Glean Connector MCP Server

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build_connector

Generate Python connector files from schema, mappings, and configuration. Preview code with dry_run or write connector.py, models.py, and mock_data.json.

Instructions

Generate Python connector files from schema + mappings + config. Use dry_run: true to preview the generated code without writing files.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "build_connector"
  }
}

Input Schema

Name	Required	Description	Default
`dry_run`	No	If true, return generated code as text without writing files. Set to false to write connector.py, models.py, and mock_data.json.
`connector_name`	No	Class name for the connector (defaults to "Connector")
`datasource_type`	No	Connector base class type	basic

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 28, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates a write operation (generating files) and notes that dry_run previews without writing. However, it omits details about side effects, required permissions, or what happens to existing files.

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 with two sentences, no redundant information, and the key action is front-loaded. Every word contributes value.

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 that generates files from multiple inputs, the description is brief. It lacks context on prerequisites (e.g., having schema/mappings/config) or post-conditions. Given the schema is fully described and siblings exist for related steps, it is minimally adequate.

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 covers all three parameters with descriptions (100% coverage). The description adds minimal extra meaning beyond highlighting the dry_run parameter's effect, which aligns with the schema.

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 generates Python connector files from schema, mappings, and config. It provides a specific verb and resource, distinguishing it from most siblings, though it does not explicitly differentiate from similar tools like 'create_connector'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions using dry_run: true to preview generated code, which is a useful usage tip. However, it does not provide explicit guidance on when to use this tool vs. alternatives or any prerequisites.

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