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Glean Connector MCP Server

Glean Connector MCP Server

by gleanwork
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

analyze_field

Analyzes a single schema field to provide samples, type details, and mapping suggestions for Glean connector development.

Instructions

Deep-dive on a single field from the current schema: samples, type details, and Glean mapping suggestions.

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": {
      "field_name": "<field_name>"
    },
    "name": "analyze_field"
  }
}

Input Schema

NameRequiredDescriptionDefault
field_nameYesName of the field to analyze in depth

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are present, so description carries full burden. It indicates the tool returns samples, type details, and mapping suggestions, but does not disclose whether it modifies state, requires authentication, or has rate limits. The read-only nature is implied but not explicit.

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?

Single sentence that is concise and front-loads the core purpose. Could be slightly more structured but is efficient with no wasted words.

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 one required parameter and no output schema, the description covers the main purpose and outputs. However, it lacks details on what exactly 'samples' entails, error handling, and assumptions (e.g., field must exist).

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 a clear parameter description. The tool description adds no additional meaning beyond 'deep-dive on a single field' and outputs, which provides context but not parameter-specific detail. With high coverage, baseline 3 is appropriate.

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?

Description clearly states verb 'deep-dive' and resource 'single field', and lists the outputs (samples, type details, Glean mapping suggestions), which distinguishes it from sibling tools like get_schema (overview) and get_mappings (mapping-specific).

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 like get_schema or get_mappings. The description implies usage for in-depth field analysis but does not provide conditions, prerequisites, or exclusions.

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

Deploy Server

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