datasetsSearch the Norfolk 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, idempotentHint, etc. Description adds return format details (resource_id, name, description, category, update date) and pagination hints, complementing annotations without contradiction.
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, no wasted words, front-loaded with action and resource, efficient.
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 no output schema, description lists return fields. Parameters are fully described in schema. Provides enough context for the agent to understand usage and results.
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%, so the schema already explains parameters. Description adds minor context: 'keyword' for query, pagination for offset, but does not significantly enhance semantics beyond 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?
Description clearly states 'Search the Norfolk Open Data catalog of open datasets by keyword', specifying verb and resource. It differentiates from sibling 'metadata' by noting to pass resource_id to that tool.
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?
Provides clear context on when to use: search by keyword. Mentions post-use action (pass resource_id to metadata), but lacks explicit when-not or alternative usage beyond this hint.
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.