metadataGet a Missouri Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "gfq7-aa86".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "gfq7-aa86". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds concrete behavioral context by specifying the exact return fields (columns, types, row count, category, last-updated), which is beyond the annotation information. 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?
The description is a single concise sentence that front-loads the action and result, includes an example, and contains no redundant words. Every part 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 no output schema, the description fully specifies the return fields (columns, types, row count, category, last-updated), providing a complete understanding of what the tool returns.
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% for the single parameter 'resource_id'. The description reinforces its purpose with an example format, but does not add extra semantic detail beyond the schema's own description. 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?
The description clearly states the verb 'Get', specifies the resource 'Missouri Open Data dataset's schema + metadata', and lists the specific outputs (columns, types, row count, category, last-updated). It also includes an example resource_id, making the tool's purpose precise and distinguishable from siblings like 'datasets' and 'query'.
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 when the agent needs schema or metadata for a dataset, but does not explicitly state when to use or avoid this tool, nor mention alternative tools. Without exclusions or comparisons, the guidance is only implicit.
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.