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mcp-server-sensory

mcp-server-sensory

by jaspertvdm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

braille_punchcard

Generate ASCII punchcard patterns from text for physical punching, creating a tangible audit trail.

Instructions

Generate ASCII punchcard pattern from text - can be physically punched for audit trail!

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": {
      "text": "<text>"
    },
    "name": "braille_punchcard"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText to convert
cell_widthNoWidth of each cell
cell_heightNoHeight of each cell

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only states the output is a pattern for physical punching, lacking details on side effects, authorization needs, or rate limits. No contradiction with annotations since none exist.

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 that is front-loaded with the core action and resource, followed by a brief value add. No superfluous words.

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?

Despite high schema coverage, the lack of output schema means the description should clarify the return format. It does not specify whether the output is a string, array, or other, leaving ambiguity for an AI agent.

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 coverage is 100%, so baseline of 3 applies. The description adds no additional meaning beyond the schema's descriptions for text, cell_width, and cell_height.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it generates an ASCII punchcard pattern from text, distinguishing it from siblings like braille_binary_grid or braille_encode which likely produce different outputs. The mention of physical punching adds specificity.

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 explicit guidance on when to use this tool vs alternatives. The note about physical punching hints at a use case but does not provide clear directives or 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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