metadataGet a Baton Rouge Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "fabb-cnnu".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "fabb-cnnu". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "fabb-cnnu"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. The description adds value beyond annotations by specifying what data is returned (columns, types, row count, category, last-updated). This informs the agent about the response content without needing an output schema.
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?
Description is a single, front-loaded sentence that efficiently conveys the tool's purpose, scope, and an example. No redundant or unnecessary 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?
Despite no output schema, the description lists the return fields reasonably well (columns, types, row count, category, last-updated). It is nearly complete for the given tool complexity, though it could optionally mention the format or if all columns are returned.
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 the parameter description already stating 'Dataset id, e.g. "fabb-cnnu".' The description does not add new semantic information beyond what the schema provides; it only restates the example. 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?
Description clearly states the action (Get), the resource (dataset's schema + metadata), and lists specific fields (columns, types, row count, category, last-updated). The example resource_id further clarifies usage. This distinguishes it from siblings like 'datasets' which likely list datasets rather than a single dataset's schema.
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 for retrieving schema/metadata of a specific dataset, but does not explicitly mention when to use this tool versus alternatives (e.g., 'datasets' or other query tools). No exclusions or contextual guidance is provided.
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.