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FlynnLachendro

methods-mcp

methods_repro_review

Resolve a paper, extract methods, locate its code repository, and assess reproducibility in a single call. Returns a structured review with plain-English narrative and actionable errors.

Instructions

Resolve a paper + extract methods + find its code repo + assess reproducibility — in one call.

Returns a MethodsReproReview object. Read narrative first — it's a plain-English summary of everything retrieved, with every numeric score echoed in context (e.g. "moderate confidence (0.72)"). Then:

  • status: "ok" (all sub-steps succeeded), "partial" (some failed — see errors), or "empty" (couldn't even resolve the input paper).

  • metadata, methods, code_repo, repro_assessment: the structured sub-results. Any step that failed contributes null for its key; null means unavailable, not zero.

  • errors: list of {step, error_type, message, hint} — one entry per failed sub-step, with an actionable hint where the failure pattern is recognised (e.g. missing ANTHROPIC_API_KEY, rate-limit, 404, timeout).

For the meaning of numeric scores and verdict buckets, see the "Scores & verdicts explained" section of the README.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
input_strYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorsNo
statusYesTop-level status of a composite-tool call. `ok` — every sub-step succeeded. `partial` — at least one sub-step failed (see `errors`) but we have usable results. `empty` — we couldn't even resolve the paper input; no sub-steps ran.
methodsNo
metadataNo
code_repoNo
narrativeYes
repro_assessmentNo
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavior, and it does so well. It explains the possible status values (ok/partial/empty), the semantics of null keys (unavailable, not zero), and the structure of errors including hints. It also tells the agent to read 'narrative' first and mentions failure patterns like missing ANTHROPIC_API_KEY, rate-limit, and timeouts. It does not explicitly state read-only semantics or potential side effects, but the error hints imply network calls; overall it is transparent for a composite read tool.

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?

The description is reasonably concise for the complexity, with the core purpose front-loaded in the first sentence and the return structure laid out in a clear bullet list. It avoids redundancy and uses formatting to separate fields. It references the README for score meanings, which is a minor downside but acceptable given the length. Overall, it is well-structured and not bloated.

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

Completeness2/5

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

Despite the complexity (composite of four steps), the description omits critical details needed to call the tool: the input format for input_str and the meaning of model. It also defers score/verdict details to the README, which is a gap because the agent receives no inline explanation. With no annotations and no parameter descriptions, the description is not self-sufficient for correct invocation, leaving the agent with unanswered questions about required inputs.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain both parameters. It only hints that input_str is a paper identifier ('Resolve a paper') but never specifies the format (DOI, arXiv ID, URL, or free text). The model parameter is completely unmentioned, leaving the agent clueless about its purpose or allowed values. This is a significant gap that forces the agent to guess or consult external docs.

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?

The description clearly states a specific verb and resource: it resolves a paper, extracts methods, finds a code repo, and assesses reproducibility in one call. It enumerates the sub-steps explicitly, which distinguishes it from the single-purpose sibling tools (e.g., extract_methods, find_code_repo) and makes its combined scope immediately obvious. The phrase 'in one call' reinforces its composite nature.

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

Usage Guidelines3/5

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

The description implies the tool is a convenience for getting all four results at once, but it does not explicitly state when to use it versus calling individual siblings, nor does it mention any exclusions or prerequisites. It says 'in one call' but does not say 'use this when you need all steps' or 'if you only need X, use Y'. This leaves the agent to infer the use case without explicit routing guidance.

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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