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Rishab-Ghosh

Reviewer Zero

by Rishab-Ghosh

review_paper

Run a full pre-submission review of a paper PDF: verify claims, prior work, citations, methodology, and writing. Returns evidence-backed findings with quotes, no scores.

Instructions

Run the full Reviewer Zero review of a paper PDF (claims, prior work, citations with support checks, methodology, writing, format) with our prompts, using the user's own ANTHROPIC_API_KEY. It costs real money (usually about $1.50 to $2.00) and takes 7 to 12 minutes.

ALWAYS call it first with confirm=false: that spends nothing and returns the estimate and the hard limit. Show both to the user and call again with confirm=true only after they agree. If a call times out, call again with the same PDF: finished steps are served from the cache and only what is left is paid for.

novelty="full" (default) sends the 150 best candidates per claim to the reranker; novelty="lite" sends 60 (quicker and cheaper, lower recall; see the README for the measured recall and time of each).

Report findings evidence-first, each with its quote. Never give a score, rating or accept/reject verdict, and never rewrite the user's text. Without ANTHROPIC_API_KEY or a local GROBID container it explains what is missing; check_format, check_citations, find_prior_work, paper and citations work without a key.

Sent off your machine by this tool: the paper's text to Anthropic under your own key; search queries and cited works' titles/DOIs/arXiv ids to the index, OpenAlex, Crossref and arXiv.

Privacy: runs on your machine. Your PDF and its text never leave it, except to Anthropic under your own API key when you call review_paper. What is sent: search queries and paper keys to the Reviewer Zero index (it counts requests per API key and stores nothing else), and, for check_citations, the titles, DOIs and arXiv ids of the works the paper cites, to the index and to OpenAlex, Crossref and arXiv. No telemetry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueYes
confirmNo
noveltyNofull
pdf_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
phaseYes'plan' (confirm=false): nothing spent; show the estimate and ask the user. 'review': the full run.
stepsNo
formatYes
findingsNo
limit_usdYesHard limit: the run stops calling the model before spending more than this.
spent_usdNo
meta_reviewNoThe allowlisted meta-review (no number anywhere; every sentence passed the D12 leak filter). None when the meta step did not run or failed.
estimate_usdYesAbout this much (sum of per-step medians for the steps not yet done; D5).
novelty_modeNofull: 150 candidates per claim go to the LLM reranker; lite: 60 (quicker, cheaper).full
steps_to_runYes
steps_already_doneYesFinished in an earlier call on the same PDF; rerunning them is served from the cache.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations at all, the description carries the full burden and does so richly: concrete cost ($1.50-$2.00), runtime (7-12 min), that confirm=false spends nothing, cache-served idempotent retries, prerequisite credentials (ANTHROPIC_API_KEY, local GROBID container), and exactly what data leaves the machine and to whom. It also constrains output behavior (evidence-first, no scores or accept/reject verdicts, never rewrite user text).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, cost and the confirm workflow, which is the right order. However the 'Sent off your machine by this tool' paragraph and the following 'Privacy' paragraph restate the same egress facts (search queries and cited works' titles/DOIs/arXiv ids to the index, OpenAlex, Crossref and arXiv), which is redundant bulk in an already long description.

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?

An output schema exists, so return values need not be described, and the description still covers cost/limit transparency, the two-step confirmation flow, credential and container prerequisites, caching, privacy/egress, and the reporting style. For a long-running, paid, multi-step tool this is complete enough to invoke correctly.

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 description coverage is 0%, so the description must compensate. It explains confirm (dry-run that returns estimate and hard limit), novelty semantics with concrete candidate counts and the recall/time tradeoff, and implicitly the pdf_path reuse rule on retries. Only venue is left unexplained, but its enum of venue names is self-explanatory.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Run the full Reviewer Zero review of a paper PDF') and enumerates the covered dimensions (claims, prior work, citations, methodology, writing, format), so it is clearly the comprehensive sibling of check_format/check_citations/find_prior_work. It never explicitly says 'use the other tools for a cheaper subset', so the differentiation is implied through the word 'full' rather than spelled out.

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?

Gives an explicit mandatory workflow: 'ALWAYS call it first with confirm=false', show the estimate and hard limit to the user, then call again with confirm=true only after agreement. Also states the timeout retry policy and the tradeoff between novelty='full' (150 candidates, default) and novelty='lite' (60, quicker/cheaper, lower recall), which is exactly the when-to-use guidance an agent needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.