opendosm_dataset_metaMetadata for one Malaysian dataset (data.gov.my): catalogue_id, data_as_of, last_updated, next_update, data_source, update_frequency, plus a sample row showing the available columns. Use this to inspect a series before pulling it, or to check freshness. Requires a dataset id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Dataset id, e.g. "cpi_headline". Required. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "cpi_headline"
+ },
+ {
+ "id": "fuelprice"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral context by listing the exact metadata fields and the presence of a sample row, which is 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?
Two sentences: first states what is returned, second gives usage guidance. No superfluous text, perfectly sized.
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?
Given the simple tool (1 param, no output schema, rich annotations), the description fully covers its purpose, usage, and output without gaps.
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?
Schema coverage is 100% with clear parameter description. The description mentions 'Requires a dataset id' but does not add new semantic meaning beyond the schema.
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 clearly states it returns metadata including specific fields and a sample row, and distinguishes itself from siblings like opendosm_get_dataset and opendosm_list_datasets by focusing on metadata inspection.
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 advises using this tool before pulling data or checking freshness, and notes the requirement of a dataset id. Could mention when not to use, but sufficient for context.
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.
The set includes three nearly-identical ask_pipeworx variants (base, beta, grounded) that differ only subtly, and deep_research overlaps with ask_pipeworx for multi-part queries. Several company/prediction tools also share adjacent purposes (entity_profile vs recent_changes vs compare_entities; polymarket_arbitrage vs polymarket_edges), though detailed descriptions help. Overall, an agent could mis-select between these overlapping tools.
Most tools follow a verb_noun or consistent prefix pattern (opendosm_*, polymarket_*), and the ask_pipeworx family is internally consistent. However, a few are noun phrases (entity_profile, ai_visibility_check, recent_alerts) and some verbs aren't uniform (list_datasets vs dataset_meta vs get_dataset). The mixture is readable but not fully consistent.
34 tools is well above the typical focused server range and includes a clearly redundant experimental variant (ask_pipeworx_beta) plus many loosely-related utility functions (memory, subscriptions, dependency scanning, llms.txt generation). While the broad scope justifies some size, the count feels excessive for the 'Opendosm My' name, which suggests a narrower Malaysian-statistics focus.
For the apparent overarching goal of a multi-domain research/query platform, the surface is quite complete: data lookup, deep research, entity comparison, claim verification, prediction-market analysis, memory, and subscriptions are all covered. The Malaysian OpenDOSM component itself has list/meta/get lifecycle. Minor gaps (e.g., no direct dataset search beyond curated lists, no way to execute arbitrary Pipeworx tools directly) are workable.