Skip to main content
Glama

suggest_cleaning

Analyzes a dataset profile and returns specific cleaning recommendations, such as handling nulls, duplicates, or whitespace issues, with rationale and priority.

Instructions

Profile a dataset and return specific recommended cleaning operations.

Analyses the profile and suggests operations with rationale, e.g.:
  - "col 'age' has 12% nulls → consider fill_na or drop_na_rows"
  - "7 duplicate rows detected → apply drop_duplicates"
  - "col 'name ' has leading/trailing whitespace → apply strip_whitespace"

Args:
    path: Absolute local path to the dataset file.

Returns JSON with profile summary and a list of suggested operations,
each with: operation, column (if applicable), rationale, priority (high/medium/low).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions profiling and suggesting operations but does not explicitly state whether the tool is read-only or modifies data. The phrase 'return specific recommended cleaning operations' suggests a read operation, but it should clearly indicate non-destructive behavior.

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 concise, front-loads purpose, and uses examples and structured Args/Returns sections. Every sentence adds value with no wasted words.

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?

Given the simple schema and lack of annotations, the description adequately explains purpose, examples, and return format. However, it lacks details on supported file formats or behavioral side effects, which would improve completeness.

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 coverage is 0%, but the description compensates by clearly defining 'path' as 'Absolute local path to the dataset file.' This is sufficient for a single string parameter. The description covers the parameter well, though it could accept additional format constraints.

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 profiles a dataset and returns specific recommended cleaning operations. Examples of suggestions (fill_na, drop_duplicates, strip_whitespace) clarify the resource. It distinguishes from siblings like 'clean_dataset' (which applies cleaning) and 'profile_dataset' (which likely only profiles).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage before cleaning operations but does not explicitly state when to use this tool versus alternatives like 'profile_dataset' or 'clean_dataset'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from examples.

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/SVerITG/Metis_PH'

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