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Dataverse Harvard

by io.github.pipeworx-io
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Dataset Files

dataset_files
Read-onlyIdempotent

List all files in the latest version of a Harvard Dataverse dataset identified by its DOI persistent ID (e.g. "doi:10.7910/DVN/..."), returning file names, content types, sizes, and download URLs. For files Dataverse has TABULAR-INGESTED (CSV/DTA/SAV/POR under its ingest size limit), also returns exact COLUMN / VARIABLE NAMES and labels from Dataverse's DDI metadata (variables_by_file). If a dataset has no tabular-ingested files (common for very large files, or non-tabular formats), column names are not available from Dataverse at all — the response says so explicitly rather than silently omitting them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
persistent_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoDataset files data
statusNoAPI response status

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "persistent_id": "doi:10.7910/DVN/EXAMPLE"
-  }
-]New value: +[
+  {
+    "persistent_id": "doi:10.7910/DVN/DUWBBU"
+  }
+]
  2. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "persistent_id": "doi:10.7910/DVN/EXAMPLE"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Files in a dataset",
+  "properties": {
+    "data": {
+      "description": "Dataset files data",
+      "properties": {
+        "files": {
+          "description": "Array of files in dataset",
+          "items": {
+            "description": "File metadata",
+            "properties": {
+              "datafile": {
+                "description": "Datafile details",
+                "type": "object"
+              }
+            },
+            "type": "object"
+          },
+          "type": "array"
+        }
+      },
+      "type": "object"
+    },
+    "status": {
+      "description": "API response status",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: it specifies that only the latest version is considered, enumerates returned fields, explains the conditional availability of column names for TABULAR-INGESTED files, and explicitly states that the response will say column names are unavailable rather than silently omitting them. This is rich, accurate context that helps an agent predict behavior.

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 three sentences with no fluff. The main action and output fields are front-loaded; the conditional column-name detail and the explicit-absence caveat each add necessary information that would otherwise be opaque. Every sentence earns its place.

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?

With an output schema present, return values do not need explanation. The description covers the sole input parameter, the behavioral scope (latest version), the conditional enrichment for tabular files, and the failure/edge mode for non-tabular datasets. Nothing critical for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the only parameter is an undocumented string. The description fully compensates by identifying persistent_id as the DOI persistent ID, providing a concrete example (doi:10.7910/DVN/...), and clarifying that it identifies the dataset. This is exactly the semantic information an agent needs to invoke the tool correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'List all files in the latest version of a Harvard Dataverse dataset identified by its DOI persistent ID.' It also enumerates the exact output fields, making the tool's function unmistakable. However, it does not explicitly differentiate itself from sibling tools like 'dataset' or 'dataverse,' so it stops short of a perfect score.

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 usage context is implied: an agent would infer to use this tool when needing a file listing from a specific Dataverse dataset, including column names for tabular-ingested files. Yet there is no explicit guidance about when not to use it or which sibling tool to prefer for dataset-level metadata. No alternatives or exclusions are mentioned.

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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