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Glama

Stat Gl

by io.github.pipeworx-io

Query Table

query_table
Read-onlyIdempotent

POST a PxWeb query to a Statistics Greenland table and return observations as json-stat2. body must be {query:[{code, selection:{filter,values}}], response:{format:'json-stat2'}}. An empty query ({query:[]}) requests the full table but may be rejected for large tables — narrow selections using codes from table_meta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes{query: [{code, selection: {filter, values}}], response: {format: "json-stat2"}}
pathYese.g. "BE/BE01/BEXSAT1.PX"

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "body": {
+      "query": [
+        {
+          "code": "Tid",
+          "selection": {
+            "filter": "item",
+            "values": [
+              "2023",
+              "2024"
+            ]
+          }
+        }
+      ],
+      "response": {
+        "format": "json-stat2"
+      }
+    },
+    "path": "BE/BE01/BEXSAT1.PX"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context: the POST requirement, the exact body structure, the response format json-stat2, and the risk of large-table rejection for empty queries. 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?

Two sentences deliver the essential purpose, body template, and usage caution. No filler words; every clause contributes actionable guidance.

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?

The tool returns json-stat2 but has no output schema, so the description's mention of 'observations as json-stat2' covers the return contract. It also explains the body format, path example, large-table rejection risk, and recommends table_meta for codes, making the description self-sufficient for a query tool.

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

Parameters5/5

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

Even though schema coverage is 100%, the description enriches both parameters: it spells out the exact body schema '{query:[{code, selection:{filter,values}}], response:{format:"json-stat2"}}' and clarifies the path with an example. It also explains behavior (empty query behavior) not present in the schema.

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-resource pair: 'POST a PxWeb query to a Statistics Greenland table and return observations as json-stat2.' It clearly identifies the operation, target, and output format, distinguishing it from sibling tools like table_meta (metadata vs. data).

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?

'Narrow selections using codes from table_meta' explicitly directs the agent to a sibling tool for codes and warns against empty queries for large tables. This provides practical usage context, though it does not fully enumerate when to use query_table over competing tools.

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

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TDQS

A3.8/5.0
Disambiguation2/5

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 Consistency2/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources