datasetsSearch the Virginia 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": "budget"
+ },
+ {
+ "limit": 50,
+ "offset": 0,
+ "query": "crime"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds behavioral context by specifying the return fields and the recommended follow-up action (pass resource_id to query/metadata), which goes beyond the annotations and helps the agent understand the tool's role in a larger workflow.
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 primary action and resource. It includes essential return fields and a clear follow-up instruction without any fluff or repetition of schema information. Every word earns its place.
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 the tool's simplicity (3 optional parameters, full schema coverage, and strong annotations), the description is complete. It explains what the tool does, what it returns, and how to proceed with the results. The absence of an output schema is compensated by the explicit list of return fields, making the tool's behavior well-understood.
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?
The input schema provides full descriptions for all three parameters (query, limit, offset) with 100% coverage, so the baseline is 3. The description does not add any parameter-specific meaning beyond the schema; it only mentions keyword search, which is already captured in the query parameter description. Therefore, the description adds no extra semantic value.
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 tool's action: 'Search the Virginia Open Data catalog of open datasets by keyword.' It specifies the resource (Virginia Open Data catalog), the verb (search), and the output (resource_id, name, description, category, update date). It distinguishes itself from sibling tools by directing the agent to pass the resource_id to query/metadata for further operations.
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 provides explicit usage guidance: use this tool to search datasets by keyword, then pass the returned resource_id to query/metadata. This tells the agent when to use this tool (discovery) and what to use next, effectively identifying an alternative workflow. It does not explicitly mention 'when not to use' but the follow-up instruction is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tool clusters are near-duplicates or easy to confuse: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta currently matches stable exactly), while polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction-market opportunities with overlapping outputs. entity_profile/compare_entities/recent_changes and ai_visibility_check/scan_competitor_ai_presence add further redundancy. Despite detailed descriptions, the boundaries require careful reading, so an agent is likely to misselect.
All names are snake_case and readable, with useful prefixes like ask_, polymarket_, pipeworx_, and scan_. However, conventions are mixed: verb_noun (ask_pipeworx, list_subscriptions, resolve_entity) coexists with bare nouns (datasets, metadata, query) and noun-first compounds (entity_profile, bet_research, deep_research). There is no single predictable pattern.
34 tools is above the 25-tool threshold, and the set spans multiple unrelated domains such as data routing, prediction markets, memory, subscriptions, Virginia Open Data, AI visibility, and npm dependency checks. Several tools are effectively wrappers or near-overlaps that could be consolidated, e.g., ai_visibility_check vs scan_competitor_ai_presence and polymarket_edges vs polymarket_arbitrage. The surface feels bloated for a single server.
Within its main sub-domains the set is solid: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, research has resolve_entity/compare_entities/entity_profile/recent_changes/validate_claim, and Polymarket has detection/arbitrage/fill-risk/edge-tracking. Minor gaps exist — no tool to fetch a raw pipeworx:// record, no write/update for Virginia Open Data, and no trade execution for prediction markets — but these do not create dead ends for a research-focused agent.