subjectsBrowse the Statistics Greenland (Grønlands Statistik) PxWeb subject tree under the /Greenland database. Empty path returns root folders (type 'l') and tables (type 't'); supply a sub-path like 'BE/BE01' to drill deeper. Table IDs carry a '.px' or '.PX' suffix — pass verbatim to table_meta or query_table.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Sub-path under /Greenland/ (default empty = root). e.g. "BE/BE01" |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "path": ""
+ },
+ {
+ "path": "BE/BE01"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds context by disclosing that an empty path returns root folders of type 'l' and tables of type 't', and that a sub-path drills deeper. It also notes the '.px' suffix, providing useful behavioral detail beyond the annotations.
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, front-loaded with the primary purpose, and each sentence adds necessary detail. It is concise and well-structured, with no wasted words.
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 navigation tool with one optional parameter and no output schema, the description covers the essential behavior: root vs sub-path, the meaning of table IDs, and how to proceed with other tools. It could mention the return format more explicitly, but the existing detail is sufficient given the tool's simplicity.
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 already covers the 'path' parameter fully (100% coverage), including the default and an example. The description repeats this information and adds the behavior of empty vs non-empty paths, but doesn't significantly enhance the meaning of the parameter itself beyond what the schema provides. Thus the 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 clearly states the tool 'browse the Statistics Greenland PxWeb subject tree', using a specific verb and resource. It distinguishes itself from siblings by explicitly mentioning how table IDs should be passed to table_meta or query_table, making its role as a navigation tool clear.
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 gives usage guidance by explaining the root vs sub-path behavior and providing an example like 'BE/BE01'. It also tells the agent to pass table IDs to table_meta or query_table, indicating alternatives, though it doesn't explicitly frame when to use subjects vs those tools.
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.
Multiple tools have unclear boundaries: ask_pipeworx_beta currently behaves identically to ask_pipeworx, and the five Polymarket tools (arbitrage, edges, bet_research, fill_risk, edge_tracker) overlap heavily in the 'should I bet on X' use case. The detailed descriptions help, but an agent could easily misselect between these clusters.
Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities, resolve_entity), noun_noun (entity_profile, table_meta, polymarket_edges), single verbs (remember, forget, recall), and brand-prefixed compounds (pipeworx_trending, polymarket_kalshi_spread) are all mixed together. The server name 'Stat Gl' also doesn't align with the Pipeworx-heavy tool set.
34 tools is well beyond the 25+ threshold that signals an oversized surface, and the set spans disparate domains: data querying, prediction markets, entity research, memory, subscriptions, and even niche utilities like generate_llms_txt and scan_dependency. While each tool has a described purpose, the count feels bloated for a coherent server.
Within its actual domains, coverage is strong: query, grounded verification, deep research, claim validation, entity profiles, comparisons, memory CRUD, and subscription lifecycle are all present, plus a complete Statistics Greenland browse/schema/query trio. Minor gaps exist (e.g., limited subscription event types, US-centric company profiles), but agents can typically find a working path without dead ends.