Skip to main content
Glama
Sign In
gemini-mcp-bridge

gemini-mcp-bridge

by hampsterx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

structured

Read-only

Generate JSON responses that conform to a provided JSON Schema. Use file context for data extraction, classification, or other machine-parseable tasks.

Instructions

Agentic structured output: generate a JSON response conforming to a provided JSON Schema. Gemini runs inside workingDirectory with read_file and grep tools, so it can read files for context. Use for data extraction, classification, or any task needing machine-parseable output. The response is validated against the schema; isError is true if validation fails.

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": {
      "prompt": "<prompt>",
      "schema": "<schema>"
    },
    "name": "structured"
  }
}

Input Schema

NameRequiredDescriptionDefault
filesNoText file paths to reference as context (no images). Gemini reads them with its own tools — contents are NOT inlined. Max 20 files, 1MB each.
modelNoGemini model override. Omit to let CLI auto-route.
promptYesWhat to generate or extract from the provided context
schemaYesJSON Schema as a string. The response will be validated against this. Max 20KB.
timeoutNoTimeout in ms (default: 120000, max: 1800000).
workingDirectoryNoWorking directory for file resolution.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint true, destructiveHint false), the description discloses the agentic design: Gemini runs inside workingDirectory with read_file and grep tools, and the response is validated against the schema with isError set on failure. This adds significant behavioral context.

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?

Three dense sentences, front-loaded with the core purpose. Each sentence contributes useful information without redundancy—purpose, agentic context, and validation behavior.

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 description covers purpose, usage, validation, and behavioral context, making it mostly self-contained. It does not describe return value structure, but with no output schema and strong annotations, this is an acceptable gap for a structured-output tool.

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 baseline is 3. The description adds context about agentic file reading and validation but does not elaborate on parameter formats beyond what the schema already states, so it adds only marginal semantic value.

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 opens with a specific verb and resource: 'generate a JSON response conforming to a provided JSON Schema.' It clearly distinguishes from siblings by targeting structured output for data extraction/classification, leaving no ambiguity about what the tool does.

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?

It explicitly says 'Use for data extraction, classification, or any task needing machine-parseable output,' providing actionable usage guidance. It does not name alternative tools, but the use cases are clear enough to guide an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools