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SujayGG

pf-debate-mcp

by SujayGG
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_card

Retrieve a debate evidence card by its ID, with a compact reading view (tag, cite, highlighted text) or the full body to review context and find indicts.

Instructions

Card by id (lib:N or cN). view="read" (default, compact): tag, cite, and only the underlined/highlighted text (==highlighted== is read aloud, underlined is context, ... marks skipped text). view="full": the whole body, needed to judge context, find indicts in unhighlighted text, or recut.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNoread
card_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.3.0
    • addedInput schema / properties / view
      Added value: +{
+  "default": "read",
+  "title": "View",
+  "type": "string"
+}
  2. First observedv0.2.1

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that 'read' view omits text (skipped marks), 'full' returns the whole body, and highlights how highlighted/underlined text is represented. This is meaningful behavioral detail beyond the schema.

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 and front-loaded with the core purpose, then explains the two views efficiently. Every sentence adds value, and the formatting with view labels is scannable.

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

Completeness4/5

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

Given the tool's simplicity (2 params, no enums) and the presence of an output schema, the description covers the key decision (which view to use) and the identifier format. It doesn't mention error cases or return structure, but the output schema likely covers that, and the description is sufficient for correct invocation.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It explains the 'view' parameter's two values and their effects, and implies card_id format (lib:N or cN). It doesn't detail the exact string format for card_id beyond examples, but the core semantics are covered.

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 retrieves a card by ID and distinguishes two views: 'read' (compact) and 'full' (whole body). It names the resource (card) and the identifier format (lib:N or cN), making the purpose specific and actionable.

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 explains when to use each view: 'read' for compact reading, 'full' for judging context, finding indicts in unhighlighted text, or recutting. It doesn't explicitly name alternatives among siblings, but the view guidance provides clear context for usage.

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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