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kosminus

querywise-mcp

add_glossary_term

Define a business glossary term that maps plain-language phrases to SQL expressions. Teach the semantic layer common business terms for consistent query generation.

Instructions

Define a business glossary term that maps business language to a SQL expression.

Use to teach the semantic layer phrases like 'active customer' so future grounding and generation apply them consistently. For a named, reusable aggregate (a KPI) use add_metric instead. Returns the new term's id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connectionYesTarget database connection — its name or id (case-insensitive). List the available connections with list_connections.
termYesThe business term being defined (e.g. 'active customer').
definitionYesPlain-language meaning of the term.
sql_expressionYesSQL snippet/predicate that implements the term (e.g. a WHERE condition).
related_tablesNoOptional list of table names the term applies to.
Behavior3/5

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

Description mentions return value (new term's id) but does not elaborate on side effects or error cases. Annotations show it is not read-only, consistent with a create operation. Adequate but not enhanced.

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 sentences, front-loaded with purpose, followed by usage guidance and alternative. No wasted words.

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?

Given annotations, schema coverage, and no output schema, the description is fairly complete: it states purpose, usage, alternative, and return value. Lacks details on idempotency or duplicate handling, but acceptable for a simple create tool.

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 descriptions cover all parameters (100% coverage). Description provides a usage example ('active customer') and mentions return value, but adds minimal extra meaning beyond schema for the parameters themselves.

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?

Description clearly states the tool defines a business glossary term mapping business language to SQL expression. It distinguishes from sibling 'add_metric' by noting the difference for KPIs.

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

Usage Guidelines5/5

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

Explicitly states when to use (teach semantic layer phrases) and provides alternative ('add_metric' for reusable aggregates). Offers clear guidance.

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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