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Glama

Datos Co

by io.github.pipeworx-io

Search Datasets

search_datasets
Read-onlyIdempotent

Search datasets published on Colombia's open-data portal datos.gov.co. Returns each dataset's Socrata 4x4 id (e.g. "gt2j-8ykr"), name, and description (Spanish). Use the id with dataset_columns and query_dataset. Query terms can be Spanish or English (e.g. "covid", "presupuesto", "contratos").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesFull-text search query (Spanish or English).
limitNoMax datasets to return (default 20).
offsetNoPagination offset (default 0).

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: +[
+  {
+    "q": "covid"
+  },
+  {
+    "limit": 10,
+    "offset": 0,
+    "q": "presupuesto"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by specifying that the search is full-text, works in Spanish/English, and returns a Socrata 4x4 id, name, and description in Spanish. No contradictions with 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?

The description is three sentences long, front-loaded with the primary action, and each sentence adds essential information. No fluff or redundancy.

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?

Given the presence of annotations and clear parameter descriptions, the description adequately explains the tool's purpose, input, output format, and follow-up actions. Missing details like pagination limits or error handling are minor for a search tool, and the description covers the most critical aspects.

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% with descriptions for all 3 parameters. The description adds meaning beyond schema by explaining the format of the returned id (e.g., 'gt2j-8ykr') and that the description is in Spanish. This helps the agent understand the output, which is not covered in the schema.

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?

Description clearly states the tool searches datasets on Colombia's open-data portal, returns specific fields (Socrata 4x4 id, name, description in Spanish), and instructs to use the id with sibling tools dataset_columns and query_dataset. It distinguishes itself from these siblings and provides concrete examples of query terms.

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?

Description tells when to use the tool (search datasets) and gives examples of query terms. It also directs to use the resulting id with other tools. However, it does not explicitly state when not to use this tool compared to other sibling tools like deep_research or compare_entities, but the context is clear enough for typical use.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct purposes, but several natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) occupy overlapping territory, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries, though the long descriptions help an agent differentiate.

Naming Consistency3/5

Names are consistently lowercase with underscores, and there are coherent subfamilies like ask_pipeworx*, polymarket_*, and scan_*. However, the overall set mixes conventions: verb_noun (query_dataset, resolve_entity), noun_noun (entity_profile, bet_research), adjective_noun (recent_changes), and bare verbs (remember, recall, forget), so no single predictable pattern governs the server.

Tool Count2/5

34 tools is a heavy surface for one server, including multiple meta/onboarding utilities (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending) and a large prediction-market subcluster. The count exceeds the 25-tool threshold where a tool set typically becomes unwieldy, and several tools could be consolidated or split into separate servers.

Completeness4/5

For a read-heavy data research platform, the surface is very complete: discovery, routed lookup, grounded verification, entity resolution, profiles, comparisons, change feeds, dataset querying, prediction-market research, and full memory/subscription lifecycles are all covered. Minor gaps exist, such as no explicit pipeworx:// URI read tool and no subscription update operation, but agents can work around them.

Resources