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HarshShinde0

GeoCroissant MCP Server

by HarshShinde0

get_structure_graph

Extract a dataset's internal structure graph to map fields, record sets, files, and foreign-key joins, tracing lineage and clarifying architecture for loading code.

Instructions

Extracts the internal structure graph of a Croissant document.

    Builds the directed multigraph that ``mlcroissant`` uses internally for
    static analysis: nodes are Metadata / FileObject / FileSet / RecordSet /
    Field objects and edges connect fields to their data sources, record sets
    to their fields, files to archives they are contained in, and referenced
    (foreign-key) fields.

    Usage: Use this tool to reason about dataset lineage and dependencies,
    e.g. "which files feed this field?", "what does this join look like?",
    or to explain a dataset's architecture before writing loading code.

    Returns:
    --------
    StructureGraph containing:
        - nodes: Every node with @id, type, name and parent @id.
        - edges: Directed edges as {source, target} @id pairs.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsonld_urlNoURL of a Croissant/GeoCroissant JSON-LD document.
jsonld_pathNoPath to a local Croissant/GeoCroissant file.
jsonld_contentNoRaw JSON string of a Croissant document.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
edgesYesDirected edges (source @id -> target @id).
nodesYesGraph nodes with @id, type and parent information.
edge_countYesNumber of edges in the graph.
node_countYesNumber of nodes in the graph.
Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It clearly explains what graph is built, what nodes and edges represent, and that it's the internal mlcroissant representation. This is sufficient for a read-only analysis tool, though it does not discuss failure modes or source-selection behavior.

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 well-structured and front-loaded with the core purpose, then detail, usage context, and return information. The Returns section is slightly redundant with the existing output schema, and the text is a bit long for what it conveys; but it earns its place by explaining the graph's internal semantics.

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?

The tool has no required parameters and an output schema, plus the description fully conveys the returned graph structure and usage context. The main gap is that three possible input sources are not explained as alternatives or prioritized, which is important for a tool that can take a URL, a path, or raw content.

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 already describes all three parameters (jsonld_url, jsonld_path, jsonld_content) with 100% coverage. The description adds no parameter-specific guidance beyond the schema. It could have clarified that the three inputs are presumably alternative sources and whether exactly one is required, but it is not required at baseline.

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 states a specific verb and resource: 'Extracts the internal structure graph of a Croissant document.' It further details the node and edge types, which clearly distinguishes this static-analysis/lineage tool from siblings like validate_croissant or get_records_preview.

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 says when to use the tool: reason about dataset lineage and dependencies, explain architecture before writing loading code, and answers questions like 'which files feed this field?' It provides clear context but does not name alternatives or exclusion conditions.

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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