table_metaFetch dimension definitions and valid coded values for a Moldova Statbank PxWeb table. Path must end in the '.px' table id (e.g. '20 Populatia si procesele demografice/POP010/POPro/POP010100rcl.px'). Returns dimensions with codes and value lists — use these to build the selection body for query_table.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | e.g. "20 Populatia si procesele demografice/POP010/POPro/POP010100rcl.px" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint/idempotentHint, and the description adds the path format constraint and states the return shape (dimensions with codes and value lists). It doesn't discuss auth/rate limits but provides meaningful behavioral context 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?
Three sentences, front-loaded with the core purpose, followed by a practical path example and downstream usage. No filler.
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 one-parameter metadata fetch with no output schema, the description adequately covers the path format, return content, and how to use the result with query_table. It might mention error cases or if the table must already exist, but overall it's sufficient.
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 input schema gives only an example, but the description clarifies what the path must be (ending in '.px') and why. This adds real semantic value beyond the schema's terse example, so the single parameter is well explained.
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 opens with a specific verb ('Fetch') and resource ('dimension definitions and valid coded values for a Moldova Statbank PxWeb table'), clearly distinguishing it from the sibling query_table which would fetch data. It even ties usage to query_table, so the tool's role is unambiguous.
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 provides a concrete path requirement (must end in '.px' table id) and explains that output is used to build query_table's selection body. This gives clear context on when to use it, though it doesn't explicitly list exclusions or alternatives beyond query_table.
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 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.
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.
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.
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.