Glean Connector MCP Server

by gleanwork

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TypeScript

update_data_client

Update a data client implementation by replacing mock code with real API calls for a specified connector module.

Instructions

Write a new data_client.py implementation (replaces the mock with real API calls).

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": {
      "module_name": "<module_name>",
      "code": "<code>"
    },
    "name": "update_data_client"
  }
}

Input Schema

Name	Required	Description	Default
`code`	Yes	Complete replacement for data_client.py. Must include DataClient class with get_source_data() method.
`module_name`	Yes	Snake_case module name matching the connector to update.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 28, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries full burden but fails to disclose critical behaviors like overwriting existing files, required permissions, or side effects. The statement 'replaces the mock' hints at mutation but is insufficient.

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?

Extremely concise, single sentence with no wasted words. However, the brevity sacrifices important behavioral details, so it's not perfectly balanced.

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 lack of output schema and annotations, the description is incomplete. It does not explain return values, errors, prerequisites, or the fact that it overwrites an existing file. A more comprehensive description is needed for a write operation.

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 coverage is 100% with meaningful parameter descriptions. The tool description adds general context but no extra semantic value beyond what the schema already provides. Baseline score of 3 is appropriate.

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 uses a specific verb ('Write') and resource ('data_client.py implementation') and adds context ('replaces the mock with real API calls'). However, it does not differentiate from sibling tools like get_data_client or create_connector, which could cause confusion.

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?

No explicit guidance on when to use this tool versus alternatives (e.g., create_connector for full connector creation or get_data_client for reading). The description implies a use case but lacks clear context or exclusions.

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