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Glama

search_lexicon

Read-onlyIdempotent

Find Greek and Hebrew lexicon entries by English word, transliteration, or concept to identify original-language terms behind biblical words and distinguish near-synonyms.

Instructions

Search the Greek and Hebrew lexicons by English word, transliteration, or concept.

Returns the matching lexicon entries ranked by relevance — typically several distinct original-language words that an English term covers (e.g. "love" → agapē, phileō, erōs), each with its Strong's number, definition, and semantic range.

Relevant for identifying which original-language terms lie behind an English word or a biblical concept, and for distinguishing between near-synonyms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return. Default: 10
queryYesSearch term (English word, transliteration, or concept)
languageNoLimit search to one language. Omit to search both.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is already clear. The description adds useful behavioral context: results are ranked by relevance, typically return several distinct words, and include Strong's numbers, definitions, and semantic ranges. It does not mention pagination or maximum result behavior, but the schema covers the limit parameter.

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?

Well-structured and front-loaded: the first sentence states the action and search axes, the second explains the return format with a concrete example, and the third gives use-case guidance. Slightly verbose with the example, but every sentence adds value.

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?

Complete for a read-only search tool with annotations covering safety and a fully described schema. It explains what is returned and why the tool is useful, though it does not discuss result limits or how to refine an overly broad search. No output schema exists, but the description adequately describes the return content.

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%, so the schema fully documents all three parameters, including the language enum and limit default. The description adds conceptual color (e.g., an English term like 'love' maps to multiple Greek words), but no extra syntax or format details beyond the schema. Baseline 3 is appropriate.

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 (Search) and resource (Greek and Hebrew lexicons) with the axes of search (English word, transliteration, concept). It distinguishes itself from siblings like search_by_strongs (which searches by number) and get_bible_dictionary (which presumably does general dictionary lookup).

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 clearly states what the tool is relevant for — identifying original-language terms behind an English word or concept, and distinguishing near-synonyms. However, it does not explicitly name alternative tools or when NOT to use this one (e.g., vs search_by_strongs or get_bible_dictionary).

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