Landscape

Field Landscape

Generate a landscape overview of a research field or topic.

Input

The user provides either:

• An arXiv category code (e.g., cs.LG, q-bio.BM, astro-ph.CO) — recognized by the pattern of a short prefix, a dot, and a short suffix
• Free text describing a research area (e.g., "protein folding", "large language models")

Tool Chain

Use the Valency MCP tools — they come from the valency MCP server registered by the companion Valency connector extension. If no valency server is registered, tell the user to install the connector and run /mcp auth valency. Stop here.

Step 1: Find papers in the field

If the input matches an arXiv category pattern (e.g., cs.LG):

Call search_by_category with:

• category (string): the category code
• limit (integer): 10
• sort_by (string): "citations"

If the input is free text:

Call semantic_search_papers with:

• query (string): the user's input
• limit (integer): 10
• sort_by (string): "citations"

If no results are found, tell the user and suggest trying different terms or checking category codes with a broader search. Stop here.

Step 2: Get publication trends

If the input is a category code:

Call get_publication_trends with:

• category (string): the category code
• granularity (string): "year"
• format (string): "compact"

If the input is free text:

Call get_keyword_trends with:

• query (string): the user's input
• granularity (string): "year"
• format (string): "compact"

Step 3: Identify top authors

Call identify_prolific_authors with:

• category (string): the category code (if input was a category), otherwise omit
• limit (integer): 10

Note: if the input was free text and no category was identified, skip this step and note that top-author ranking requires a category code.

This tool can time out for very large categories (e.g., cs.LG). If it times out, skip this section in the output and note that author ranking was unavailable due to the size of the category.

Step 4: Identify subdomains

Call identify_research_domains with:

• limit (integer): 10

Note: this returns corpus-wide domain rankings. If the input was a category, the results contextualize where this category sits within the broader landscape.

Step 5: Get corpus metrics

Call analyze_corpus_metrics with:

• category (string): the category code (if input was a category), otherwise omit

Output Format

Field Summary

A brief paragraph covering:

• Total papers found (from Step 5 or Step 1 result count)
• Date range of publications
• Growth trajectory (from Step 2 — is volume increasing, stable, or declining?)

Publication Trends

A year-by-year table from Step 2:

Top Authors

A numbered list of the top 10 authors from Step 3 with their paper counts.

Subdomain Breakdown

A table of the top research domains from Step 4, giving context for how this field relates to the broader corpus.

Notable Recent Papers

List 5 papers from Step 1 (the most-cited papers found). For each:

• Title (with paper ID)
• Authors (first 3, then "et al." if more)
• Year
• Category

Observations

2-3 brief observations drawn from the data. Examples:

• "Publication volume has doubled since 2021"
• "The field is concentrated in 2-3 subdomain categories"
• "Author X dominates with 3x more papers than the next most prolific"

Suggested Follow-ups

• /valency:profile <author> — for any top author listed
• /valency:trends <category> — for deeper trend analysis
• /valency:similar <paper_id> — for any notable paper listed