metadataGet a Michigan Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "fbey-tu9a".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "fbey-tu9a". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "fbey-tu9a"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and no destructive behavior. The description adds value by specifying the exact metadata returned (columns, types, row count, etc.) and the data source (Michigan Open Data). No contradictions.
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?
Single sentence, front-loaded with the verb and key information. No extraneous words; every part adds value.
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?
Description adequately covers purpose, parameter, and return fields. With no output schema, it provides sufficient detail for a simple metadata retrieval tool. Could mention lack of pagination or error cases, but not required given simplicity.
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 for resource_id. The tool description echoes the parameter with an example, adding minimal context about the data source. Baseline 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 states a specific verb ('Get') and resource ('Michigan Open Data dataset's schema + metadata') and lists output fields (columns, types, row count, category, last-updated). It clearly distinguishes from sibling tools, which are unrelated or serve different purposes.
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 context (need a resource_id, likely obtained from 'datasets' tool), but does not explicitly compare to alternatives or state when not to use. Thus, clear context but no 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.
There are multiple severe overlap clusters. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,578 tools, and ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly' — a direct ambiguity. The six polymarket_* tools plus bet_research form another dense, hard-to-distinguish cluster, and entity_profile/recent_changes/compare_entities/resolve_entity all have overlapping entity-investigation purposes. The long descriptions help but an agent would frequently misselect.
The dominant families are internally consistent (polymarket_* prefix, ask_pipeworx_* suffix family, and the verb-based remember/recall/forget), which aids navigation. However, the overall set mixes several conventions: single-word nouns (query, datasets, metadata, recall), verb_noun compounds (validate_claim, generate_llms_txt), and domain_noun names (entity_profile, polymarket_edges). Readable, but there is no unified pattern across the server.
34 tools is clearly over the 25-threshold for heaviness, and several earn little distinct value: ask_pipeworx_beta is a live duplicate of ask_pipeworx, the five-algorithm Polymarket family could be consolidated, and meta/utility tools (suggest_questions, discover_tools, pipeworx_trending, generate_llms_txt, scan_dependency) feel bolted on rather than essential. The breadth of the data domain justifies some size, but the redundancy and tangents push it into bloat.
Within its core sub-domains the surface is fairly complete: company research has resolve→profile/compare→recent_changes→validate_claim as a full lifecycle, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. The Polymarket workflow is especially thorough (detect→verify→fill-risk→track-decay). However, the server's stated identity ('Data Michigan') is barely served — the Michigan Open Data surface is only search/schema/query with no update or write path — and the scatter of unrelated tools (npm dependency scan, llms.txt generation) makes the overall purpose incoherent, so gaps are hard to evaluate.