Tripitaka MCP (hosted)

open_sutta_viewer

Read-onlyIdempotent

Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU.

Renders each segment as: Pāli on top (canonical), the Bhikkhu Sujato English below it (verification anchor), and — when you supply translations — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to read a sutta.

sutta_id — standard SuttaCentral id, e.g. sn56.11, mn10, dn22.
around — a segment_id (e.g. dn22:18.1, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line.
offset — 0-based segment index for paging long suttas (use next_offset from the previous result). Do NOT combine with around.
window — segments before/after around to include (default 12).

🌐 Translating for the user (important): when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via translations so the user reads in their own language while still seeing the originals:

Fetch the segments first (get_sutta with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus translations to upgrade the view.) Your translation must travel through the translations parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts translations, so never report it as missing.
Translate from the Pāli as the source, using the English as a semantic guide — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents.
Call this tool with translations=[{segment_id, text}, ...] covering ONLY the segments being displayed (never a whole long sutta), translation_language (BCP-47, e.g. "th", "es"), and translation_disclaimer — one short line IN THE USER'S LANGUAGE saying the translation is AI-generated in this conversation and should be checked against the Pāli/English above. Translations are conversation-ephemeral: nothing is stored server-side; the canon stays Pāli + English only. Translations whose segment_id is not in the displayed window are dropped (reported in translations_dropped).

Without around, shows the sutta from the top (capped for long suttas).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`around`	No
`offset`	No
`window`	No
`sutta_id`	Yes
`translations`	No
`translation_language`	No
`translation_disclaimer`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds substantial behavioral detail beyond this: segment rendering order, ephemeral translations, dropped translations behavior, paging behavior via `offset`/`window`, capping for long suttas, and reporting of `next_offset`/`translations_dropped`. No contradiction with annotations.

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?

The description is long but well-structured with headers, bullet points, and numbered instructions. It front-loads the core purpose and rendering model, then organizes optional parameters and the translation workflow into scannable sections. Every sentence contributes necessary usage information, making the length justified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter, complex tool with an output schema, the description is highly complete. It covers the rendering contract, translation workflow, paging, centering, constraints, and fallback behavior. The output schema handles return-value documentation, so the description's focus on behavior and usage fills the remaining gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for top-level parameters, so the description carries the full burden. It explains every parameter: `sutta_id` format with examples, `around` as a highlighted centered segment, `offset` as a 0-based paging index, `window` as segments around `around`, and the full `translations`/`translation_language`/`translation_disclaimer` trio with concrete usage guidance and examples.

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?

The description clearly states the tool opens an interactive sutta viewer with Pāli and English, plus optional user-language translation. It explicitly distinguishes itself from dumping raw segments by saying 'Prefer this over dumping raw segments when the user wants to read a sutta,' and the sibling context (especially get_sutta) reinforces this differentiation.

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?

The description provides explicit when-to-use guidance ('Prefer this over dumping raw segments...'), when to use `around` after a search, and when translations should be supplied. It also gives a clear 'Do NOT combine with `around`' warning for `offset`, and step-by-step instructions for the translation workflow.

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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TDQS

A4.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose. Search tools are differentiated by strategy (keyword, hybrid, semantic, exhaustive). Reading tools separate content retrieval, comparison, and viewer rendering. Parsing and definition tools have complementary roles. No overlapping functionality.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case, e.g., compare_translations, get_sutta, list_editions, search_by_keyword. The only slight deviation is open_sutta_viewer using 'open' instead of 'view', but it still fits the pattern.

Tool Count5/5

12 tools is well within the ideal range for a domain-specific server. Each tool addresses a distinct need: retrieval, search, parsing, dictionary, citation, structure overview, and comparison. No bloat or thinness.

Completeness5/5

The tool surface covers the full lifecycle for a read-only Pāli canon server: fetching content, searching (multiple modes), viewing with translations, comparing translations, parsing words, looking up definitions, listing editions and structure, and generating citations. There are no obvious gaps for the intended use case.

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