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Prepare Partner and Operational Data for Atlas DataSynch

detect_file_schema
Read-onlyIdempotent

Analyze a bounded preview of CSV, spreadsheet, JSON, EDI-normalized, EDIFACT-normalized, cXML-derived, inventory, telemetry, CRM, item, location, shipment, or partner records to identify their structure and determine how they may fit Atlas DataSynch workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesBetween 1 and 100 preview rows from a CSV, spreadsheet, JSON, EDI-normalized, or other tabular payload. Rows are inspected in memory and are not committed by MCP preview tools.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context with 'bounded preview', indicating the tool operates on a limited sample and is non-committal. The input schema further clarifies that rows are 'inspected in memory and are not committed'. No contradictions with annotations.

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 a single, front-loaded sentence that starts with the verb 'Analyze'. While the long enumeration of data types is somewhat list-heavy, each item conveys the tool's broad applicability. It is concise and avoids redundant filler, though it could be trimmed slightly without losing meaning.

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 tool's simplicity (one parameter, read-only, no output schema), the description is largely complete: it explains the input scope and the goal of mapping to Atlas DataSynch workflows. It does not disclose return format or error behavior, but with no output schema and clear annotations, this is not a significant gap. It would be slightly better if it noted typical follow-up tools, but it remains sufficient.

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%, with the 'rows' parameter clearly described (array of 1-100 preview objects, inspected in memory). The description's phrase 'bounded preview' loosely aligns with the schema constraints, but it does not add meaningful new semantics beyond what the schema already provides. Baseline 3 is appropriate when schema covers all parameters.

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 uses the specific verb 'Analyze' with a well-defined resource ('bounded preview of CSV, spreadsheet, JSON, etc.') and a clear outcome ('identify their structure and determine how they may fit Atlas DataSynch workflows'). It clearly distinguishes itself from siblings like detect_location_columns, which is more narrowly scoped, and validate_manifest_schema, which focuses on validation.

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 as a general-purpose initial schema detection tool by listing many supported data types, but it does not explicitly state when to use this tool versus alternatives. For example, it does not mention 'use detect_location_columns when you only need to identify location fields' or indicate that this tool precedes suggest_field_mapping. The context is implied rather than explicit.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools target distinct actions and resources, but get_manifest_summary and wanderloop.get_manifest both involve retrieving manifests, and list_capabilities vs wanderloop.list_provider_capabilities overlap conceptually. However, descriptions clarify the differences, so agent confusion is unlikely for careful readers.

Naming Consistency4/5

The majority of tools follow a consistent verb_noun snake_case pattern (e.g., detect_file_schema, validate_coordinates). The wanderloop.* tools use dot notation as a namespace prefix, which is a minor deviation but still maintains a predictable structure within that subdomain.

Tool Count4/5

With 17 tools, the set is slightly above the typical 3-15 range but appropriately sized for Atlasemoji's broad domain, which includes geofencing, data integration, validation, and research. Each tool serves a distinct purpose, so the count feels justified rather than bloated.

Completeness4/5

The set provides strong coverage for its analytical and validation focus: schema detection, location mapping, data preview, run status, manifest validation, and research capabilities. There are no direct create/update/delete operations, but these appear to be outside the server's intended purpose as a read/analyze/prepare toolset.

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