Skip to main content
Glama
maximizeGPT

netsuite-saved-search-mcp

by maximizeGPT

get_parse_warnings

Review parse warnings for an exported file to identify rows with issues like phantom columns, bad datetimes, encoding problems, or empty rows. Call after a non-zero warning count is reported.

Instructions

Return the parse warnings for the export at file_path, parsing it on demand if it isn't already cached. Warning kinds: phantom_column (cell at a column index beyond the header count), bad_datetime (DateTime cell that wouldn't parse — raw string is preserved in the row), encoding_recovery (lxml had to recover from invalid XML), empty_row_skipped. Call this after any other tool reports a non-zero warning_count to see exactly which rows are affected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses on-demand parsing behavior and lists warning kinds. With no annotations, this is good coverage, though it could mention error cases or performance impact.

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?

Two sentences with a concise list of warning kinds. Every sentence adds value with no waste. Well-structured and front-loaded.

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?

Given the presence of an output schema, the description fully covers what the tool does, when to use it, and what the returned warnings represent.

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 schema coverage at 0% and a single trivial parameter (file_path), the description merely restates the parameter without adding meaning. For a simple parameter, this is adequate but not exemplary.

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?

Clearly states it returns parse warnings for an export at a given file_path, with on-demand parsing. Distinguishes itself from sibling tools like aggregate_export or query_export by focusing on warning details.

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 instructs to call this tool after another tool reports a non-zero warning_count, providing clear when-to-use context and implied alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/maximizeGPT/netsuite-saved-search-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server