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Scholar Feed

by io.github.YGao2005

Get Paper

get_paper
Read-only

Get full details for one or more papers by arXiv ID. Pass a single-element array for one paper; pass multiple IDs to batch-fetch up to 50 papers in one call. Pass format='bibtex' to get a .bib citation entry (bibtex is single-paper only; for multi-paper bibtex, call repeatedly). Default returns a lean 13-field shape (arxiv_id, title, authors, year, categories, has_code, github_url, citation_count, venue_name, llm_summary, llm_significance, llm_novelty_score, impact_pct — where impact_pct is the ML-forecast impact percentile 0-100 computed WITHIN the paper's own arXiv-category cohort, so it is a cohort-relative rank rather than an absolute score, and is NULL on older papers outside the recent ~90-day scoring window). Pass verbose=true for the full shape with structured extraction (method_name, contribution_type, task_category, datasets, baselines) and institution_tags. Use fields='arxiv_id,title,abstract' to select an exact subset, or fetch_fulltext with sections='all' for the full paper.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoComma-separated list of fields to return (e.g. 'arxiv_id,title,llm_summary,abstract'). If omitted, returns the lean 12-field default unless verbose=true.
formatNoResponse format. 'json' (default) returns structured paper data. 'bibtex' returns a .bib citation entry. Bibtex mode uses the first ID in arxiv_ids.
verboseNoIf true, returns the full 28-field paper shape (method/task/dataset extraction, application_domain, baselines, etc.). Default false returns the lean 12-field set. Ignored when `fields` is provided.
arxiv_idsYesOne or more arXiv IDs. Single-paper lookup uses [id]; batch lookup passes multiple IDs (max 50). Example: ['2407.15831'] or ['2407.15831', '2402.09906'].

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
modeNoSearch mode actually applied.
noteNo
pageNo
sortNoSearch sort order actually applied.
countNo
limitNo
topicNo
totalNoTotal results available for the query. null when the count was skipped (query-less browse, or the count query timed out).
bibtexNoBibTeX entry (format='bibtex').
formatNo
papersNoMatched / returned papers.
messageNo
directionNoCitation direction (get_citations: citing | cited_by).
not_foundNoRequested IDs that had no match.
next_cursorNoKeyset cursor for the next page, or null when exhausted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint/destructiveHint annotations, the description discloses important behavioral details: bibtex is single-paper only and requires repeated calls for multiple papers, impact_pct is a cohort-relative percentile computed within an arXiv-category cohort and is NULL for older papers, and verbose mode returns structured extraction fields. This gives the agent a realistic model of the tool's behavior without needing a call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense and every clause adds useful guidance, but it is structured as one long run-on sentence with many semicolons. It is appropriately sized for the tool's complexity, but breaking it into shorter, structured sentences would improve readability and machine parsing. Front-loading the core purpose is done well.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with multiple modes (json/bibtex, lean/verbose, batch/single, field selection) and rich output, the description covers all major usage paths. It explains batch limits, format constraints, default vs verbose fields, field subsetting, full-text access, and the special semantics of impact_pct. The presence of an output schema relieves it from documenting every return field, so the description is complete for an agent deciding how to call the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all four parameters at 100%, so the baseline is 3. The description adds significant value by enumerating the default 13-field shape, explaining what verbose=true adds, giving field-selection examples, and describing batch limits. However, there is an inconsistency: the description says 'lean 13-field shape' while the input schema says 'lean 12-field default,' which could confuse an agent trying to reason about the actual response shape.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Get full details for one or more papers by arXiv ID.' It clearly distinguishes this tool from siblings like fetch_fulltext by later noting that fetch_fulltext is used for full paper text, while get_paper returns structured metadata and summaries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear guidance for batch vs single-paper use, bibtex vs json, lean vs verbose output, and field selection. It also hints at an alternative (fetch_fulltext) for full paper content, though it does not explicitly state when to prefer search_papers or get_citations. The guidance is strong but not fully exhaustive regarding all sibling alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation4/5

Most tools target a distinct resource and action — search vs. saved-library synthesis vs. citation analysis vs. article metadata — and the descriptions explicitly cross-reference one another to reduce confusion. A few retrieval/analysis tools (get_field_orientation, get_foundational_lineage, get_citations, check_drift) have adjacent purposes and could be misselected without reading their descriptions carefully.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun snake_case pattern: create_watch, delete_watch, list_library, save_paper, annotate_paper, fetch_fulltext, search_papers. Minor deviations like co_author_graph and the interchangeable retrieval verbs (search, find, get, check, ask) create slight inconsistency, but the overall convention is predictable.

Tool Count3/5

27 tools is on the heavy side, but the server covers several coherent subdomains: search/discovery, library/collection management, watches, annotations, and research analysis. The count is justifiable for the broad purpose, though some of the discovery/analysis tools could likely be consolidated or split into a separate server.

Completeness4/5

The tool surface covers the core lifecycle well: search, fetch, save, organize into collections, annotate, watch for new papers, and analyze citations/authors/gaps. Minor gaps exist — there is no collection deletion/rename, no explicit mark-as-read tool, and no unlike operation — but these are workable edge cases rather than blocking omissions.

Resources