dataverseDataverse (collection) metadata by alias or id.
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Dataverse metadata | |
| status | No | API response status |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "identifier": "harvard"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Dataverse collection metadata",
+ "properties": {
+ "data": {
+ "description": "Dataverse metadata",
+ "properties": {
+ "affiliation": {
+ "description": "Affiliated institution",
+ "type": "string"
+ },
+ "dataverseContacts": {
+ "description": "Contact information",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "description": {
+ "description": "Dataverse description",
+ "type": "string"
+ },
+ "id": {
+ "description": "Dataverse ID",
+ "type": "integer"
+ },
+ "identifier": {
+ "description": "Dataverse alias or identifier",
+ "type": "string"
+ },
+ "name": {
+ "description": "Dataverse name",
+ "type": "string"
+ },
+ "type": {
+ "description": "Dataverse type",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "status": {
+ "description": "API response status",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety info (read-only, idempotent). The description adds the 'by alias or id' scope but no further behavioral traits. Adequate but minimal extra value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no filler. Could be considered too minimal but still conveys core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the tool is simple, the description is minimally complete. However, it does not explain what a dataverse is or how the identifier relates to the alias/ID distinction.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description says the parameter is an alias or ID, but no format or example details beyond the schema's example. Insufficient compensation for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves metadata for a dataverse (collection) by alias or ID. It is specific enough to distinguish from sibling tools like 'dataset', though it could elaborate on what metadata includes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'dataset' or 'search'. Missing when-not scenarios and alternative tool suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool pairs have overlapping purposes, notably ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. Also, remember/recall/forget overlap with general memory operations, and multiple polymarket tools overlap in edge detection. While the descriptions attempt to differentiate, an agent will frequently need to choose between nearly identical tools (e.g., ask_pipeworx vs. ask_pipeworx_beta).
Naming conventions are mixed: snake_case (ai_visibility_check, compare_entities), camelCase (ask_pipeworx, generate_llms_txt), and inconsistent verb usage (some start with verbs like 'search', others with nouns like 'dataset'). The polymarket and pipeworx prefixes are helpful, but overall patterns are unpredictable.
With 35 tools, this server has a very large surface area. While the domain is broad (Harvard Dataverse + Pipeworx data + Polymarket), the count feels heavy and includes many near-duplicate tools (ask_pipeworx variants) and niche tools that inflate the total. Many agents would benefit from a smaller, more focused set.
The Dataverse subset captures file metadata and search but lacks direct download/upload capabilities, causing dead ends for users who want to access actual data. The Polymarket subset lacks the ability to actually place orders despite extensive edge analysis. The Pipeworx subset covers many data queries but feels unfocused. Overall, there are notable gaps given the stated scope of the server.