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Glama

lookup_code

Retrieves official KRA eTIMS code tables and specific code definitions for tax types, payment methods, credit note reasons, and stock movement types.

Instructions

Look up official KRA code tables or specific code definitions (tax types, payment methods, credit note reasons, stock movement types, packaging units, quantity units).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoOptional specific code to inspect (e.g. 'B' for tax type, '06' for payment method, '15' for stock discarding)
table_nameYesCode table identifier or 'all' to list all available tables

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden, and it does signal that the data is a static, authoritative KRA reference set ('official KRA code tables'), which reassures the agent no mutation occurs. However, it never says what a call returns (a list of codes, a single definition record, or both) or whether 'all' yields a different shape, which matters here because there is no output schema.

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?

A single sentence that front-loads the verb and the resource, with the enumeration earning its place by telling the agent which domains are covered. The parenthetical list is long but each item is a real category the agent can match a question against.

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 two-parameter reference lookup with no output schema and no annotations, the description covers what the tool reads but not what comes back or in what form. The distinction between the list-table mode and the single-code mode is only implied, leaving the agent to guess the response shape before calling.

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 description coverage is 100% and the table_name enum is fully self-documenting, so the schema does the heavy lifting and the baseline of 3 applies. The description's parenthetical category list loosely mirrors enum members but adds no new syntax, defaults, or interaction rules beyond what the schema already states.

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?

States a specific verb+resource ('Look up official KRA code tables or specific code definitions') and enumerates the domains covered (tax types, payment methods, credit note reasons, etc.), so the agent knows exactly what data lives here. It stops short of naming a sibling, so it does not differentiate itself from search_knowledge_base or get_api_spec, which could plausibly surface overlapping reference content.

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

Usage Guidelines2/5

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

There is no when-to-use guidance at all — nothing says to prefer this over search_knowledge_base for code lookups, or over get_api_spec when the agent needs schema-level detail. The two operating modes (pass table_name alone to list a whole table vs. add code to inspect one entry) are left for the agent to infer from the schema rather than stated.

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