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Switch all tools to a different local database by name. Point find, ingest, and sync operations to the selected database until changed again. Specify 'default' for the standard database.

Instructions

Use to point every other tool at a different named database.

All tools (find, ingest, sync, etc.) will target the selected database's daemon until changed again. Use list(kind="databases") to see the database the daemon is fixed to.

Only selects among LOCAL databases: while a remote target (QUARRY_URL or a 'quarry login') is active, the remote daemon is fixed to its own database and this has no effect.

Args: name: Database name (e.g., 'coding', 'work'). Use 'default' for the default database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the important state-changing effect ('all tools will target the selected database's daemon until changed again') and the remote-target no-op condition. This is sufficient for the tool's scope, though it does not detail error behavior.

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?

The description is well-structured with a clear purpose up front, behavioral notes, and an args section. The first two sentences restate the same idea somewhat, but the organization is still efficient and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-argument context-switching tool, this description covers everything an agent needs: persistent side effects, how to verify current state, the local-only constraint, and parameter semantics. The output schema covers the return value, so no additional return-value detail is necessary.

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 input schema provides only a string field with no description, so the schema coverage is 0%. The description compensates by explaining the argument ('Database name'), giving concrete examples ('coding', 'work'), and explaining the special value 'default'.

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 identifies the tool's function: switching the target database for all other tools. It uses a specific verb and resource ('point every other tool at a different named database') and distinguishes itself from sibling data-operation tools by describing its context-setting role.

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 description gives practical usage context: it names the persistent effect until changed again, tells the user how to see the current target via list(kind='databases'), and explicitly states when the tool has no effect (active remote target). It does not explicitly enumerate alternatives, but the when/when-not guidance is clear.

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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