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find_cells_with_value

Locates all cells containing a specific value in CSV datasets for data validation, quality checking, and pattern identification.

Instructions

Find all cells containing a specific value for data discovery.

Searches through the dataset to locate all occurrences of a specific value, providing coordinates and context. Essential for data validation, quality checking, and understanding data patterns.

Returns: Locations of all matching cells with coordinates and context

Search Features: 🎯 Exact Match: Precise value matching with type consideration 🔍 Substring Search: Flexible text-based search for string columns 📍 Coordinates: Row and column positions for each match 📊 Summary Stats: Total matches, columns searched, search parameters

Examples: # Find all cells with value "ERROR" results = await find_cells_with_value(ctx, "ERROR")

# Substring search in specific columns
results = await find_cells_with_value(ctx, "john",
                                    columns=["name", "email"],
                                    exact_match=False)

AI Workflow Integration: 1. Data quality assessment and error detection 2. Pattern identification and data validation 3. Reference data location and verification 4. Data cleaning and preprocessing guidance

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesThe value to search for (any data type)
columnsYesList of columns to search (None = all columns)
exact_matchYesTrue for exact match, False for substring search

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether operation completed successfully
coordinatesYes
exact_matchYes
search_valueYes
matches_foundYes
search_columnNo
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does: searches through datasets, returns coordinates and context, and includes search features like exact match vs. substring. It mentions return types ('Locations of all matching cells with coordinates and context') and provides examples, though it could add more on performance or limitations.

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 well-structured with clear sections (e.g., 'Returns:', 'Search Features:', 'Examples:', 'AI Workflow Integration:'), making it easy to scan. It is appropriately sized but could be slightly more concise, as some parts (like the workflow integration list) are somewhat verbose without adding critical information.

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 tool's complexity (search operation with multiple parameters), no annotations, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, usage, behavior, and examples, providing sufficient context for an AI agent to understand and invoke the tool effectively without needing to explain return values in detail.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds minimal parameter semantics beyond the schema, such as implying 'value' can be 'any data type' and showing usage in examples, but it does not significantly enhance understanding of parameters like 'columns' or 'exact_match' beyond what the schema already provides.

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 tool's purpose: 'Find all cells containing a specific value for data discovery.' It specifies the verb ('find'), resource ('cells'), and scope ('all'), and distinguishes itself from siblings like get_cell_value (single cell) or check_data_quality (broader validation).

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 description provides clear context for when to use the tool ('data discovery,' 'data validation, quality checking, and understanding data patterns') and includes 'AI Workflow Integration' with specific use cases. However, it does not explicitly state when NOT to use it or name alternatives among siblings (e.g., vs. find_anomalies or check_data_quality).

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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