Solr MCP

get-default-text-vectorizer

Retrieve the default text vectorizer's model name, vector dimensionality, and service URL to align Solr vector fields with the embedding model for semantic-select.

Instructions

Get the default embedding model used for semantic-select.

Returns model name, vector dimensionality, and service URL. Use this to ensure Solr vector fields match the embedding model dimensions.

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": "get-default-text-vectorizer"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden, and it does disclose the return content (model name, vector dimensionality, service URL) plus the read-only, zero-argument nature implied by the call shape. It does not state failure modes (e.g., what happens if no default is configured), which keeps it short of a 5.

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?

Three short sentences, front-loaded with the purpose, then the return payload, then the use case. No filler or redundancy.

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?

An output schema exists, so return values need not be enumerated, yet the description helpfully summarizes them anyway, and the intended workflow (aligning Solr vector field dimensions) is clear. Only edge cases such as misconfigured or absent defaults are unaddressed.

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?

The tool takes zero parameters, so the baseline is 4; there is nothing to document and schema coverage is 100%. No additional parameter meaning is needed or missing.

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?

States a specific verb and resource ('Get the default embedding model') and immediately scopes it to the semantic-select feature, which no sibling tool covers. An agent can distinguish this from search, sql-select, vector-select, and get-schema without opening the schema.

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 says when to use it: 'Use this to ensure Solr vector fields match the embedding model dimensions.' That is a concrete triggering condition. It does not name alternatives, but no sibling duplicates this function, so the omission is minor.

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

Deploy Server