datasetFull metadata for one Harvard Dataverse dataset by its DOI persistent id: title, authors, abstract, subject, keywords, version and publication date. The id must come from search — DOIs cannot be guessed.
| Name | Required | Description | Default |
|---|---|---|---|
| persistent_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Dataset metadata | |
| status | No | API response status |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesPrevious value: -[
- {
- "persistent_id": "doi:10.7910/DVN/EXAMPLE"
- }
-]New value: +[
+ {
+ "persistent_id": "doi:10.7910/DVN/DUWBBU"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "persistent_id": "doi:10.7910/DVN/EXAMPLE"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Dataset metadata response",
+ "properties": {
+ "data": {
+ "description": "Dataset metadata",
+ "properties": {
+ "authority": {
+ "description": "Authority (e.g., 10.7910)",
+ "type": "string"
+ },
+ "datasetVersion": {
+ "description": "Dataset version details",
+ "type": "object"
+ },
+ "id": {
+ "description": "Dataset ID",
+ "type": "integer"
+ },
+ "identifier": {
+ "description": "Dataset persistent identifier",
+ "type": "string"
+ },
+ "persistentUrl": {
+ "description": "Persistent URL to dataset",
+ "type": "string"
+ },
+ "protocol": {
+ "description": "Protocol (e.g., doi)",
+ "type": "string"
+ },
+ "publicationDate": {
+ "description": "Publication date",
+ "type": "string"
+ },
+ "publisher": {
+ "description": "Publisher name",
+ "type": "string"
+ },
+ "storageSize": {
+ "description": "Storage size in bytes",
+ "type": "integer"
+ },
+ "subjects": {
+ "description": "Subject tags",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ }
+ },
+ "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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds a behavioral constraint (DOIs cannot be guessed) and clarifies the data source (Harvard Dataverse), but it does not elaborate on response fidelity, potential errors, or rate limits. Given the annotations, the description's contribution beyond them is modest but not redundant.
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?
The description is two sentences, with the core functionality (what it returns) front-loaded and the constraint (DOI must come from search) stated afterward. Every phrase earns its place; there is no fluff or repetition of schema fields.
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?
For a single-parameter tool with an output schema present, the description covers the essential aspects: what the tool does, what parameter it takes, and the prerequisite for using it. It does not explain error conditions or pagination, but given the output schema and the simplicity of the tool, these are not critical gaps. The description is sufficiently complete for reliable invocation.
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?
The schema provides no description for the persistent_id parameter (coverage 0%), so the description carries the full burden. It explains that the parameter is a DOI persistent id and specifies that it must come from search. This gives the parameter clear meaning and a sourcing rule. While it doesn't mention the format, the schema example shows a valid DOI format, so the description sufficiently compensates for the schema's silence.
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 states a specific verb ('get' implied by 'Full metadata'), a specific resource ('one Harvard Dataverse dataset'), and identifies the key identifier (DOI persistent id). It enumerates the returned fields (title, authors, abstract, subject, keywords, version, publication date), which distinguishes it from sibling tools like dataset_files (which lists files) and search (which finds datasets).
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?
The description provides clear usage guidance: the DOI must come from search ('The id must come from search — DOIs cannot be guessed'). This implicitly tells the agent to use the search tool first and not to attempt constructing or guessing DOIs. It does not explicitly name alternatives, but the directive is unambiguous and actionable.
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.