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harvest.latest

Find recently-updated Hugging Face datasets and trending GitHub repositories to identify current training data sources.

Instructions

Scan what's trending: recently-updated Hugging Face datasets and hot/recent GitHub repos. Returns HarvestItem[].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gh_tokenNoOptional GitHub token for higher rate limits. Falls back to the GITHUB_TOKEN env var.
hf_tokenNoOptional Hugging Face token for higher rate limits. Falls back to the HF_TOKEN env var.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, but it only states purpose and return type. It does not reveal ordering, result limits, pagination, or the operational meaning of 'hot/recent'. The rate-limit fallback behavior is documented only in the schema parameter descriptions, not in the tool description.

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?

Two compact sentences with zero filler: the action and scope are front-loaded, and the return-type note earns its place given the absence of an output schema. Nothing redundant remains.

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?

The tool is simple, has zero required parameters, and the description covers the core purpose, making it callable with minimal confusion. Yet the description only names HarvestItem[] without defining its structure, and it omits pagination or ordering behavior, leaving some ambiguity about the result contents and size.

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%, and each parameter already carries clear semantics: optional GitHub/Hugging Face tokens with env-var fallback for higher rate limits. The tool description adds no parameter-specific information beyond what the schema provides, so the baseline of 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?

The description uses a specific verb ('Scan') and identifies concrete resources: recently-updated Hugging Face datasets and hot/recent GitHub repos. It also names the return type HarvestItem[], and the 'trending'/'latest' framing clearly distinguishes it from the query-oriented sibling harvest.search.

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 intended use case: when the agent wants trending or recent items rather than targeted searches. However, it never explicitly names alternatives, states when not to use this tool, or explains how it relates to harvest.search, harvest.expert, harvest.compose, or harvest.pack.

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