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machinemates-ai

Gemini Research MCP Server

call_tool

Execute any discovered tool by providing its name and arguments. Enables programmatic invocation of tools for AI-powered research tasks.

Instructions

Call a tool by name with the given arguments.

Use this to execute tools discovered via search_tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the tool to call
argumentsNoArguments to pass to the tool
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states the basic action without mentioning error handling, side effects, or permissions. For a tool that dynamically executes other tools, significant behavioral context is missing, such as what happens if the tool fails or is unavailable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise at two sentences, with the key action front-loaded. It wastes no words, but could be slightly more structured for clarity. Overall, it efficiently conveys the essential information.

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 complexity (dynamic tool execution) and the absence of an output schema, the description is minimal. It covers the basic purpose and usage context but lacks details about return values, errors, or expected behavior. For a generic call tool, it barely meets the minimum viable level.

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 input schema has 100% description coverage, so the schema already explains the parameters. The description adds no extra meaning beyond restating 'with the given arguments.' Baseline 3 is appropriate as the description does not detract but adds no value.

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: 'Call a tool by name with the given arguments.' It identifies the core action (calling a tool) and the resource (the named tool). While not overly specific, it distinguishes from sibling tools like research_web or search_tools, which are different operations.

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 provides context: 'Use this to execute tools discovered via search_tools.' This tells the agent when to use it (after discovery) and implies a sequence. It does not explicitly state when not to use it, but the usage is straightforward enough that alternatives are not needed.

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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