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Equinox MCP Server

Equinox MCP Server

by s27183
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get-documentation

Retrieve Equinox documentation sections by name or path to access API references, modules, and patterns for development guidance.

Instructions

Retrieve full documentation for requested sections. Accepts section name(s) or path(s) like 'attention', 'api/nn/attention', or ['module', 'pattern'].

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": {
      "section": "<section>"
    },
    "name": "get-documentation"
  }
}

Input Schema

NameRequiredDescriptionDefault
sectionYesSection name(s) or path(s) to retrieve

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the action ('Retrieve full documentation') and parameter usage, but fails to address key behavioral aspects like whether this is a read-only operation, potential rate limits, error handling, or what the output format looks like (e.g., text, structured data). This leaves significant gaps for an AI agent.

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 front-loaded with the core purpose and efficiently follows with parameter usage examples in a single, well-structured sentence. There is no wasted text, making it highly concise and easy to parse.

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?

Given the tool's moderate complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameter usage but lacks details on behavioral traits and output, which are crucial for an AI agent to use it effectively. Without annotations or output schema, more context would be beneficial.

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?

The schema description coverage is 100%, so the input schema already documents the 'section' parameter thoroughly. The description adds value by providing concrete examples of valid inputs (e.g., 'attention', 'api/nn/attention', or ['module', 'pattern']), which clarifies semantics beyond the schema. This meets the baseline for high schema coverage.

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?

The description clearly states the tool's purpose with a specific verb ('Retrieve') and resource ('full documentation for requested sections'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'list-sections', which might list sections without retrieving documentation, leaving room for ambiguity.

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 no guidance on when to use this tool versus alternatives, such as 'list-sections'. It includes usage examples for the parameter but lacks context on appropriate scenarios or exclusions, offering minimal help for an AI agent in tool selection.

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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