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Glama

Statfin Fi

by io.github.pipeworx-io

Table Meta

table_meta
Read-onlyIdempotent

List the dimensions and every valid value of one Statistics Finland (StatFin) PxWeb table of Finnish official statistics — use it to see exactly what a table breaks down by before slicing it, or when query_table reports that a value matched nothing. StatFin dimension codes are native Finnish and cannot be guessed from the English table title: "vkour/15ig.px" uses "ikaryhma_10_20180101" for age (whose total is "15-", not "SSS"), "sukupuoli_9_20180101" for gender, "syntypera_101_20180101" for origin, "timeperiod_y" for year, and contentscode values such as "kaste5T8" (population with a tertiary level qualification). path is "folder/table.px" using the bare 4-character table id, e.g. "vkour/15ig.px" or "khi/11xs.px".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesfolder/table.px with the bare 4-character table id, e.g. "vkour/15ig.px" or "khi/11xs.px". Find ids with subjects.

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "path": "khi/statfin_khi_pxt_11xs.px"
-  }
-]New value: +[
+  {
+    "path": "vkour/15ig.px"
+  }
+]
    • changedInput schema / properties / path / description
      Previous value: -"e.g. \"khi/statfin_khi_pxt_11xs.px\" (folder/table.px)"New value: +"folder/table.px with the bare 4-character table id, e.g. \"vkour/15ig.px\" or \"khi/11xs.px\". Find ids with subjects."
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "path": "khi/statfin_khi_pxt_11xs.px"
+  }
+]
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description explains that the tool returns all valid values and that dimension codes are native Finnish and cannot be derived from English titles. This gives agents a clear expectation of the output's nature and the necessity of the path parameter, adding significant behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than typical but each sentence adds necessary context (purpose, usage timing, code examples, path format). The purpose is front-loaded, and the detail is warranted given the non-obvious Finnish codes. It is somewhat verbose but not wasteful.

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?

With only one parameter, no output schema, and rich annotations, the description fully covers the tool's functionality, including examples and important cultural background. An agent can call it correctly without needing further specification.

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?

The schema already documents 'path' with examples and description covering 100% of the parameter. The description reinforces this with additional examples and explains why the path format matters (bare 4-character id) and that it points to a specific table, adding value beyond the schema baseline.

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 precise action ('List the dimensions and every valid value') on a specific resource (a StatFin PxWeb table), and clarifies what the tool is for (to inspect breakdowns before slicing, or when query_table returns no matches). It clearly distinguishes this metadata-exploration tool from data-fetching siblings like query_table.

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

Usage Guidelines5/5

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

It explicitly states when to use it ('before slicing it, or when query_table reports that a value matched nothing') and gives context on the Finnish dimension codes, which prevents misuse. It also indirectly points to the 'subjects' tool for finding table ids (in the schema example), offering clear guidance on the workflow.

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

All Statfin Fi toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose (beta explicitly 'currently matches ask_pipeworx exactly'), and the five polymarket_* tools plus bet_research create a dense cluster an agent must pick through. The descriptions are unusually detailed and do differentiate them, but the boundaries between the ask_pipeworx variants and between bet_research/polymarket_edges/arbitrage remain easy to misselect.

Naming Consistency4/5

All names are lowercase snake_case and mostly follow verb_noun or domain-prefix patterns (ask_pipeworx, polymarket_edges, list_subscriptions, resolve_entity). Minor deviations exist: subjects and table_meta are bare nouns rather than verbs, the ask_pipeworx family uses an ask_ prefix while the closely related deep_research does not, and entity appears as both a prefix (entity_profile) and a suffix (resolve_entity).

Tool Count2/5

34 tools is well above the 25-tool threshold for 'too many,' and the mismatch is sharpened by the server name 'Statfin Fi': only 3 of 34 tools (query_table, subjects, table_meta) actually relate to Statistics Finland, while the rest are a sprawling multi-domain platform covering prediction markets, AI visibility, npm packages, memory, and subscriptions. The count is appropriate for a general data platform but not for the apparent StatFin scope, making the surface feel bloated and unfocused.

Completeness4/5

The platform covers the full research lifecycle: discovery (discover_tools, suggest_questions), identifier resolution (resolve_entity), lookups (ask_pipeworx, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), monitoring (subscribe, recent_alerts, recent_changes), and memory (remember/recall/forget), with no obvious dead ends. Minor gaps exist — there is no keyword search across the StatFin catalog (browse-only via subjects), and one-off tools like generate_llms_txt and scan_dependency feel bolted on rather than part of a coherent domain.

Resources