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  • Costs 2¢ per call. Compares 2-5 parts you already know side by side in one call (find candidates with search_parts or find_alternative first). Returns each part's details (per-provider specs, price breaks and links, the cached datasheet summary), a comparison table of parameters × parts built from each part's own provider data, and per-part flags: lifecycle (obsolete, NRND, last-time-buy), stock, datasheet_status, and resolved_as when a lookup landed on a different MPN. compact=true returns only the flags and the table, a much shorter answer; add include_summaries=true for the summaries. Never starts extraction: prefetch_datasheets first if you need summaries. If any input is not a real part number, the whole call is rejected with a 'rejected' map; a valid MPN no provider knows still gets a column, with an error. Example: compare_parts(['TPS54302', 'LM2596', 'MP2359']) datasheet_status: 'ready' → read it; 'extracting' / 'queued' / 'pending' / 'resolving' → poll check_extraction_status; 'not_extracted' → prefetch_datasheets; 'no_source' / 'unsupported' → request_datasheet_upload. Full list in the server instructions.
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Matching MCP Servers

  • A
    license
    A
    quality
    C
    maintenance
    Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.
    8
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.
    Apache 2.0

Matching MCP Connectors

  • Research methods for AI assistants: study design guidance, R and Python code, and citations

  • Check a plot document and get a link that opens it in Graph Paper. No account, no key.

  • Look up a single paper by its DOI. Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both. Returns: An envelope with found status and the paper in result, or a not-found message. A found paper counts as one result.
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  • Look up a single paper by its DOI. Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both. Returns: An envelope with found status and the paper in result, or a not-found message. A found paper counts as one result.
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  • Find paper titles, recorded contributors, DOI and exact repository versions without research or payment. Default: retained catalog metadata with observation times. Set searchRepositories=true only to explicitly send the query to arXiv/Crossref (at most two requests, no paper-body reading). Missing DOI, peer review and withdrawal/replacement status remain unknown. Returns a field-scoped bibliography card with provenance, a short reference and reusable BibTeX/RIS text. Use this for exact title/ordered author/year/journal/DOI/version questions before requesting paid research; original paper text is not required for recorded metadata.
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  • The catalog behind the other tools: indexed subjects/topics (for search_sambodh_knowledge and quiz subject filters), available PYQ years, published current-affairs month slugs, and NewsPulse GS-paper values. Call this first when unsure which parameter values are valid.
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  • List the saved payment methods (used only to pay for flight bookings, not for cards or adding funds). Returns each method with its id, brand, last 4 digits, and expiry, and marks the default one. Use setup_payment_method to add a new one. The gated tools set_default_payment_method and remove_payment_method also exist; call them by name even though they aren't in the tools list.
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  • Discover AgentMarketplace's capabilities, tools, auth methods, and scopes. Call this first when connecting to AgentMarketplace to understand what's available and how to authenticate. No authentication required. Returns a catalog of available tools, resources, auth methods, and scopes.
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  • Returns the FOUNDATIONAL WORK FOR A PAPER'S NICHE via the citation graph — the relative question ('what is foundational for THIS paper's specific sub-field', often itself only modestly cited) rather than the obvious global landmarks. Anchors on the paper, takes its embedding neighbourhood as the niche, and ranks what the niche cites into three tiers: `niche_roots` (the niche-specific foundations, ranked by how specifically the neighbourhood builds on them — surfaces canonical anchors that semantic search misses), `field_level` (broader secondary foundations), and `discipline` (universal landmarks like Attention Is All You Need, collapsed out of the way). Each paper carries `cited_by_in_niche` evidence so the claim is grounded, not asserted. Use this to trace prior art / lineage for a paper, or to find the canonical methods a niche is built on. Complements get_field_orientation (which is topic-anchored and retrieval-only). No Pro key and no LLM calls required.
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  • Add a paper to a collection, addressed by collection_id OR collection_name (get-or-create by name — no need to look up an id first). Nest with "/": collection_name "AgentOPA/Formal" files the paper under an "AgentOPA" folder. MUTATES: also auto-saves the paper to the library. Idempotent (adding a paper already in the collection is a no-op). Requires SF_API_KEY.
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  • List the members this account follows on the paper-trading feed (their paper-trading usernames). Read-only; requires login. Use `recommended_traders` to discover traders worth following, `trader_profile` for one member's stats, and `follow_trader` / `unfollow_trader` to change the list.
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  • Browse the complete WORKMINE catalog for free: paid API names, exact USDC-denominated prices, HTTP methods and URLs. Use this when comparing available tools; use find_workmine_tool when you already have a natural-language task. This catalog call itself does not execute or purchase a paid API.
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  • Get full metadata for a single paper by ID. Accepts a Semantic Scholar paper ID, or a prefixed ID like "DOI:10.1145/3292500", "arXiv:2106.15928", or "CorpusId:215416146". Returns abstract, TLDR summary, authors, venue, citation/reference counts, fields of study, and open-access PDF. Keyless.
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  • "How many times has paper [DOI] been cited" / "citation count for [paper]" / "is [study] highly cited" — incoming citation count for a DOI. Fast version of `citations` when you only need the number, not the citing DOIs.
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  • "How many references does paper [DOI] have" / "how big is the bibliography of [paper]" — outgoing reference count for a DOI. Fast version of `references` when you only need the number.
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