articleFetch a single PLOS article record by DOI (e.g. 10.1371/journal.pone.0153207), returning title, authors, publication date, journal, and abstract.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | e.g. "10.1371/journal.pone.0153207" |
| Name | Required | Description | Default |
|---|---|---|---|
| response | No | ||
| responseHeader | No | Response metadata from Solr |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "doi": "10.1371/journal.pone.0153207"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "response": {
+ "properties": {
+ "docs": {
+ "description": "Array of article documents",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Article DOI",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "numFound": {
+ "description": "Total number of matching documents",
+ "type": "number"
+ },
+ "start": {
+ "description": "Starting position of results",
+ "type": "number"
+ }
+ },
+ "type": "object"
+ },
+ "responseHeader": {
+ "description": "Response metadata from Solr",
+ "type": "object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds value by listing the exact return fields, which helps the agent know what to expect. It doesn't discuss edge cases like invalid DOIs, but given annotations and simplicity, it provides sufficient behavioral context.
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 a single, concise sentence that front-loads the main action (fetch by DOI) and lists return fields in a compact list. No wasted words, appropriate length for a simple tool.
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?
With a single parameter, clear annotations, and an output schema present, the description fully covers the tool's behavior and return values. It is complete for a simple read-only fetch operation.
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?
Schema coverage is 100% and the schema already describes the 'doi' parameter with an example. The description repeats a similar example in prose, adding no meaning beyond what the schema already provides. This matches the baseline for high schema coverage.
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 the tool fetches a single PLOS article record by DOI, and lists the returned fields (title, authors, publication date, journal, abstract). This is a specific verb+resource+scope, and it distinguishes from sibling search/recall tools by targeting a single known DOI.
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 implies the tool should be used when the agent has a specific DOI and needs the article record. However, it does not explicitly state when not to use it or mention alternatives like search tools. The usage context is clear but not explicitly contrasted with siblings.
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 tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in function, while compare_entities and entity_profile both fan out across data sources. The PLOS-specific tools (article, search, recent) are distinct, but the broader set creates confusion about which entry point to use for general data questions.
The naming is a mix of single-word nouns (article, recent), verb phrases (search_authored_by, validate_claim), and adjective_noun constructions (recent_changes, recent_alerts). While most multi-word names use snake_case, the lack of a consistent prefix or verb_pattern (e.g., some start with action verbs, others with data categories like polymarket_ or pipeworx_) makes the set feel inconsistent.
At 35 tools, the server is heavily over-scoped for a PLOS journal interface—only 4 tools (article, search, search_authored_by, recent) actually relate to PLOS. The remaining 31 tools form a broad Pipeworx/Polymarket data platform, which suggests the server is trying to do far more than its name implies.
For the stated domain (PLOS), the surface is thin: basic search, fetch, recent, and author search lack advanced features like citation metrics, journal browsing, or full-text download links. Conversely, the Pipeworx tools are extensive but unrelated to PLOS, so the set is simultaneously over-complete in unrelated areas and incomplete for its apparent purpose.