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Research reading lists

research

Read-onlyIdempotent

CALL THIS FIRST for a literature question about chemistry, materials or physics research -- a broad one ('what matters in X') or a specific process or parameter one (a deposition condition, a property value, a defect mechanism). Send the question in plain words (q), or a paper you already know (paper: DOI or title). Returns reading lists in four lenses: relevant (the papers whose TITLE AND ABSTRACT best match the question, across the whole corpus, BM25 -- start here for a specific question), foundational (the classics the question's own papers cite most), now (highest impact, young papers' citations projected) and newest (last two years); plus the research communities and element sub-communities the question is about, with why each matched and whether it is rising. Put the question's key terms in q: filler words are ignored. unmatched_terms lists words that matched nothing; rephrase those. Each paper carries its DOI, a one-line reason and its abstract where one is on file. Our corpus is curated, not complete: the relevant lens also brings up to 3 of every 10 results from OpenAlex, marked source='openalex' and labelled not yet curated. One page = up to 10 papers per lens; ask for page 2+ only if needed. Requires a Personal+ account. Without a key, pay per call: $0.05 in USDC on Base or Solana via x402 (send the payment in _meta["x402/payment"]).

Input Schema

Name	Required	Description
`q`	No	The research question, in plain words.
`page`	No
`paper`	No	Alternative to q: a DOI or title to start from.
`lenses`	No	Default: all four.
`page_size`	No	Papers per lens.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 16, 2026

changedInput schema / properties / lenses / description

Previous value: -"Default: all three."New value: +"Default: all four."

changedInput schema / properties / lenses / items / enum

Previous value: -[
-  "foundational",
-  "now",
-  "newest"
-]New value: +[
+  "relevant",
+  "foundational",
+  "now",
+  "newest"
+]

AddedOct 11, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover read-only/idempotent/open-world, but the description adds substantial context the annotations cannot: the corpus is curated but incomplete, the relevant lens blends up to 3 of every 10 results from OpenAlex marked source='openalex' and labelled not-yet-curated, and the access model (Personal+ account, or $0.05 USDC per call via x402 with payment in _meta['x402/payment']).

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?

Front-loaded with the imperative 'CALL THIS FIRST' and the question types, and nearly every clause carries operational information (lens behavior, corpus limits, payment). It is a single very dense paragraph, though, and the lens definitions plus payment mechanics make it heavier than ideal for skimming.

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?

There is no output schema, yet the description fully covers return shape: papers with DOI, one-line reason and abstract, four lenses with their semantics, matched communities with rising/falling status, unmatched_terms, source labeling for OpenAlex results, and page/per-lens limits. Nothing an agent needs to call or interpret it correctly is missing.

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 80%, so the baseline is 3, but the description adds real meaning: q ignores filler words and unmatched terms are surfaced for rephrasing, paper accepts a DOI or title as an alternative to q, each lens is defined (BM25 title/abstract match, foundational citations, projected impact, last two years), and page_size maps to 'up to 10 papers per lens'. The page parameter itself is left only to schema bounds.

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 action (returns reading lists) for a specific domain (chemistry, materials, physics literature questions) and enumerates what comes back: four lenses plus communities. It clearly differs from the sibling lookup tools (get_subject_papers, get_element_papers, get_subject_history), which are entity-scoped rather than question-driven.

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

Usage Guidelines5/5

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

Explicitly says 'CALL THIS FIRST', distinguishes broad questions ('what matters in X') from specific process/parameter questions, names the alternative input path (q vs paper: DOI or title), and gives a reasoning hint on lens order ('start here for a specific question' on relevant) and pagination ('ask for page 2+ only if needed').

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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