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grzetich

AI Developer Tools MCP Server

by grzetich

compare_tools

Compare adoption metrics between 2-3 AI developer tools like OpenAI SDK, Anthropic SDK, Cursor, GitHub Copilot, or LangChain to analyze usage trends and make informed tool selection decisions.

Instructions

Compare adoption metrics between 2-3 AI developer tools (e.g., OpenAI vs Anthropic SDK)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolsYesArray of 2-3 tool IDs to compare
time_rangeNoTime range for comparison metrics30d
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it works—e.g., what metrics are compared (e.g., downloads, usage), whether it requires authentication, if there are rate limits, or what the output format looks like. This leaves significant gaps for an agent to understand the tool's behavior.

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 a single, efficient sentence that front-loads the core purpose ('compare adoption metrics') and provides relevant examples. There is no wasted text, and it's appropriately sized for the tool's complexity.

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?

Given the tool's complexity (comparing metrics across tools) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'adoption metrics' entail, how results are presented, or any behavioral traits like data sources or limitations. This makes it inadequate for an agent to fully understand the tool's context and usage.

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%, with both parameters well-documented in the schema (tools array with enum and constraints, time_range with enum and default). The description adds minimal value beyond this, only implying the tools parameter with examples but not explaining the time_range or any additional context. Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('compare adoption metrics') and the resource ('2-3 AI developer tools'), with specific examples provided. However, it doesn't explicitly distinguish this tool from its siblings (get_tool_history, get_trending_tools, search_tools), which likely serve different purposes like retrieving historical data, identifying trends, or searching tools respectively.

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 its siblings or alternatives. It mentions comparing 2-3 tools but doesn't explain why this tool is preferred over, say, using get_tool_history multiple times or how it differs from search_tools. No exclusions or prerequisites are stated.

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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