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PNPC-MCP-Tally-Prime

create_vouchers_batch

Post hundreds of Tally vouchers in one batch call, split into chunks with automatic verification of which vouchers actually landed. Retry only the unconfirmed ones, avoiding duplicates after timeouts or errors.

Instructions

Post many vouchers (Journal, Payment, Receipt, etc. — same shape as create_voucher, one entry per voucher) in one call, for bulk work where posting one at a time is impractical (e.g. hundreds of balance-confirmation adjustment journals). Internally splits into chunks (default 10, max 50 — Tally's own gateway has a 10-second timeout per request, and a bigger single import risks silently exceeding it) and, after each chunk posts, runs ONE bulk query to confirm exactly which vouchers in that chunk actually exist in Tally now — this always runs, even if the post itself errored or timed out, because a client-side timeout does not mean Tally didn't still create some or all of them server-side; retrying without checking first risks posting duplicates. Verification matches each voucher back by (voucherType, narration) — NOT voucherNumber, confirmed live to be unreliable even when set explicitly (Tally silently reassigned its own auto-series number instead for a Journal voucher); reference was tried next and also came back empty on read. narration is what's confirmed live to persist exactly as sent, so it's REQUIRED and must be non-empty on every voucher, and must be unique per voucherType across the whole batch — two vouchers sharing the same (voucherType, narration) can't be told apart afterward and are rejected up front as invalid rather than silently mismatched (e.g. embed each row's own reference/id in the narration text). The result names exactly which vouchers were confirmed and which were not, per chunk, so a partial failure can be retried by resending only the ones that didn't land — never blindly resend the whole batch. Does not go through preview_write/confirm_write given the scale involved; sending a small batch (2-3 vouchers) first to confirm shape/ledger names before a large run is recommended.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vouchersYesOne entry per voucher, same fields as create_voucher — narration is required here and must be unique per voucherType across the batch (see the tool description).
chunkSizeNoVouchers per internal request. Defaults to 10. Keep at or below 50 — Tally's gateway has a 10-second timeout per request and larger imports take proportionally longer server-side.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.13.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations are all false/absent, so the description carries full burden. It discloses chunking (default 10, max 50), the post-chunk verification query that always runs, why verification is needed due to client-side timeouts, the matching method (by voucherType/narration, not voucherNumber), the narration uniqueness requirement, and that it bypasses preview/confirm_write. This is exceptional transparency about behavior.

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?

The description is long, but the length is justified by the complexity of the tool (chunking, verification, matching, partial failure). It is front-loaded with the core purpose and then flows logically through chunking, verification, matching, narration, and recommendations. It is dense but not verbose; every sentence earns its place.

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?

Complete for a bulk mutation tool with no output schema. It explains the verification mechanism, how to identify successful vs failed vouchers, the required narration format, and the recommendation to test with a small batch. An agent has all information needed to call it correctly and handle failures.

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 description coverage is 100%, so baseline is 3. The description adds crucial semantics beyond the schema: narration is required, must be non-empty, and must be unique per voucherType across the batch because it's the verification key; chunkSize defaults to 10 and max 50 with the timeout rationale. This meaningfully enriches the schema.

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 action ('Post many vouchers') and the resource ('vouchers'), explicitly noting it's the same shape as create_voucher but for bulk work. It distinguishes itself from the single-voucher sibling by scale and use case, leaving no ambiguity about what the tool does.

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?

Explicitly states when to use it ('for bulk work where posting one at a time is impractical') and gives concrete advice: test with a small batch first, and on partial failure resend only the missing vouchers rather than the whole batch. It also contrasts with create_voucher for the voucher shape, providing clear usage context.

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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