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Glean Connector MCP Server

Glean Connector MCP Server

by gleanwork
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

infer_schema

Parse data files to analyze field types, null rates, cardinality, and sample values. Understand source data before defining mappings.

Instructions

Parse a data file (.csv, .json, .ndjson) and return field analysis: detected types, null rates, cardinality, and sample values. Use this to understand the source data before defining mappings.

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": {
      "file_path": "<file_path>"
    },
    "name": "infer_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
saveNoIf true, save the inferred schema to .glean/schema.json
file_pathYesPath to a .csv, .json, .ndjson, or .jsonl file to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4/5.0
Behavior3/5

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

Describes the core behavior (parse and return analysis) but omits the side-effect of the save parameter (optional save to .glean/schema.json). No annotations are provided, so description carries the burden; missing details on mutability and save 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?

Two sentences, no wasted words. First sentence states what it does, second sentence advises when to use. Efficient and front-loaded.

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?

Given no annotations and no output schema, the description covers the core functionality and use case well. Minor gap: does not explicitly mention the save parameter's effect or that the file is not modified. Still adequate for a straightforward 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 both parameters are already described in the schema. The description adds no additional parameter meaning beyond what the schema provides, earning a baseline score of 3.

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?

Clearly states it parses data files (.csv, .json, .ndjson) and returns field analysis including types, null rates, cardinality, and sample values. Distinguishes from sibling tools like get_schema (retrieves existing schema) and analyze_field (single field analysis).

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?

Explicitly advises using this tool to understand source data before defining mappings, providing clear context. Does not list exclusions or alternatives but the purpose implies when not to use (e.g., when schema is already known).

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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