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clearskies MCP Server

clearskies MCP Server

by clearskies-py
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

suggest_modules

Identify recommended modules for a given component type. Specify the component category to receive targeted suggestions.

Instructions

Suggest modules that provide a specific component type.

Args:
    component_type: The component category (e.g. "backends", "contexts", "models")

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": {
      "component_type": "<component_type>"
    },
    "name": "suggest_modules"
  }
}

Input Schema

NameRequiredDescriptionDefault
component_typeYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries full burden. It doesn't disclose whether the tool is read-only, how it handles invalid component types, or what form the suggestions take. The bare statement 'Suggest modules' offers minimal behavioral insight.

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 extremely concise, containing a one-line purpose statement and a single parameter explanation. Every word earns its place, and the 'Args:' structure is clear and scannable.

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?

For a tool with one parameter and an output schema, the description is minimally viable but leaves context gaps. It doesn't explain what 'modules' refers to, what criteria are used for suggestion, or what the output represents beyond what the output schema might cover. It's adequate but not complete.

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?

The schema only defines component_type as a string, but the description enriches it by explaining it as 'The component category' with concrete examples ('backends', 'contexts', 'models'). This is crucial given 0% schema description coverage and helps the agent use the parameter correctly.

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 function with a specific verb ('Suggest') and a resource ('modules that provide a specific component type'). It distinguishes itself from sibling tools like list_available_* by focusing on module suggestion rather than listing, though it doesn't explicitly differentiate.

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 like list_modules or get_module_info. It lacks any contextual hints about use cases, prerequisites, 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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