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fingerprint_source

Compute a stable hash of a dataset's schema and content to identify changes between sessions without re-running exploratory analysis.

Instructions

Compute a stable hash of (schema + content sniff) for a loaded source.

    Use the returned `fingerprint` to detect whether a dataset has changed
    across MCP sessions without re-running EDA. Output size: tiny.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It implies a non-mutating computation by using 'compute' and 'stable hash', and adds behavioral details like 'loaded source' and 'Output size: tiny'. However, it does not explicitly state side-effect absence or error conditions, but the nature of hashing makes the read-only behavior clear.

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, front-loaded with the core action. Every sentence earns its place: compute action, usage guidance, and output size. There is zero waste and excellent structure.

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?

This is a simple single-parameter tool with an output schema present. The description explains the purpose, usage scenario, and output size, which is sufficient given the tool's complexity. It also places the tool in a workflow (avoiding re-running EDA), making it contextually complete.

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 schema has only source_id with no description, and schema_description_coverage is 0%. The description adds that the tool works 'for a loaded source', which gives context to the parameter, but it does not explicitly define source_id or its constraints. This partial compensation is adequate but not complete.

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 computes a stable hash of schema and content sniff for a loaded source. This specific verb+resource combination distinguishes it from siblings like describe_source or check_stability. The purpose is unambiguous and immediately understandable.

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 a clear use case: detecting dataset changes across MCP sessions without re-running EDA. It gives practical context but does not explicitly mention alternative tools or when not to use it, so it misses the 'when-not' or alternatives that would earn a 5.

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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