recentMost recently published PANGAEA datasets (newest DOI registrations first).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Only datasets registered within the last N days (optional). | |
| size | No | How many (default 20, max 50). |
| Name | Required | Description | Default |
|---|---|---|---|
| hits | No | ||
| took | No | ||
| _shards | No | ||
| timed_out | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / days / descriptionAdded value: +"Only datasets registered within the last N days (optional)."Input schema / properties / size / descriptionAdded value: +"How many (default 20, max 50)."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the sorting behavior ('newest DOI registrations first') and the domain (PANGAEA), which are not covered by annotations. This adds contextual behavioral information beyond the safety profile.
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 efficiently conveys the tool's purpose and behavior. Every word is meaningful, with no unnecessary filler or repetition.
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 simple list tool with 2 optional parameters, an output schema, and rich annotations, the description provides sufficient context. It states the resource and ordering, while the schema covers parameter details and defaults. No additional return format or pagination explanation is needed given the output schema.
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 description coverage is 100%, so the schema fully documents both parameters ('days' and 'size'). The description does not add any additional meaning beyond the schema, so a baseline of 3 is appropriate.
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 ('published') and resource ('PANGAEA datasets'), and clarifies the ordering ('newest DOI registrations first'). This clearly distinguishes it from sibling tools like 'dataset' or 'search', which target specific datasets or general search.
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 use when the user needs recently published PANGAEA datasets, but does not explicitly mention alternatives or exclusions. It provides clear context ('most recently published') without saying 'use X instead', making it a clear context but not explicit alternative guidance.
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 heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and validate_claim both perform claim verification, and polymarket_edges and polymarket_arbitrage both surface trading opportunities. While descriptions are detailed, the boundaries are fuzzy and agents could easily select the wrong tool.
Naming mixes verb-first (search, subscribe, recall, validate_claim) with noun-first (dataset, facets, recent, entity_profile) conventions, and some names are just adjectives or nouns. Consistent prefixed groups exist (polymarket_*, ask_pipeworx_*), but the overall style is inconsistent and the server name 'Pangaea' doesn't align with the dominant 'pipeworx' prefix.
36 tools is excessive for a coherent set, especially given the server bundles unrelated domains (earth science, general data research, prediction markets, memory, utilities). Several tools are redundant (e.g., ask_pipeworx_beta duplicates ask_pipeworx), and many are niche (ai_visibility_check, generate_llms_txt, scan_dependency) that don't fit the apparent primary purpose.
The PANGAEA dataset surface covers search, retrieval by ID/DOI, recent, and facets, but lacks export or citation tools. The Pipeworx research tools are broad, but prediction market access has no simple market-price query (only analysis-oriented tools), and there are notable gaps in lifecycle coverage for some subdomains.