get_datasetDataset metadata + column schema.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Dataset ID (e.g. "d_8b84c4ee58e3cfc0ece0d773c8ca6abc") |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "dataset_id": "d_8b84c4ee58e3cfc0ece0d773c8ca6abc"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Dataset metadata and column schema",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds no behavioral context beyond stating the output, so it adds minimal value. No mention of error handling, auth, or rate limits.
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?
The description is extremely concise at 4 words, front-loading the core purpose. It is not verbose, but slightly more context could be beneficial without being wasteful.
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 tool has an output schema (not shown) and only one simple parameter, the description is adequate. It does not need to explain return values due to output schema. However, it could mention that the dataset must exist or that errors are returned for invalid IDs.
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% and schema describes dataset_id. The description does not add any parameter details beyond what the schema already provides, so baseline 3 is appropriate.
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 title 'Get Dataset' and description 'Dataset metadata + column schema' clearly state the verb (get) and resource (dataset metadata and schema). It distinguishes from sibling tools like 'search_datasets' and 'query_dataset' by focusing on retrieving a single dataset's details.
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 does not provide explicit when-to-use or alternatives guidance. However, the sibling tool names imply this is for fetching metadata of a known dataset, contrasting with search or query tools. No exclusions or prerequisites are mentioned.
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.
Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the Polymarket and company-research toolsets overlap significantly (bet_research vs polymarket_edges, entity_profile vs compare_entities vs recent_changes). Even with strong descriptions, an agent can easily misselect among these near-duplicate entry points.
Names are readable but mix conventions: verb_noun forms (search_datasets, query_dataset, generate_llms_txt, validate_claim) coexist with noun/adjective forms (air_quality_pm25, taxi_availability, entity_profile, polymarket_edges). There is no single predictable pattern, though the domain-prefix style for Singapore data tools is consistent.
40 tools is far too many for a server nominally scoped to Singapore government data. The bulk of the surface is a general-purpose Pipeworx/prediction-market/research toolkit that has nothing to do with Data Gov Sg, so the actual Singapore dataset tools are buried under dozens of unrelated capabilities.
For the core data.gov.sg use case, the surface is solid: search_datasets, get_dataset, and query_dataset cover dataset discovery and retrieval, supplemented by live-data tools (weather_now, air_quality_psi, traffic_incidents, taxi_availability, uv_index). The broader Pipeworx side also includes helpful auxiliary lifecycle tools like discover, subscribe, recent_alerts, memory, and feedback, so there are no critical dead ends.