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Klarix Intelligence Engine

Peer-reviewed and preprint evidence (Semantic Scholar + arXiv)

search_scientific_evidence
Read-onlyIdempotent

Retrieve verifiable academic and engineering literature for a technical topic so a claim can cite a resolvable DOI instead of a vendor blog post. Queries Semantic Scholar (citation graph, abstracts, open-access PDFs) and the arXiv preprint index, merges duplicates, and ranks by citation count then recency. Returns title, authors, year, DOI / arXiv URL, citation count, and an extractive key finding taken verbatim from the abstract. Keyless; set SEMANTIC_SCHOLAR_API_KEY to escape the shared anonymous rate limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTechnical topic phrase, e.g. "battery cathode thermal runaway" or "vector database latency". Noun phrases beat questions.
sourceNoWhich index to query. Default "all" merges Semantic Scholar, arXiv, and PubMed (clinical/biotech) and dedupes by DOI / PMID / arXiv id / title.
year_fromNoPublication-year floor. Papers older than this, and papers with no known year, are dropped.
max_resultsNoPapers to return. Default 5.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
papersYes
sourceYes
paper_countYes
provider_notesYes
providers_queriedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint; the description adds substantial behavioral detail beyond that: it queries multiple indexes, merges duplicates, ranks by citation count then recency, returns specific fields including a verbatim abstract extraction, and discloses the keyless rate-limit escape hatch. No contradiction exists between description and annotations.

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

Conciseness5/5

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

Four sentences, each earning its place: purpose, sources and ranking, return payload, and rate-limit note. The most decision-relevant information is front-loaded, and there is no redundant restatement of title or schema.

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?

For a tool with four parameters and an output schema, the description covers inputs, source behavior, ranking, deduplication, return fields, and operational constraints (keyless rate limit). An agent has everything needed to invoke it correctly without inferring hidden behavior.

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 description coverage is 100%, so the schema already documents all four parameters. The description adds context about default merging behavior and the ranking order, but it does not significantly elaborate on parameter formats beyond what the schema already provides, so the baseline 3 is appropriate.

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 verb and resource: 'Retrieve verifiable academic and engineering literature' from Semantic Scholar and arXiv. The purpose is anchored to a concrete outcome ('so a claim can cite a resolvable DOI instead of a vendor blog post'), and the described scope clearly differentiates it from the sales-oriented sibling tools.

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

Usage Guidelines4/5

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

The description gives clear context: use when a technical claim needs a verifiable academic citation rather than a vendor source. It does not explicitly name alternatives or exclusion conditions among siblings like get_deep_research, but the intended scenario is stated plainly enough to guide selection.

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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