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Replication Radar

Replication Radar

by ScienceLiveHub
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

replication_status

Check whether a DOI has been independently replicated and how strongly the verdicts agree, returning Science Live outcomes, CiTO relations, repositories, and replicated claims.

Instructions

Has this DOI been independently replicated, and did it hold? Returns every Science Live verdict — pulled LIVE from the nanopub network, any signer (author-agnostic) — with the validation status, CiTO relation, the replication's repository, and links to the signed Outcome/CiTO nanopubs. 'open' if not replicated. This is the reliability signal the OpenAIRE Graph structurally cannot hold.

Also returns:

  • agreement: how the independent verdicts agree — pattern is one of robustly_validated / validated / qualified (partial support only) / inconclusive / contested / refuted, with confirm/partial/contradicted counts (so you can say how robustly it held, not just that it did). Never "validated" without a confirmation.

  • claims: the exact FORRT claim(s) that were replicated — each an atomic AIDA statement plus its claim type (descriptive pattern, statistical significance, …).

  • the paper's title and abstract (from OpenAIRE, markup stripped), so you can read it or extract/compare the atomic claim yourself.

Input Schema

NameRequiredDescriptionDefault
doiYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden well: it discloses the data source (live nanopub network), signer policy (author-agnostic, any signer), the 'open' fallback semantics, and the exact shape of returned verdicts and agreement patterns. It omits only operational traits like rate limits or error behavior.

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 core question and answer shape, then structured bullet lists for secondary fields. Mostly earns its space, though the 'reliability signal the OpenAIRE Graph structurally cannot hold' line is promotional filler rather than actionable detail.

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

Completeness4/5

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

No output schema exists, so the description appropriately documents the return payload (validation status, CiTO relation, repository, agreement pattern, claims, title/abstract). Only gaps are DOI input format and failure modes for malformed or unknown DOIs.

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

Parameters3/5

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

Schema coverage is 0% for the single `doi` parameter, and the description only implies it is the paper's DOI without specifying format (bare DOI vs. URL) or whether resolution is required. It does add meaning via the 'open' return semantics for an unresolvable/unreplicated DOI, but the format gap keeps this at a minimum-viable level.

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?

Opens with a precise question stating verb and resource ('Has this DOI been independently replicated, and did it hold?') and immediately scopes it to Science Live replication verdicts. This is clearly distinguishable from siblings like verified_claims or find_dataset without needing their schemas.

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 a lookup-by-DOI task and notes the 'open' result for unreplicated papers, but it never states when to prefer this tool over siblings (e.g., verified_claims) or any prerequisite conditions. Usage is inferable rather than explicit.

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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