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Glama

Cso Ie

by io.github.pipeworx-io

List Datasets

list_datasets
Read-onlyIdempotent

Search the CSO catalog of ~12,600 Irish statistics tables. Returns matching tables with their matrix code (e.g. "CPM01"), label, dimensions, and last-updated date. The full catalog is large, so always pass a keyword query to narrow it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 200).
queryNoKeyword(s) matched against table label and matrix code, e.g. "consumer price", "population", "QLF". Case-insensitive.

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: +[
+  {
+    "query": "consumer price"
+  },
+  {
+    "limit": 20,
+    "query": "population"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds context about the large catalog size and the nature of the returned data (matrix code, label, dimensions, last-updated date), which goes beyond the annotations and helps set expectations.

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 concise sentences that front-load the core purpose and then provide a practical tip. Every word earns its place with no redundancy or irrelevant detail.

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?

For a simple search/list tool with no output schema, the description adequately covers the tool's purpose, output format, and a caveat about the large catalog. Combined with the rich annotations and schema, this provides sufficient context for an agent to use the tool 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?

The input schema already provides comprehensive descriptions for both parameters (100% coverage). The description adds value by emphasizing the query parameter as practically required ('always pass a keyword query'), which is not reflected in the schema's required field list.

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 verb 'Search' and the resource 'CSO catalog of ~12,600 Irish statistics tables,' and specifies the output fields (matrix code, label, dimensions, last-updated date), which distinguishes it from sibling tools like get_dataset or query_dataset.

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?

It provides clear usage guidance by stating 'The full catalog is large, so always pass a keyword query to narrow it,' which gives a strong recommendation for using the query parameter. However, it doesn't explicitly mention when not to use this tool or name alternative tools.

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

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TDQS

A3.8/5.0
Disambiguation3/5

Several tools have overlapping purposes: the four ask_pipeworx variants all route to the same 5,581 tools (ask_pipeworx_beta is currently identical to stable), and the six polymarket_* tools have subtle boundaries between research, edges, and arbitrage that could cause misselection. That said, the descriptions are unusually detailed, and non-overlapping clusters (CSO table tools, memory tools, subscription tools) are clearly distinct.

Naming Consistency3/5

All names are snake_case and mostly verb-first (get_dataset, resolve_entity, validate_claim), but conventions are inconsistent: polymarket_* and pipeworx_* are brand/noun-first while bet_research and ask_pipeworx put the verb first for the same domains, and some tools are pure nouns (entity_profile, recent_alerts). The pattern is readable but far from predictable.

Tool Count2/5

At 35 tools the server is well past the 25+ 'too many' threshold for a single MCP. Several tools don't earn their place: ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six polymarket tools plus four ask_pipeworx variants represent heavy redundancy for what are essentially two sub-domains.

Completeness4/5

The data-access core is covered end to end: discovery (discover_tools, list_datasets, suggest_questions), structured reads (ask_pipeworx, get_dataset, query_dataset), grounded verification (validate_claim, ask_pipeworx_grounded), comparison (compare_entities), change tracking (recent_changes), plus memory and subscription CRUD. Minor gaps: subscriptions can't be edited (only recreated) and unrelated utilities (generate_llms_txt, scan_dependency) dilute the focus rather than fill a real gap.

Resources