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ByteTrail

Latest Papers

latest_papers
Read-onlyIdempotent

New AI papers from arXiv, newest first or by ByteTrail's relevance score, with excerpt, authors and links.

Input Schema

NameRequiredDescriptionDefault
daysNo
sortNonew
fieldNoa ByteTrail field slug such as 'agents', 'rag', 'code-models' (see trending kind='fields')
limitNo
categoryNoan arXiv category such as 'cs.CL'
has_codeNoonly papers a GitHub repo implements

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds the ordering semantics ('newest first or by ByteTrail's relevance score') and what each result contains, but says nothing about default windowing, result caps, or rate/auth considerations. With an output schema present, the return-value detail is largely redundant.

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?

A single front-loaded sentence naming source, ordering, and payload with no filler. It is dense but every clause carries information; slightly more structure around the two sort modes would help.

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

Completeness3/5

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

For a filter-list tool with an output schema and full annotation coverage, the definition is adequate but thin: it omits how the time window defaults behave, what 'relevance' is scored on, and when to prefer this over search or trending. Those gaps are small individually but leave the agent guessing on routing.

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 50% and the schema already documents field, category, and has_code. The description only clarifies the sort parameter's two meanings and hints at recency, leaving days, limit, and the ByteTrail field slug concept unelaborated beyond the schema.

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+resource ('New AI papers from arXiv') plus the returned payload (excerpt, authors, links) and the two sort modes. It reads clearly as a listing tool, implicitly distinct from the singular get_paper sibling, though it never names that alternative.

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?

Usage is only implied: 'new AI papers' suggests browsing recent items, while get_paper, search, or trending would cover other intents. No when-to-use or when-not-to-use guidance is given, nor any mention of prerequisites such as a field slug source.

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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