metadataGet a Norfolk Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "cab7-wvn5".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "cab7-wvn5". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "cab7-wvn5"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint false, indicating safe read-only behavior. The description adds that it returns schema and metadata details, providing additional context about the return value but not contradicting or significantly extending 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?
The description is a single, well-structured sentence that starts with the core action and includes an example. It is concise with no superfluous information.
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 metadata retrieval tool with one parameter, the description covers the main functionality and return value. It could briefly mention error handling for invalid resource IDs, but overall it is 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 description coverage is 100%, so the parameter is fully documented in the schema. The description reinforces that the resource_id is a dataset ID and provides an example, but does not add meaning 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 tool retrieves a dataset's schema and metadata, listing specific attributes (columns, types, row count, category, last-updated). It provides an example resource ID, distinguishing it from siblings that do not specifically target Norfolk Open Data metadata.
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 gives an example usage but no explicit guidance on when to use this tool versus alternatives. It does not state prerequisites or conditions for appropriate use.
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.
ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps ask_pipeworx for multi-faceted questions, and the five Polymarket tools plus bet_research all target the same market-analysis space. Several tools appear to do the same thing, and even detailed descriptions can't fully separate them.
All names are lowercase snake_case and mostly readable, but the set mixes bare nouns (datasets, query, recall) with verb phrases (generate_llms_txt, validate_claim) and domain-prefixed compounds (polymarket_edges, pipeworx_trending). The ask_pipeworx family is internally consistent, but the overall pattern is not uniform.
34 tools is well above the 25-tool threshold for a coherent set, especially since many are meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending) or one-off utilities (generate_llms_txt, scan_dependency). The server tries to cover data lookup, prediction markets, memory, subscriptions, and AI visibility in a single surface.
Within each subdomain (data lookup, memory, subscriptions, Polymarket) the main workflows are covered, and the memory/subscription clusters have full CRUD. But the server name promises Norfolk Open Data, which is barely represented by three read-only tools, and odd one-offs like generate_llms_txt and scan_dependency have no supporting ecosystem.