datasetsSearch the Baton Rouge Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "crime"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "budget"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and behavior. The description adds value by specifying the returned fields and the expected workflow, but does not elaborate on authorization 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 concise, two sentences long, with the action and resource front-loaded. Every sentence serves a purpose: defining the search and explaining the output. No wasted words.
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 rich annotations, fully documented input schema, and lack of output schema, the description provides complete context: it explains the tool's purpose, return value, and next step, leaving no critical gaps for an agent.
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 100%, so the baseline is 3. The description does not add new details about parameters beyond what the schema already provides, but it correctly implies the role of 'query' as the keyword.
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 action 'search', the resource 'open datasets', the source 'Baton Rouge Open Data catalog', and the specific output fields. It distinguishes itself by specifying the catalog's geographic scope, which is unique among siblings.
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 searching for datasets by keyword and hints at a follow-up action (pass resource_id to query/metadata), but it does not explicitly state when to use this tool versus alternatives or provide exclusions.
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 are clearly distinct, but the ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are near-identical routers (beta currently matches stable exactly), and several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping use cases. Detailed descriptions mitigate but don't fully eliminate the risk of an agent picking the wrong member of a cluster.
Names are consistently snake_case and mostly follow a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, subscribe). Minor deviations like bare nouns (datasets, metadata) and bare verbs (remember, recall, forget), plus the branded ask_pipeworx variants, keep it from a perfect pattern but the style remains predictable.
34 tools is well above the 25+ threshold and feels heavy even for a broad research platform; many are meta/utility tools (pipeworx_feedback, pipeworx_trending, suggest_questions, discover_tools) tangential to the core data-access purpose. The server name suggests a small Baton Rouge Open Data server, making the actual count a poor match for that name.
Within its actual scope, coverage is strong: universal query, grounded mode, deep research, entity/comparison profiles, entity resolution, claim validation, prediction-market edge/arbitrage/fill-risk, memory, and subscriptions all have lifecycle-appropriate tool sets. Obvious gaps are hard to find; the main issue is that the set is over-inclusive rather than incomplete.