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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_data_wrangler

Cleans and normalizes messy data from CSV, JSON, PDF extracts, and copy-pasted tables, returning cleaned records, a quality score, and SQL insert hint.

Instructions

Clean and normalise messy data: CSV, JSON, PDF extracts, copy-pasted tables. Returns cleaned records, a quality score, and SQL insert hint.

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": {
      "data": "<data>"
    },
    "name": "timps_data_wrangler"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYesRaw messy data (paste content)
formatNoInput format hint ('csv'|'json'|'text'|'auto')auto
target_schemaNoOptional: {field_name: type} target schema

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the return values (cleaned records, quality score, SQL insert hint), which is helpful, but it does not explicitly state side effects or safety profile (e.g., whether it's read-only). It lacks details on how 'normalise' works or any error conditions.

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 extremely concise: two short sentences. It front-loads the core action and supported formats, then lists the outputs. Every word earns its place, with no redundant information.

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 moderate complexity and absence of an output schema, the description adequately explains what the tool does and what it returns. It covers input formats and output components, but could be more detailed about the exact structure of 'cleaned records' or the quality score. Still, it is sufficient for an agent to select and invoke the tool correctly.

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?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds some context by listing input formats and mentioning the SQL insert hint, which relates to target_schema, but it does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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 with a specific verb ('Clean and normalise') and resource ('messy data'), and lists concrete input formats (CSV, JSON, PDF extracts, copy-pasted tables). It also distinguishes itself from siblings like timps_data_pipeline or timps_dataset_agent by focusing on data cleaning and normalization.

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 on when to use the tool: when you have messy data in formats like CSV, JSON, PDF extracts, or copy-pasted tables. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to infer appropriate usage.

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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