table_metaFetch dimension definitions and valid coded values for a Statistics Greenland PxWeb table. Path must be the full sub-path ending in '.px'/'.PX' (e.g. 'BE/BE01/BEXSAT1.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. "BE/BE01/BEXSAT1.PX" |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "path": "BE/BE01/BEXSAT1.PX"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds crucial behavioral context: the path must end in .px/.PX, that it returns dimensions with codes and value lists, and how the output is consumed. No contradictions.
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?
Two sentences, each earning its place: first states the core purpose, second provides path constraints and usage guidance. No filler or redundancy.
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 simple tool with one parameter, good annotations, and no output schema, the description fully covers what it returns, how to invoke it, and how to use the result. Nothing important is missing.
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 schema covers the single 'path' parameter, and the description enriches it by specifying the full sub-path requirement and file extension constraint. This is essential information beyond the schema's generic example.
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 uses a specific verb ('Fetch') and identifies the exact resource (dimension definitions and valid coded values for a Statistics Greenland PxWeb table). It clearly distinguishes from the sibling query_table by stating the output is used to build its selection body.
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?
Explicitly states when to use the tool (before query_table, to build its selection body) and provides a precise path format requirement with an example. The relationship to the sibling tool makes the intended workflow clear.
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 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 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.
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.
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.