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secoda-analysis-mcp

secoda-analysis-mcp

by mbrummerstedt
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

glossary

Fetch all business term definitions from your workspace glossary, with the ability to truncate text fields to a specified character limit.

Instructions

Retrieve all business term definitions from the workspace glossary.

Args:
    truncate_length: Maximum characters for text fields in results (default: 150)

Returns:
    Glossary with text fields truncated to specified length

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": "glossary"
  }
}

Input Schema

NameRequiredDescriptionDefault
truncate_lengthNoMaximum characters for text fields in results

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.3

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the truncation behavior but does not mention that it is a read-only operation, potential performance impact, or any side effects. The behavior is partially transparent.

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 concise with only three sentences, each adding value. It uses a clear structure with Args and Returns sections, and front-loads the main action immediately.

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?

For a simple read tool with one optional parameter and an output schema, the description is largely complete. However, it does not mention pagination or size limits for the glossary, which might be important for large glossaries.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds the default value and return structure (truncated fields), which provides additional context beyond the schema alone. The parameter meaning is clear.

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 clearly states the verb 'Retrieve' and the resource 'workspace glossary', and it distinguishes itself from siblings like ai_chat or entity_lineage by focusing on business term definitions.

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?

The description does not provide any guidance on when to use this tool versus alternatives. It lacks context about prerequisites, limitations, or when to prefer glossary over similar tools like list_resources.

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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