Solr MCP

vector-select

Perform advanced vector similarity search by combining a query vector with SQL filtering to find records matching both semantic and structured conditions.

Instructions

Vector similarity search combined with SQL filtering (advanced).

Extends sql-select with a query vector matched against a dense_vector/knn_vector field. ORDER BY is not allowed in query.

Parameters:

query: SQL query to execute
vector: Query vector (dimensions must match the vector field)
field: Vector field name (optional; auto-detected when omitted)

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": {
      "query": "<query>",
      "vector": "<vector>"
    },
    "name": "vector-select"
  }
}

Input Schema

Name	Required	Description	Default
`field`	No
`query`	Yes
`vector`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose two real constraints beyond the schema: the ORDER BY prohibition and the auto-detection behavior of 'field'. It says nothing about read-only safety profile, cost/latency of vector search, or error modes when vector dimensions mismatch, leaving meaningful gaps for an unannotated tool.

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?

Purpose and the key caveat are front-loaded, followed by a compact parameter list. The parameter list is justified given 0% schema coverage rather than redundant, though the bullet formatting is slightly verbose for three fields.

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 no explanation, and the description covers the purpose, main constraint, and all three parameters. What it lacks is guidance versus the remaining vector-related siblings and any note on failure conditions such as dimension mismatch.

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 description coverage is 0%, so the description must compensate, and it does: it explains that 'vector' dimensions must match the vector field and that 'field' is optional and auto-detected when omitted. Only the exact syntax/format expected inside 'query' is left unspecified.

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+resource combination ('vector similarity search combined with SQL filtering') and explicitly positions itself relative to a named sibling ('Extends sql-select with a query vector matched against a dense_vector/knn_vector field'). An agent can distinguish this from sql-select and semantic-select without opening any 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?

The phrase 'Extends sql-select with a query vector' clearly tells the agent when to pick this over sql-select (when a vector match is needed). However, it never names the other plausible alternative semantic-select, nor states any when-not-to-use condition or prerequisite, so the guidance is clear but incomplete.

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

Deploy Server