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ComplyEaze

ComplyEaze Bridge: TallyPrime MCP server for Claude Desktop

Official

validate_masters

Read-only

Validate up to 100 ledger names against the live TallyPrime catalogue, returning exact, identifier, near_miss, or missing match states and bounded near-miss candidates for import review.

Instructions

Bind 1–100 nonblank ledger names (at most 1024 characters each) against the live catalogue. An identifier embedded in a master name is matched before the name itself. match_state is exact, identifier, near_miss or missing; folded names remain near_miss candidates because this catalogue has no qualified scope to bind them. Only exact is admitted by build_import_xml, and a bound row alone carries exact_live_spelling. A near-miss is never resolved: it returns candidates with the rule that surfaced each, bounded to 25 names and 8192 UTF-8 bytes per requested name, with candidate_count, candidate_count_is_lower_bound and truncation reported. When candidate_count_is_lower_bound is true, the count is a conservative lower bound and must be shown as at least that many candidates. There is no ranking and no score. Each call appends metadata-only receipt lines (tool, company, counts, request and response fingerprints; no book content) to ComplyEaze Bridge's local log on this computer; it writes nothing to Tally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ledgersYes
company_guidYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

A4.2/5.0
Behavior5/5

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

Annotations only cover the safety profile (readOnly, non-destructive, non-idempotent), leaving the description to disclose the real behavior – it does so richly: match_state semantics, that near-misses are never resolved, candidate bounds (25 names / 8192 bytes), the lower-bound count flag, truncation reporting, and metadata-only receipt logging. The append-per-call logging is consistent with idempotentHint=false, so there is no contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded, but the body is an extremely dense run of jargon-heavy clauses (near_miss, candidate_count_is_lower_bound, fingerprints) crammed into few sentences with poor scannability. Every sentence carries content, but structure could be far cleaner.

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?

For a complex, non-trivial tool with no output schema, the description covers return behavior (match states, candidate lists, truncation, lower-bound counts) thoroughly. The only meaningful omission is any explanation of the required company_guid parameter.

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

Parameters3/5

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

With 0% schema description coverage, the description must compensate; it partially does by specifying ledgers constraints (1–100 entries, nonblank, max 1024 chars each). But company_guid – a required parameter – is never explained anywhere, leaving a real gap.

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 verb (bind/validate) and resource (ledger names against the live catalogue) are stated specifically, and the description carves out clear scope by noting only `exact` results are admitted by build_import_xml and that nothing is written to Tally. An agent can distinguish this from sibling tools like masters or ledger_masters without opening a schema.

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?

Usage context is clear: it is a pre-import validation step, and only `exact` results pass downstream to build_import_xml. However, it never explicitly names an alternative sibling tool or states when NOT to use this one, so the routing guidance is implied rather than spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.