metadataGet a Bloomington Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "yv82-z42g".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "yv82-z42g". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "yv82-z42g"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, establishing the tool as a safe read. The description adds detail on the return fields (schema, metadata) but does not disclose additional behavioral traits beyond what annotations convey.
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 a single, well-structured sentence that front-loads the action and key output, with an example. No 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?
For a simple read tool with no output schema, the description adequately lists the returned fields (columns, types, row count, category, last-updated). It does not cover error handling or invalid IDs, but the tool's low complexity and annotations make this sufficient.
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 a clear description of 'resource_id' as a dataset ID with example. The description text echoes this example but adds no new semantic information beyond the schema.
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 verb 'Get' and the resource 'dataset's schema + metadata', listing specific fields returned (columns, types, row count, category, last-updated) with an example resource_id. It distinguishes from siblings like 'datasets' by specifying the exact output, though not explicitly differentiating.
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 versus alternatives. The description explains what it does but does not specify when not to use it or provide explicit comparison to sibling tools like 'datasets'.
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.
Multiple tool clusters overlap heavily — ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, etc.) have blurry boundaries. The entity-research family (entity_profar, compare_entities, recent_changes) is also easy to misselect despite detailed descriptions.
All names are snake_case, but conventions are mixed: verb_noun (resolve_entaty, validate_claim), bare verbs (remember, recall, query), adjective_noun (recent_alerts, recent_changes), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Family prefixes and verbs help readability, but the overall pattern is not uniform.
At 34 tools this exceeds the 25+ threshold and spans loosely related domains — Bloomington open data, Pipeworx research, prediction markets, AI marketing, memory, npm scanning, and llms.txt generation. The scope feels heavy and unfocused relative to the 'Data Bloomington' server name.
The core research lifecycle is well covered: routing (ask_pipeworx), grounded verification, deep research, entity profiles/comparisons/changes, claim validation, entity resolution, subscriptions, and memory all exist. Minor gaps remain — no standalone tool for fetching a pipeworx:// citation URI and no API-key management — but agents can work around them.