Iceberg MCP Server (via Impala)

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execute_query

Execute read-only SQL queries against Iceberg tables through Impala as the authenticated user, returning rows as JSON. Use aggregates, filters, and LIMIT to keep results complete.

Instructions

Execute a read-only SQL query on the Impala database and return the rows as JSON. The query runs as the authenticated end user. Large results are cut off: the answer is then a JSON object with "truncated": true and a note, and it is not the complete data. Prefer aggregates, filters and LIMIT over fetching raw rows.

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>"
    },
    "name": "execute_query"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful traits: the query is read-only, executes under the authenticated end-user's identity, and large results are truncated with a 'truncated': true marker plus a note rather than silently failing. It stops short of error behavior, timeouts, or any row/time limits that would let an agent predict when truncation triggers.

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?

Three sentences, front-loaded with purpose, then return format, then advice. Every sentence is relevant; the phrase 'and it is not the complete data' mildly restates the 'truncated': true flag.

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 elaboration, yet the truncation behavior is still documented, which is the key operational caveat. Missing pieces are secondary for a one-parameter read tool: failure modes, timeout, and when truncation kicks in.

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 0% — the single 'query' parameter is documented only as a string — so the description must compensate. It adds dialect (Impala), the read-only constraint, and the aggregate/filter/LIMIT style recommendation, but no syntax details or examples for the query string itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ('Execute a read-only SQL query on the Impala database') plus the return shape (rows as JSON). It is distinct enough from get_schema to be identifiable, but it never explicitly contrasts itself with that sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It offers real guidance on query shape ('prefer aggregates, filters and LIMIT over fetching raw rows'), which tells the agent how to call it, but there is no statement of when to reach for this tool versus get_schema, and no prerequisites or exclusions.

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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