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k11 API Functional QA

k11 API Functional QA

Official
by K11-Software-Solutions
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Python
Hybrid

Query test database

query_db

Run read-only SQL (SELECT/WITH/SHOW/EXPLAIN) with named parameters against a configured test database to find test data and build database checks. Masked columns appear as •••.

Instructions

Run one read-only query (SELECT/WITH/SHOW/EXPLAIN) against a configured test database, with :named params. Masked columns are shown as •••. Use it to find test data and write db checks.

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": {
      "sql": "<sql>"
    },
    "name": "query_db"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYes
paramsNo
databaseNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations present, the description carries the full burden and does disclose the key safety trait (read-only, statement whitelist) and the masking behavior for sensitive columns, which is genuinely useful. It omits other operationally relevant traits: row limits or pagination, timeout/error behavior, what happens when a non-whitelisted statement or unknown named param is supplied, and whether the configured database is fixed per call.

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 short sentences, front-loaded with the operation and its constraints, with the usage hint last. Every clause carries information; the only mild weakness is the terse ':named params' and '•••' phrasing, which pack meaning densely but are understandable in context.

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?

For a three-parameter query tool with no annotations and no output schema, the definition covers intent, read-only safety, and masking but leaves the result shape, limits, and the database parameter undefined. It is adequate to attempt a correct call, but not complete for a tool that returns arbitrary tabular data.

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%, so the description must compensate and it partially does: it documents the ':named' placeholder convention for the params object and the SQL statement forms accepted by sql. The database parameter is never mentioned, and the shape/keys of the params object (types, nesting) remain undocumented in both schema and description.

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 (Run) plus resource (a read-only query against a configured test database), and enumerates the allowed statement families (SELECT/WITH/SHOW/EXPLAIN). Nothing about this overlaps with siblings like call_api or run_flows, so an agent can identify the tool's function 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?

The closing sentence gives a clear when-to-use signal: find test data and write db checks. It stops short of exclusions or naming alternatives for cases that are not read-only (e.g., drafting or running flows), so an agent must still infer the boundary, but the positive context is explicit.

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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