datasetsSearch dataset catalogue.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| offset | No |
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Result limit applied | |
| offset | No | Result offset applied | |
| results | No | Array of dataset results |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "crime"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "building permits"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Search results from Socrata catalog API",
+ "properties": {
+ "limit": {
+ "description": "Result limit applied",
+ "type": "number"
+ },
+ "offset": {
+ "description": "Result offset applied",
+ "type": "number"
+ },
+ "results": {
+ "description": "Array of dataset results",
+ "items": {
+ "description": "Dataset metadata",
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds no behavioral context beyond the implied 'search', such as pagination behavior or result format.
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 minimal (3 words). While concise, it lacks sufficient detail, making it under-specified rather than appropriately compact.
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?
With 3 undocumented parameters and no explanation of the dataset catalogue's scope or result structure, the description is inadequate for effective tool use, even though an output schema exists.
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 description coverage is 0%, meaning the schema provides no parameter descriptions. The description 'Search dataset catalogue' offers no clarification on the 'query', 'limit', or 'offset' parameters, leaving the agent without guidance on how to use them.
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 'Search dataset catalogue' clearly states the tool's verb and resource. However, it does not differentiate from sibling search tools like 'search_within' or 'deep_research', which may also search catalogues.
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?
No guidance on when to use this tool vs alternatives such as 'search_within' or 'query'. The description does not specify when not to use it or provide examples of appropriate use cases.
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.
Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/research; polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_edge_tracker all analyze prediction markets; entity_profile, compare_entities, and recent_changes overlap on company research. An agent could easily select the wrong one.
All names are lowercase snake_case, but the verb-noun convention is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some noun-first (entity_profile, polymarket_edges, recent_changes), and some are bare nouns (query, metadata, datasets). The pattern is readable but not uniform.
34 tools is excessive for the apparent scope, especially given the server name suggests a Chicago city-data focus while the vast majority of tools are generic data-research, prediction-market, and memory utilities. This feels like a kitchen-sink bundle rather than a focused, well-scoped toolset.
The tool surface is a disjointed collection covering querying, research, memory, subscriptions, and prediction markets, but it lacks coherent lifecycle coverage for any single domain. For the named 'Cityofchicago' purpose, there is almost no city-specific functionality, and even as a general data tool, obvious gaps remain (e.g., no direct dataset management or update/delete operations).