Skip to main content
Glama

Pangaea

by io.github.pipeworx-io

Facets

facets
Read-onlyIdempotent

Aggregate matching datasets by a facet — top values of a category field (e.g. "agg-mainTopic", "agg-author", "agg-method", "agg-location", "agg-campaign") with counts. Optionally scoped to a full-text query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoTop-N buckets (default 25).
fieldYesA keyword facet field, e.g. "agg-mainTopic", "agg-author".
queryNoOptional full-text scope for the aggregation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsNo
tookNo
_shardsNo
timed_outNo
aggregationsNoAggregation results

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "field": "source"
-  },
-  {
-    "field": "topic",
-    "q": "paleoclimate",
-    "size": 15
-  }
-]New value: +[
+  {
+    "field": "source"
+  },
+  {
+    "field": "topic",
+    "size": 15
+  }
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / field / description
      Added value: +"A keyword facet field, e.g. \"agg-mainTopic\", \"agg-author\"."
    • removedInput schema / properties / q
      Removed value: -{
-  "type": "string"
-}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Optional full-text scope for the aggregation.",
+  "type": "string"
+}
    • addedInput schema / properties / size / description
      Added value: +"Top-N buckets (default 25)."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, and open-world behavior. The description adds useful context beyond annotations by explaining that results are top values with counts and that the aggregation can be scoped by a full-text query. No contradiction with annotations.

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 a single, front-loaded sentence that conveys the action, output shape, and optional scoping parameter, followed by illustrative examples. There is no filler or redundant content.

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?

Given full schema coverage, a present output schema, and rich annotations, the description provides all essential information: what the tool returns, how to scope it, and examples of valid fields. No critical details are missing for selecting and invoking the tool.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that 'field' is a category field with concrete examples and by explaining that 'query' is an optional full-text scope, which enriches the bare schema definitions.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Aggregate matching datasets by a facet', then defines the output as top values with counts and gives concrete field examples. This clearly distinguishes the tool from sibling search/list tools by focusing on faceted aggregation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies this tool is for getting top category values with counts and notes that the full-text query is optional, providing context for when to use it. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it lacks formal exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Pangaea toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

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 Consistency3/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources