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register_source

Idempotent

Register a data source to profile all columns, detect formula relationships, and automatically identify join keys to existing sources, making the source queryable by name.

Instructions

Advanced tool. Register a data source and get full schema profiling + join detection. Profiles every column (type, cardinality, fill rate, distribution). Detects formula relationships (A×B≈C) within the source. Detects join keys to every already-registered source automatically. After registration the source is queryable by name via query_data. Safe to call multiple times — re-registration is a no-op if data is unchanged. Registration persists the source profile under the active API key's organization. Returns source_id and the profiled schema with columns, roles, formulas, and detected join keys.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesData source definition. Provide exactly one of: records, csv, json_str, url.
descriptionNoOptional human description of this source.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

The annotations already indicate idempotent, non-destructive, and non-read-only behaviorikuha. The description adds meaningful context beyond this: 'Safe to call multiple times — re-registration is a no-op if data is unchanged', 'Registration persists the source profile under the active API key's organization', and the return value details. It clearly explains side effects and persistence, which is exactly the kind of additional transparency the dimension rewards.

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 dense but every sentence serves a purpose. It opens with the core purpose, enumerates key capabilities, notes idempotency and persistence, and closes with the return value. There is no fluff or redundant restatement of the schema. The structure is front-loaded with the most decision-relevant 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 there is no output schema, the description adequately covers the return: 'Returns source_id and the profiled schema with columns, roles, formulas, and detected join keys.' It also explains side effects, idempotency, and post-registration behavior. The nested input schema is thoroughly covered by the schema itself, so the description needn't repeat it. The tool is complex, but the description provides enough context for an agent to use it 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?

The input schema fully describes both parameters and their nested properties, including descriptions for records, csv, json_str, url, and connection. The tool description does not add significant parameter-level semantics beyond what the schema already provides. With 100% schema coverage, the baseline is 3 and nothing in the description materially improves on it.

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 specific operation: 'Register a data source and get full schema profiling + join detection.' It identifies the primary resource ('data source'), the action ('register'), and the expected outcome, which distinguishes it from sibling tools like list_sources or get_source_schema. The statement 'After registration the source is queryable by name via query_data' further clarifies the tool's unique role.

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 explains what register_source does and mentions 'Advanced tool' at the start, hinting at careful use subs. It also states 'Safe to call multiple times' which gives some usage guidance. However, it does not explicitly say when to prefer this tool over siblings like connect_data or onboard_dataset, nor does it give exclusions or alternative routes for simpler use cases.

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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