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Orolol

FactCheck MCP Toolkit

by Orolol

score_check_worthiness

Scores each sentence in a text for check-worthiness using ClaimBuster to identify claims that warrant fact-checking.

Instructions

Score sentences in a text for check-worthiness using ClaimBuster.

Args: text: The text containing one or more sentences to score.

Returns: List of dicts with keys: sentence, score, index. Returns empty list on error or missing API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

There are no annotations, so the description carries the full burden. It discloses the output shape, the empty-list-on-error behavior, and the dependency on an API key, which are useful behavioral traits beyond the basic purpose.

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?

The description is compact, front-loads the main purpose, and uses a clean Args/Returns structure. Every sentence adds useful information, with no filler or redundant elaboration.

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 simple one-parameter tool with an output schema, the description is complete: it states the input, the operation, the return format, and the error behavior. Nothing essential is missing for an agent to invoke it correctly.

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

Parameters5/5

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

Schema coverage is 0% and the schema only says 'text' is a string. The description adds meaningful semantics: 'The text containing one or more sentences to score.' For the single parameter, this fully compensates for the lack of schema-level documentation.

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 opens with a specific verb and resource: 'Score sentences in a text for check-worthiness using ClaimBuster.' It clearly identifies what the tool does and is easily distinguishable from the sibling data-search and retrieval tools.

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 its usage context—when sentence-level check-worthiness scores are needed—and gives an input constraint ('text containing one or more sentences'), but it never explicitly states when to prefer this tool over alternatives or when not to use it. The usage is clear but left implicit.

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