dallas_datasetsSearch the Dallas open-data catalogue (www.dallasopendata.com) for datasets by keyword. Returns dataset names, descriptions, and Socrata resource ids to use with dallas_query.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s). | |
| offset | No | Offset for paging. | |
| _apiKey | No | Optional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-data-dallas-api-key",
+ "query": "permits construction"
+ },
+ {
+ "_apiKey": "your-data-dallas-api-key",
+ "limit": 10,
+ "query": "crime incidents"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds useful context about returned content (names, descriptions, Socrata ids) and links to dallas_query, enhancing transparency beyond 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 with no waste. The first sentence states the action and source, the second describes the output. Front-loaded and efficient.
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 no output schema, the description explains return format (names, descriptions, resource ids) and mentions the source URL. Parameter details are covered in schema. Adequate for a search tool with good annotations.
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 each parameter already described in the input schema (limit, query, offset, _apiKey). The description does not add new semantic meaning beyond what is in the schema, so baseline score of 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 description clearly states the tool searches the Dallas open-data catalogue for datasets by keyword and specifies the return fields (names, descriptions, Socrata resource ids). It distinguishes from siblings like dallas_query and dallas_recent by mentioning the resource ids are for use with dallas_query.
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 implies usage when one needs to find datasets before querying, as it returns resource ids for use with dallas_query. No explicit when-not or alternatives, but the context is sufficient given the sibling tools.
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.
Most tools have clearly distinct purposes, with a few overlapping pairs (e.g., ask_pipeworx vs ask_pipeworx_grounded, multiple polymarket tools) that could cause mild confusion, but descriptions adequately differentiate them.
Tool names consistently use snake_case, but the verb_noun pattern is not consistently applied; some names are noun_noun (dallas_datasets, bet_research) or adjective_noun (ai_visibility_check), creating a mixed nomenclature.
With 33 tools, the server exceeds the typical well-scoped range. While the broad data domain justifies many tools, the count feels heavy and would benefit from consolidation of related functions.
The tool set covers a wide array of domains (company data, drugs, economics, prediction markets, memory, subscriptions) with only minor gaps (e.g., no direct web search tool, as ask_pipeworx mostly covers it). Overall, it is comprehensive for its purpose.