data-analysis-agent

by Soham-Donode

Overview Schema Source Related Servers Discussions Admin

Python

find_missing_values

Detect missing or null values per column and count affected rows to evaluate data quality.

Instructions

Analyze missing/null values per column and count affected rows in dataset.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "session_id": "<session_id>"
    },
    "name": "find_missing_values"
  }
}

Input Schema

Name	Required	Description	Default
`session_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the transparency burden. 'Analyze' and 'count' imply a read-only diagnostic operation, but there is no explicit statement that the dataset is not modified, and no mention of side effects or requirements beyond the session_id. This is minimally adequate but not fully transparent.

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 single front-loaded sentence with no filler. It names the action, the subject, and the result in 13 words, which is ideal for tool selection.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter analysis tool with an output schema, the description covers the core operation. However, it omits explicit safety semantics and fails to distinguish itself from handle_missing_values, leaving an agent to infer when to choose it. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 for parameter meaning. It does not mention session_id or explain that the analysis applies to the dataset associated with that session. The parameter name and title are self-explanatory, but the description adds no value for it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Analyze'), a clear target ('missing/null values per column'), and names the output ('count affected rows'). It is readily distinguishable from sibling 'handle_missing_values' because it signals analysis rather than transformation, though it never explicitly names that sibling.

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

Usage Guidelines2/5

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

The description gives no explicit when-to-use guidance or exclusions. Despite a nearby sibling tool named 'handle_missing_values', the description does not say to use this for diagnosis and the sibling for remediation, so an agent must infer usage.

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