Skip to main content
Glama

Scholar Feed

by io.github.YGao2005

Fetch Full Text

fetch_fulltext
Read-only

Extract paper content from an arXiv paper's LaTeX source, falling back to PDF text. Two modes: 'results' (default) returns ~800 chars of results/experiments + up to 3 table captions — lean, ideal for checking a reported number. 'all' returns full paper sections (abstract, introduction, related work, method, results, conclusion) at up to 3000 chars each + 5 table captions, ~15KB, so prefer 'results' unless you need the whole paper. Content is available for ~95% of arXiv papers; a 404 means neither LaTeX nor PDF extraction yielded text. May take a few seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
arxiv_idYesarXiv ID of the paper
sectionsNo'results' (default): lean ~800-char results/experiments excerpt + table captions. 'all': full paper (abstract, intro, method, results, conclusion, related work) — much larger payload.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoWhere the text came from (e.g. arxiv).
arxiv_idNo
sectionsNoPer-section text (sections='all').
results_textNoResults/experiments excerpt (default 'results' mode).
table_captionsNo

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With readOnlyHint and destructiveHint already covering safety, the description adds substantial behavioral detail: LaTeX-to-PDF fallback, mode-specific payload sizes, ~95% availability, 404 semantics, and latency. This goes well beyond the annotations and helps the agent anticipate outcomes.

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

Conciseness5/5

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

The description is dense but every sentence earns its place: behavior, mode comparison, size estimates, availability, and failure semantics. It is front-loaded with the core extraction behavior and uses structure to separate the two modes clearly.

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 two-parameter read-only tool with an output schema, the description covers what the tool does, how the modes differ, what the fallback behavior is, failure meaning, and expected latency. Nothing critical is missing for an agent to invoke it correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics for the 'sections' parameter, including default behavior, approximate character counts, table captions, and the 'ideal for checking a reported number' use case, which is more than the schema alone provides.

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

Purpose4/5

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

The description clearly states the tool extracts paper content from arXiv LaTeX source with PDF fallback, which is specific and actionable. However, it does not explicitly differentiate from sibling tools like get_paper, leaving some inference to the agent.

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 explicit mode-selection guidance, telling the agent to prefer 'results' unless the whole paper is needed. It does not address when to choose this tool over sibling alternatives such as get_paper or search_papers, so it is clear but not fully exclusive.

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

All Scholar Feed toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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