Skip to main content
Glama

Stat Ee

by io.github.pipeworx-io

Query Table

query_table
Read-onlyIdempotent

Pull ESTONIA official statistics figures from a Statistics Estonia (Statistikaamet) table — wages, population, GDP, prices, unemployment. body is a PxWeb query object; get valid dimension values from table_meta first. Filter selections to stay under the ~100k-cell limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes{query: [{code, selection: {filter, values}}], response: {format: "json-stat2"}}
pathYesTable path ending in .px, e.g. ".../RV021.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": "Aasta",
+          "selection": {
+            "filter": "item",
+            "values": [
+              "2023"
+            ]
+          }
+        }
+      ],
+      "response": {
+        "format": "json-stat2"
+      }
+    },
+    "path": "rahvastik/rahvastikunaitajad-ja-koosseis/rahvaarv-ja-rahvastiku-koosseis/RV021.PX"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds meaningful constraints: the body must be a PxWeb query object, values must come from table_meta, and there is a ~100k-cell limit. These are behavioral details beyond what annotations provide, with no contradiction.

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 two sentences, with the main purpose front-loaded. Every sentence adds unique value: the first covers what the tool does, the second covers how to use it correctly (body semantics and limit). No filler or redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with nested body object and no output schema, the description covers the core aspects: purpose, input format, prerequisite, and a critical constraint. It doesn't explicitly describe return values, but the example in the schema includes response formatting, and the tool's purpose implies a return of statistical figures. The overall context is sufficiently complete for an agent to use it correctly.

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% for both parameters, but the description adds value by explaining that body is a PxWeb query object and directing users to table_meta for valid dimension values. This clarifies how to construct the body parameter beyond the schema's minimal description.

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 states a specific action ('Pull ESTONIA official statistics figures') with a clear resource (Statistics Estonia table) and enumerates example topics (wages, population, GDP, prices, unemployment). It distinguishes itself from siblings by focusing on querying data from a table, as opposed to table discovery or metadata.

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 gives an explicit workflow prerequisite: 'get valid dimension values from table_meta first', which tells the agent to use the sibling tool first. It also warns about the ~100k-cell limit, guiding selection filtering. It doesn't explicitly state when not to use this tool vs alternatives, but the workflow guidance is strong.

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

All Stat Ee 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

A3.9/5.0
Disambiguation3/5

Most tools have fairly distinct action/resource targets and the descriptions carefully separate entry points like ask_pipeworx, deep_research, and ask_pipeworx_grounded. However, ask_pipeworx_beta is explicitly an identical clone of ask_pipeworx right now, and a few related pairs (ai_visibility_check vs scan_competitor_ai_presence, stat_ee_find_table fetch_latest vs estonia_average_wage) add ambiguity.

Naming Consistency3/5

Names are consistently lowercase snake_case and verb-led names like resolve_entity, query_table, and suggest_questions are clear. But the set mixes conventions: bare nouns (subjects, recall, forget), adjective-noun phrases (recent_alerts, recent_changes), no-verb names (estonia_average_wage, table_meta), and multiple prefixes (pipeworx_*, polymarket_*, stat_ee_*). It is readable but not a single predictable pattern.

Tool Count2/5

36 tools is well above the 15-tool threshold for a well-scoped server, and the set spans many unrelated domains: Estonian statistics, Pipeworx research, Polymarket betting, AI visibility, npm scanning, memory, and subscriptions. There is also clear redundancy (ask_pipeworx_beta duplicates ask_pipeworx, ai_visibility_check could be folded into scan_competitor_ai_presence). This feels scattered for a server named 'Stat Ee'.

Completeness4/5

For the broad data-research/agent-assistant purpose, key workflows are well covered: discovery/query/grounded/deep research, entity resolution/profile/compare/validate/recent changes, complete memory CRUD, subscription CRUD with alert feeds, and a full Polymarket edge/arb/fill-risk suite. The main gap is not missing operations within these workflows but rather the overall scope being too broad and unfocused.

Resources