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Marcwarn

doings-evidence-mcp

by Marcwarn

classify_claims

Extract and classify claims from text into causal, normative, diagnostic, descriptive, or prescriptive categories for evidence assessment.

Instructions

Extracts and classifies claims from text as causal, normative, diagnostic, descriptive or prescriptive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
claimTypesNo
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the action without specifying behaviors like return format, handling of long text, or side effects. This is minimal transparency.

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?

Single sentence front-loaded with the core verb and resource. No redundancies or fluff. Every word contributes to purpose.

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

Completeness2/5

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

Given the lack of output schema and zero parameter descriptions, the tool description should explain return values and usage details. It does not, leaving significant gaps for the agent.

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

Parameters2/5

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

Schema description coverage is 0% and the tool description does not add meaning beyond the schema. The 'claimTypes' parameter is mentioned in the output but not explained as an input filter. No details on 'text' format or constraints.

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 clearly states the tool extracts and classifies claims from text into five specific types: causal, normative, diagnostic, descriptive, or prescriptive. This verb+resource+categories structure is unambiguous and distinguishes it from siblings like 'critique_claim'.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives (e.g., 'critique_claim' or 'detect_solution_first_thinking'). The agent must infer from the name alone. No exclusions or prerequisites mentioned.

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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