A tool can accept an input, finish without an exception, and return a convincing explanation while leaving the most important scientific question unresolved: what exactly was predicted, using which materials and method?

Paper2Agent makes that boundary worth examining. The authors connect research papers and their original code to callable tools, tests and natural-language use. Their public TISSUE example names three input files and requests a 95% prediction interval for Acta2. That is a concrete task with a reviewable chain, rather than an invitation to ask an unconstrained chatbot for a biological conclusion.

This article explains that example and gives a small metadata checker you can run locally. It does not install Paper2Agent, generate an MCP server, read research matrices, train a model or reproduce the authors’ result. The Python example checks our own teaching contract, not the implementation or validity of TISSUE.

Begin with the materials, not the prose

The repository’s example uses Spatial_count.txt, Locations.txt and scRNA_count.txt. Those names alone do not establish that a new dataset is suitable. A file can have the expected extension while using a different gene naming system, an incompatible orientation or locations that no longer match the count matrix.

An adequate handoff therefore includes the role of each file, its provenance and permission, the relevant units or representation, and how identifiers line up. Preserve the exact filenames from an author example when explaining it. For your own work, preserve the actual file references; do not rename unrelated files to make an interface appear compatible.

The target also needs two parts. Acta2 identifies a gene in this example. It does not, by itself, identify every quantity that a method could predict about that gene. The requested prediction target and its unit or scale still need an explicit definition that matches the original method.

Separate a contract check from an analysis

Here is an intentionally incomplete, author-created teaching contract. The paper date and repository link identify the source we are discussing. The missing revision and prediction target represent unresolved review work; they are not values we can infer from a nice-looking answer.

contract = { "repository": "https://github.com/jmiao24/Paper2Agent", "repository_revision": None, "inputs": { "spatial_counts": "Spatial_count.txt", "spatial_locations": "Locations.txt", "single_cell_reference": "scRNA_count.txt", }, "gene": "Acta2", "prediction_target": None, "interval": {"kind": "prediction", "level": 0.95}, } def review_gaps(c): gaps = [] for key in ("repository", "repository_revision", "gene", "prediction_target"): value = c.get(key) if not isinstance(value, str) or not value.strip(): gaps.append(f"Missing reviewed metadata: {key}") inputs = c.get("inputs", {}) for role in ("spatial_counts", "spatial_locations", "single_cell_reference"): value = inputs.get(role) if not isinstance(value, str) or not value.strip(): gaps.append(f"Missing input reference: {role}") interval = c.get("interval", {}) level = interval.get("level") if interval.get("kind") != "prediction": gaps.append("Confirm interval kind against the source method") if isinstance(level, bool) or not isinstance(level, (int, float)) or not 0 < level < 1: gaps.append("Interval level must be a number strictly between 0 and 1") return gaps for gap in review_gaps(contract): print(gap)

For this exact dictionary, the output is:

Missing reviewed metadata: repository_revision Missing reviewed metadata: prediction_target

I checked this exact standard-library-only example locally for the article. No scientific tool or external service is called. The checker reports missing metadata; it does not validate file contents, data permission, a commit’s correctness, software dependencies or scientific assumptions. Even an empty list of gaps would mean only that these selected fields passed this small structural check.

A wrapper should preserve the chain

The next step is not to ask an AI to fill the two blanks with plausible text. Retrieve the actual repository revision used for the work and locate the prediction target in the source method and configuration. If that information is unavailable, record the limitation and retain the incomplete state.

For a real execution, retain the actual command or tool call, its arguments, the software environment and input references, and the original output files. Keep an explanation as an additional interpretation layer. A rewritten narrative should not become the only surviving record of what the method returned.

The same principle applies to tests. Record what the test supplied, what it asserted and which path it covered. A successful example can establish that a particular interface path ran under stated conditions. It cannot establish correctness for every dataset, every parameter choice or every downstream biological interpretation. Do not relabel interface success as experimental replication.

Preserve the meaning of the interval

For the TISSUE request, retain the interval type, level and specified prediction target when describing the output. A prediction interval concerns a prediction under the method’s setup and assumptions. The phrase “95%” does not transform it into a 95% probability that the gene causes a biological effect.

Do not invent interval bounds to complete a presentation, or substitute an uncertainty illustration for a returned numerical result. If no analysis was performed, a diagram can show the names of expected fields while stating that it contains no computed result. If a result exists, interpret it using the relevant method, data and validation evidence rather than the confidence of a generated explanation.

Practical handoff

Before relying on a paper-derived tool call, assemble the source paper and code revision, authorized input references and alignment notes, target and parameters, covered tests, original output, and interpretation limits. That package makes disagreements actionable: a reviewer can locate a missing dependency, a mismatched identifier or an unsupported claim without guessing which version produced the answer.

We are the RigorMed team. This is a developer-focused method note about public author materials, not a claim that we ran the example or integrated this particular project into our product. Open-source code also does not settle model/API costs, dependency availability or data permissions.

Sources read for the underlying example: the Nature paper, published September 16, 2026, and the authors’ repository, reviewed November 2, 2026 for the original video materials. This is an explanation of an existing publication, not a claim of a new release today.