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michalhron

Scopus Plus MCP

by michalhron
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Python
Remote

research_fronts

Detect research fronts in a paper set: cluster its citation network into communities, extract core papers, years and keywords, and map the main path across fronts.

Instructions

Research fronts of a paper set: Louvain communities of its direct-citation network (as in CitNetExplorer), each described by its years, density, core papers (most cited within the set) and the keywords that distinguish it. Also reports which front each paper of the global main path belongs to and where the path hops from one front to another: a main path that stays in one front traces a single conversation, one that hops stitches several together. Give ids or a query. Writes JSON and Pajek .net plus .clu (partition) files. Cost: one reference and one abstract request per paper (cached).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsNoThe papers (Scopus IDs/EIDs; with source='openalex', DOIs or OpenAlex IDs). Use this or query.
queryNoSearch query defining the set, instead of ids.
scopeNoRestrict to journals, by ISSN: a list of ISSNs, or a basket name ('basket_of_eight' / 'ais8': the AIS Senior Scholars' Basket of Eight). ISSNs are used rather than journal names, which Scopus spells inconsistently.
sourceNoData source. 'scopus' (default) needs subscriber entitlement for search, citations and references. 'openalex' needs none: IDs may be DOIs, OpenAlex work IDs (W...), or Scopus IDs (resolved to a DOI via Scopus metadata), and results carry OpenAlex IDs. Never mix sources within one analysis.scopus
min_sizeNoSmallest front reported; smaller groups count as unclustered (default 3).
resolutionNoLouvain resolution: above 1 gives more, smaller fronts (default 1).
corpus_fileNoA corpus file from import_records, instead of ids or query.
max_resultsNoWith query: how many papers to include (default 300).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses side effects (writes JSON, Pajek .net and .clu partition files) and a cost model (one reference and one abstract request per paper, cached). It omits failure modes, permissions specifics beyond the schema, and runtime/large-set caveats.

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?

A dense but well front-loaded paragraph: the core operation leads, interpretation and cost follow. Every sentence carries information, though the main-path elaboration is lengthy relative to its decision value for an agent.

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

Completeness4/5

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

For an 8-parameter, no-output-schema tool, the description usefully summarizes the returns (fronts described by years, density, core papers, distinguishing keywords; per-paper front membership; output files). What an agent needs to invoke it correctly is largely covered, though scope/source interaction is left to the schema.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents ids, query, scope, source, min_size, resolution, corpus_file and max_results. The description adds only broad framing ('Give ids or a query') and no extra syntax, defaults, or interactions beyond what the schema provides, so baseline 3 applies.

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?

States a specific verb and resource: computing Louvain communities of a paper set's direct-citation network, plus main-path front membership. This is clearly distinguishable from siblings like citation_network, thematic_evolution and path_transmission without opening any schema.

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?

Gives concrete input options ('Give ids or a query') and explains the analytical interpretation (a path staying in one front vs hopping). However, it never states when to prefer this over adjacent tools such as citation_network or path_transmission, so the routing guidance stops short of explicit 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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