get_parallel_passageGet aligned source-language text and English translation for a passage when sidecar data is available.
| Name | Required | Description | Default |
|---|---|---|---|
| author | Yes | ||
| work_id | Yes | ||
| section_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| date | No | ||
| notes | No | ||
| title | No | ||
| author | No | ||
| work_id | No | ||
| category | No | ||
| section_id | No | ||
| author_slug | No | ||
| data_source | No | ||
| source_text | No | ||
| translation | No | ||
| source_title | No | ||
| source_language | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and non-destructive behavior. The description adds the key constraint that sidecar data must be available, which is not covered by annotations. Some behavioral detail (e.g., no mention of error states) is missing.
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?
Single sentence, no wasted words. Front-loaded with purpose. However, lacks structural elements like bullet points or clear sectioning.
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?
Output schema exists (not shown) so return values are covered. Description explains what is returned. But no context on error handling, limitations, or when to use among many siblings. Adequate but not comprehensive.
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?
The schema has 0% description coverage and the tool description does not explain any of the three parameters (author, work_id, section_id). The agent must rely solely on parameter names, which is insufficient.
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 action ('Get'), the resource ('aligned source-language text and English translation'), and the condition ('when sidecar data is available'). It distinguishes from siblings like 'get_passage' by specifying parallel alignment.
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?
No explicit guidance on when to use this tool versus alternatives. The phrase 'when sidecar data is available' implies a prerequisite but doesn't compare to sibling tools like 'search_parallel' or 'get_passage'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of the corpus: general search vs. specific entity search, passage retrieval vs. work metadata, allusions vs. cross-references, etc. No two tools appear to do the same thing.
All tool names follow a consistent verb_noun pattern (e.g., list_authors, search_corpus, get_passage) and use snake_case throughout, making the set predictable and easy to navigate.
With 19 tools, the server is slightly above the ideal 3-15 range, but each tool covers a necessary function for a rich text corpus API (search, retrieval, cross-referencing). No tools feel redundant or excessive.
The tool surface comprehensively covers the domain: listing authors/works, searching for passages/entities/Greek phrases/letters, retrieving full passages, work metadata, allusions, crossrefs, parallel texts, citation formatting, and AI evidence pack building. No obvious gaps are present.