metadataGet a Santa Clara County Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "n9u6-aijz".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "n9u6-aijz". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "n9u6-aijz"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that it returns schema metadata (columns, types, row count, etc.), which is consistent. No further behavioral traits disclosed; rating is adequate given annotation coverage.
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 front-loaded with verb and resource, listing key metadata fields. No unnecessary words, efficient and clear.
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 one-parameter tool with no output schema, the description adequately covers purpose, parameter, and return fields. Lacks details on error handling or rate limits, but acceptable given minimal complexity.
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 param description 'Dataset id, e.g. "n9u6-aijz"'. Description adds context (Santa Clara County Open Data) and example, enriching meaning beyond the schema's type string. Baseline 3, elevated to 4 for added 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 verb 'Get', resource 'dataset schema + metadata', lists specific attributes (columns, types, row count, etc.), and provides an example resource_id. This distinguishes it from sibling tools like 'datasets' (which lists available datasets) and 'query' (which retrieves data within a dataset).
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?
No explicit guidance on when to use this tool versus alternatives. While the purpose is clear, the description does not mention when not to use it or suggest alternative tools for different tasks (e.g., 'datasets' to find data, 'query' to retrieve data).
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.
Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same 5,798-tool catalog and can return similar evidence-backed answers. Additionally, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, and bet_research all circle prediction-market edge detection, creating boundary ambiguity despite detailed descriptions.
Most tools follow a verb_noun or noun_verb pattern (e.g., list_subscriptions, generate_llms_txt, scan_dependency, compare_entities, resolve_entity), and consistent snake_case is used throughout. However, some names are vague and unclear (query, metadata, recall, forget, datasets), and the ask_pipeworx family is not clearly versioned in naming.
34 tools is heavy for a server that is conceptually a data-access gateway plus a few meta utilities. The count is inflated by multiple near-duplicate research modes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and an extensive prediction-market subfamily that could be consolidated.
The server covers its main domains well: entity resolution, company profiles, comparisons, change feeds, claim verification, grounded Q&A, and data discovery all exist. Minor gaps include no obvious tool for general web search or full-text legal records, and the subscription/alert system lacks an update-subscription tool, but core workflows have no dead ends.