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Cec1c

ERPNext MCP

by Cec1c

erpnext_result_read

Read-onlyIdempotent

Read complete result or input data from stored ERPNext API operations in bounded base64 chunks. Use offset and limit to page, and JSON pointer to select a specific subtree.

Instructions

Read complete result/input bytes in bounded base64 chunks. Optional JSON pointer selects a subtree.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
artifact_idYes
json_pointerNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context beyond those annotations by disclosing bounded base64 chunking, completeness of reads, and subtree selection via JSON pointer, which are not visible in the structured 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 a compact two-sentence definition with no filler. It front-loads the core action and resource, then adds the optional capability, making it easy to scan and parse.

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 read-only, idempotent tool with an output schema, the description covers the main operational aspects: full coverage, chunking, and subtree filtering. It does not mention where artifact_id originates or how clients detect the final chunk, but the output schema and sibling context likely fill those gaps.

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 carries the burden of explaining parameters. It explicitly explains json_pointer and implies limit/offset through 'bounded base64 chunks', but it never states whether limit/offset are raw bytes, base64 characters, or chunk indices, which is a meaningful ambiguity for an agent invoking the tool.

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 names a specific verb ('Read'), a concrete resource ('complete result/input bytes'), and the chunking mechanism ('bounded base64 chunks'). The optional JSON pointer adds further precision, and this clearly separates the tool from siblings like erpnext_doc_get or erpnext_input_begin.

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 read behavior is clearly stated and easily distinguishable from the mutation and query siblings, so the intended context is obvious. It does not explicitly list exclusions or alternatives, but no sibling appears to offer the same artifact-bytes read capability.

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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