datasetsSearch the Missouri 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds the returned fields and downstream instruction but no behavioral traits beyond 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?
Two sentences, no redundant information, perfectly front-loaded.
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?
Covers purpose, input, and output adequately. Does not explain pagination behavior beyond schema, but with 3 optional params and no output schema, it is sufficiently complete.
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 descriptions for all 3 parameters. Description adds no new parameter semantics beyond mentioning returned fields.
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?
Clearly states it searches the Missouri Open Data catalog by keyword and lists returned fields. Distinct from all sibling tools.
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?
Implies use for searching datasets, hints at downstream use via 'pass the resource_id to query/metadata', but provides no explicit when/when-not guidance or alternatives.
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.
Several tool pairs have blurred boundaries: 'ask_pipeworx', 'ask_pipeworx_beta', and 'ask_pipeworx_grounded' serve overlapping routing purposes with minor differences in grounding or versioning, which can confuse an agent. Similarly, 'pipeworx_feedback' and the user feedback mechanism inside other tools lack clear tool-level distinction. Most other tools are distinct but the cluster of ask_pipeworx variants lowers overall clarity.
Tool names largely follow a consistent verb_noun or prefix_noun pattern (e.g., ask_pipeworx, resolve_entity, scan_dependency). Some names like 'bet_research' and 'datasets' deviate from this pattern but remain readable. No chaotic mixing of conventions like camelCase and snake_case is present, so consistency is high overall.
With 34 tools, the count is on the higher side for a single server, yet the tool set covers a broad domain of data access, analysis, and monitoring (data pipelines, prediction markets, compliance scans). Given the variety of distinct capabilities offered, 34 is borderline but not excessive enough to drop to a 2, as each tool addresses a concrete need.
The tool set offers a remarkably complete lifecycle for data operations: discovery (suggest_questions, discover_tools, datasets), entity resolution (resolve_entity, metadata), querying and retrieval (ask_pipeworx, deep_research, query), analysis and comparison (compare_entities, entity_profile, validate_claim), memory (remember, recall, forget), monitoring (subscribe, recent_alerts, polymarket_edge_tracker), and feedback (pipeworx_feedback). Niche tools like bet_research, scan_dependency, and generate_llms_txt further fill domain-specific gaps. No obvious missing operations for the stated purpose.