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SadaaYogee

Notion MCP Server

by SadaaYogee

Inspect Data Source

notion_inspect_data_source
Read-only

Inspect a Notion data source schema and get a compact property summary for AI agents to choose valid property names, options, and relation targets before creating or updating items.

Instructions

Inspect a Notion data source schema and return a compact property summary for AI agents. Use this before creating or updating items so the model can choose valid property names, option values, and relation targets without reading the full Notion API object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoSpecify the response format. 'json' returns the original data structure, 'markdown' returns a more readable format. Use 'markdown' when the user only needs to read the page and isn't planning to write or modify it. Use 'json' when the user needs to read the page with the intention of writing to or modifying it.
data_source_idYesThe data source ID to inspect. Use notion_find or notion_retrieve_database first if you only have a title or database ID.It should be a 32-character string (excluding hyphens) formatted as 8-4-4-4-12 with hyphens (-).
Behavior3/5

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

Annotations already set readOnlyHint=true and destructiveHint=false, so the description's mention of 'inspect' and 'compact property summary' adds minimal behavioral detail. It confirms the tool is read-only but doesn't elaborate on potential rate limits or other traits.

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 two sentences long, front-loads the primary purpose, and contains no unnecessary words. Every sentence earns its place.

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?

No output schema is provided, but the description explains the output is a 'compact property summary'. This is somewhat vague for an AI agent to understand exact return format, but given the tool's simplicity and good annotations, it is minimally adequate.

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 both parameters fully described in the schema itself. The description adds no additional semantic meaning beyond what the schema provides, so baseline score of 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 'Inspect a Notion data source schema and return a compact property summary for AI agents,' specifying the verb (inspect) and resource (data source schema). This distinguishes it from sibling tools like notion_retrieve_data_source, which likely returns full API objects.

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 explicitly states 'Use this before creating or updating items' to choose valid properties/options, giving clear guidance on when to use. It implies when not to use (when full API object is needed) and suggests alternatives like notion_find or notion_retrieve_database for ID retrieval.

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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