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fojin — Buddhist Canon Tools

by app.fojin

verify_quote

Verify that a Buddhist-canon quote exists VERBATIM in the corpus.

Call this before presenting any quoted scripture to a reader: LLMs
routinely invent plausible-looking quotes. Returns `verbatim` (bool),
where it was found (`matches`, each with a resolvable `urn`), or the
closest near-miss window when it wasn't. `cite` optionally narrows the
search — a CBETA id ("T0374") or fojin URN ("fojin:cbeta/T0374.13") —
and `cite_matched` reports honestly whether the quote is where you
claimed (a hit in a different fascicle does NOT confirm your citation).
Quote must be ≥4 CJK chars after normalisation; Classical Chinese only.
Short quotes are answered but say less: a four-character phrase recurs
across the canon, so read `cite_matched` rather than `verbatim`, and check
`matches_capped` before treating the list as complete. Each match carries
an absolute `reader_url` — cite that, not a reconstructed third-party link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
citeNo
juanNo
quoteYes

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It richly discloses return values (verbatim, matches with urn, near-miss window), the honesty of cite_matched, the caveat about short quotes, the matches_capped limitation, and the advice to use reader_url. This goes far beyond a basic tool description.

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 front-loaded with a clear one-line purpose, and every subsequent sentence adds critical operational detail. It is dense yet efficient, covering return semantics, input constraints, and usage warnings without repetition or fluff.

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?

Given no output schema, the description effectively covers all essential return fields (verbatim, matches, urn, cite_matched, matches_capped, reader_url) and usage limitations. It is slightly incomplete in not explaining the `juan` parameter or the exact output structure, but overall it provides enough context for an agent to use the tool correctly.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It thoroughly explains `quote` and `cite`, including format examples (CBETA id, fojin URN). However, the `juan` parameter is not explicitly described, though 'fascicle' appears in the text. Most parameters are well-addressed, but one remains undocumented.

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 first sentence clearly states a specific verb and resource: 'Verify that a Buddhist-canon quote exists VERBATIM in the corpus.' This distinguishes it from sibling tools like search_corpus by emphasizing exact verbatim verification and near-miss reporting, making its purpose unmistakable.

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 explicitly instructs when to use it: 'Call this before presenting any quoted scripture to a reader' and explains the problem of LLM-invented quotes. It also provides context by mentioning the requirement of ≥4 CJK characters and Classical Chinese only. It does not name alternative tools, but the situational guidance is strong.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource or operation: commentaries for historical glosses, get_parallels for cross-tradition alignments, lookup_dictionary for terms, lookup_entity for entities, read_passage for full text, resolve_urn for citation resolution, search_corpus for semantic search, and verify_quote for verbatim verification. There is no overlap in purpose, and the boundaries are clear even between similar-looking lookups.

Naming Consistency4/5

Most tool names follow a consistent verb_noun snake_case pattern (get_parallels, lookup_dictionary, read_passage, resolve_urn, search_corpus, verify_quote). The lone exception is 'commentaries,' which is a noun rather than a verb_noun construction, but this is a minor deviation in an otherwise uniform set.

Tool Count5/5

With 8 tools, this server is well-scoped for a specialized Buddhist canon research domain. Each tool earns its place, covering search, retrieval, verification, lookup, and cross-reference without redundancy or bloat.

Completeness5/5

The toolset covers the complete research workflow: finding passages (search_corpus), reading them (read_passage), verifying quotes (verify_quote), resolving citations (resolve_urn), understanding terms (lookup_dictionary) and entities (lookup_entity), and exploring cross-tradition parallels (get_parallels) and commentaries (commentaries). No significant gaps exist for the stated purpose.

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