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jmp-docs-mcp

jmp-docs-mcp

by nepseli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Find Example

find_example

Find worked examples in JMP documentation, optionally scoped to a sample data table.

Instructions

Find worked examples, optionally restricted to a sample data table.

JMP users learn from the built-in sample data, so this searches the ~630 'Example of...' pages specifically.

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": {
      "topic": "<topic>"
    },
    "name": "find_example"
  }
}

Input Schema

NameRequiredDescriptionDefault
kNoMax examples.
topicYesTopic or JMP platform to find examples for.
datasetNoOptional sample data table, e.g. 'Big Class.jmp'.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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 the search scope (~630 pages) and optional dataset restriction, but doesn't mention behavior like result ordering, pagination, or what happens when no examples are found. The description adds some context beyond the schema but not deep behavioral detail.

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 two sentences with no wasted words. The core purpose is front-loaded, and the second sentence provides valuable context about the search scope. Every sentence earns its place.

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?

The tool has an output schema, so return values are covered elsewhere. The description explains the search scope and optional restriction, which is sufficient for an agent to invoke it correctly. It could mention result limits or error behavior, but the output schema and parameter defaults cover most operational needs.

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 description coverage is 100%, so the schema already documents all three parameters. The description adds context about the 'dataset' parameter (restricting to a sample data table) and the 'topic' parameter (topic or JMP platform), but doesn't add syntax or format details beyond the schema. Baseline 3 is appropriate.

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's purpose: finding worked examples, optionally restricted to a sample data table. It also distinguishes itself from generic documentation search by specifying it searches the ~630 'Example of...' pages, which differentiates it from siblings like search_jmp_docs and get_jmp_page.

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 the context ('JMP users learn from the built-in sample data') and implies this tool is for finding examples rather than general docs. It doesn't explicitly name alternatives or say when not to use it, but the scope is clear enough for an agent to select it appropriately among siblings.

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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