Skip to main content
Glama

Statbank Md

by io.github.pipeworx-io

Query Table

query_table
Read-onlyIdempotent

POST a PxWeb query to a Moldova National Bureau of Statistics (Statbank) table and return observations as json-stat2. body must be {query:[{code, selection:{filter,values}}], response:{format:'json-stat2'}}. PxWeb caps responses at ~10,000 cells — narrow each dimension's values using codes from table_meta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes{query: [{code, selection: {filter, values}}], response: {format: "json-stat2"}}
pathYesPath ending in the ".px" table id.

TDQS

A4.4/5.0
Behavior4/5

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

The description goes beyond the readOnlyHint/idempotentHint annotations by explaining the response size cap and the requirement to structure the body as a PxWeb query. It also discloses the return format json-stat2, adding behavioral context not present in 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?

Three sentences, each conveying essential information: the action, the required body format, and a critical constraint. No filler 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 query tool with no output schema, the description covers the request format, the response format (json-stat2), and a key limitation. It could detail error conditions or how to interpret json-stat2, but the core information for a standard query is present.

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%, but the description supplements it by explaining the body structure and emphasizing the need to narrow dimension values using codes from table_meta. This clarifies parameter semantics beyond the schema's property descriptions.

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 clearly states the action: 'POST a PxWeb query to a Moldova National Bureau of Statistics (Statbank) table and return observations as json-stat2.' It names the specific resource and output format, distinguishing it from sibling tools like table_meta.

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 implies when to use it: when you need observations from a PxWeb table. It explicitly directs users to 'narrow each dimension's values using codes from table_meta,' indicating a prerequisite and an alternative tool. It also gives a constraint ('PxWeb caps responses at ~10,000 cells') that informs usage decisions.

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

All Statbank Md 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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in scope, and the prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) share similar functions. While some tools are distinct, the boundaries between many are unclear.

Naming Consistency2/5

Names mix product-like identifiers (ask_pipeworx, deep_research), descriptive nouns (entity_profile, subjects), and inconsistent verb forms (query_table, resolve_entity, scan_competitor_ai_presence). No consistent verb_noun pattern is maintained across the set.

Tool Count2/5

With 34 tools, the server is overpopulated relative to its apparent purpose. The name 'Statbank Md' suggests a narrow statistical service, but only 3 tools are Statbank-specific; the rest form a sprawling general-purpose data toolkit. The count is far beyond what the core function needs.

Completeness3/5

For the Statbank subset, the surface is complete (browse subjects, get metadata, query data). As a general data research suite, it covers many domains (SEC, FDA, economics, prediction markets) but lacks execution/trading tools for prediction markets and has no bulk data export or analytics beyond excerpts. Notable gaps exist but many core workflows are covered.

Resources