get_verse_textFetch the exact text of a Bible verse, passage, or whole chapter from a public-domain translation (kjv = King James Version). Deterministic — no AI generation.
| Name | Required | Description | Default |
|---|---|---|---|
| reference | Yes | e.g. "Romans 8:28" or "Psalm 23" | |
| translation | No | default: kjv |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool is deterministic (no AI generation), returns exact text, and uses public-domain translations. This is valuable context for an agent deciding between this and study tools. It could mention error handling or return formatting, but for a simple retrieval tool, the transparency is above average.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, followed by a key behavioral trait. Every word earns its place; no fluff or repetition. Excellent conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (2 parameters, no output schema). The description covers purpose, input granularity (verse/passage/chapter), translation option, and deterministic behavior. Given the low complexity, this is fully complete for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. Both parameters (reference and translation) are described in the schema. The description adds minor context by explaining 'kjv = King James Version', which aids understanding of the enum, but it does not significantly add meaning 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: fetching exact text of Bible verses, passages, or chapters. It specifies the resource (text) and the source (public-domain translation), and distinguishes it from sibling tools like 'find_verse_study_page' or 'study_bible_passage' by focusing on raw text retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: when you need exact, deterministic text from a public-domain translation. It does not explicitly name alternatives or exclusions, but the deterministic/no-AI-generation note helps differentiate it from study or verification tools. Clear context, though no explicit alternative comparison is given.
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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Most tools have distinct purposes, but get_verse_text and resolve_reference overlap by both returning verse text, making it unclear when to use one over the other. The other three tools are clearly differentiated by their focus on study pages, AI-powered study, and fact-checking.
All tool names follow a consistent snake_case verb_noun pattern: find_verse_study_page, get_verse_text, resolve_reference, study_bible_passage, verify_scripture_quote. No mixed conventions or vague verbs.
Five tools is well-scoped for a Bible study copilot, covering retrieval, reference resolution, AI-assisted study, and quote verification without unnecessary bloat. Each tool has a clear role, and the count is within the ideal range.
The core workflow is covered: resolving references, fetching text, checking for study pages, asking AI questions, and verifying quotes. A minor gap is the lack of a dedicated search tool for finding verses by keyword, and the overlap between get_verse_text and resolve_reference slightly reduces efficiency, but the surface is largely complete.