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paperclip-mcp

paperclip-mcp

by shanewiseman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get a decision training example

pc_get_decision_training_by_id
Read-onlyIdempotent

Retrieve a decision training example by its ID to review or reuse training data.

Instructions

Get a decision training example

Paperclip operation: GET /api/decision-training/{id}. Authorization class: board_or_agent.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "pc_get_decision_training_by_id"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes

Output Schema

NameRequiredDescriptionDefault
bodyYes
headersYes
encodingYes
status_codeYes
content_typeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already provide safety hints (readOnly, idempotent, non-destructive). The description adds the authorization class and exact API endpoint, but does not elaborate on response behavior beyond what the output schema covers.

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?

The description is short (two sentences) but the first sentence repeats the title verbatim. It is front-loaded with the tool's purpose, earning a high score despite minor redundancy.

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 description is adequate for a simple read tool with annotations and an output schema, but the lack of parameter explanation and usage guidance limits completeness.

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

Parameters1/5

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

Schema coverage is 0%, and the description does not explain the purpose of the path.id parameter, how to obtain it, or any constraints on its value.

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 'Get a decision training example' uses a clear verb and resource, distinguishing it from sibling tools like pc_delete_decision_training_by_id and pc_post_companies_by_company_id_decision_training.

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?

The description provides the HTTP method, URL path, and authorization class but offers no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions.

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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