Skip to main content
Glama

filter_sheet

Fetches live data from an Excel sheet in OneDrive, returning only rows that match filter conditions such as exact text, contains, numeric comparisons, and date ranges. Includes diagnostic info when no matches are found.

Instructions

Fetches a specific sheet LIVE from OneDrive and returns rows matching the given conditions. Always live — no cache. Use when you already know which file and sheet to query. Get column names from get_workspace_graph first.

Condition formats (string form): Exact match: {"ColumnName": "value"} Contains: {"ColumnName": "~value"} (literal, not regex) Comparisons: {"ColumnName": ">100"} (also >=, <, <=) Date bounds: {"Batch Date": ">=2026-01-01"} (ISO-8601)

Condition formats (object form, combinable): IN list: {"Status": {"in": ["Closed", "Shipped"]}} Range: {"Qty": {"between": [10, 500]}} Date range: {"Batch Date": {">=": "2026-01-01", "<": "2026-04-01"}} Null check: {"Notes": {"is_null": false}} Contains: {"Name": {"contains": "oxide"}}

Multiple conditions are ANDed together; multiple operators inside one object are ANDed too. An unknown column name or operator is an error, not an empty result.

MATCHING IS NORMALISED, NOT STRICT: exact string matches ignore case and surrounding whitespace ("closed" matches "Closed "), because Excel cells carry stray whitespace constantly. Pass exact_case=true for byte-for-byte matching. Contains (~) is case-insensitive. If zero rows match, the response includes zero_match_diagnostics showing what each condition matched on its own and the values actually present in the column — use it to correct a near-miss and retry instead of concluding the data does not exist. At most 1000 rows are returned; check the truncated and total_matched fields in the response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sheetYes
sort_byNo
file_nameYes
conditionsYes
exact_caseNo
folder_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description takes full responsibility for behavioral disclosure. It covers the live/cache behavior, case-insensitive normalized matching, exact_case flag, literal-contains semantics, error behavior for unknown columns/operators, zero_match_diagnostics, and the 1000-row limit with truncated/total_matched fields.

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 long but earns its length: a clear purpose sentence, structured condition formats with examples, then matching semantics and edge-case behavior. Each section serves a distinct need, and examples are concrete.

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 seven parameters, nested conditions, and an output schema, this description covers the high-risk behaviors: error semantics, matching rules, zero-match diagnostics, and response truncation. The presence of an output schema covers return-value structure, and the description supplements it with total_matched/truncated details. The only gaps are sort_by/folder_path semantics, which are minor.

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 0%, so the description must compensate. It does, thoroughly, for the conditions parameter: exact/contains/comparison/date/IN/between/is_null/contains object forms, AND semantics, and normalization. It also explains exact_case and limit behavior. However, sort_by and folder_path are not explicitly described beyond the schema, a minor 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 description opens with a specific verb-resource pair: 'Fetches a specific sheet LIVE from OneDrive and returns rows matching the given conditions.' It also preempts sibling confusion by noting to get column names from get_workspace_graph first and stating 'Use when you already know which file and sheet to query.'

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?

It explicitly says 'Use when you already know which file and sheet to query,' and directs users to get_workspace_graph for column names, setting clear context. It doesn't spell out when not to use it relative to query/aggregate/join_sheets, but the specificity of the condition syntax and the mention of the 1000-row limit imply boundaries.

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/Karunya-Muddana/ExcelMCP'

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