metadataGet a Sonoma County Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "3rsj-iche".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "3rsj-iche". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "3rsj-iche"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already declare the tool safe and idempotent. The description adds value by detailing the returned metadata fields, which supplements the annotations without contradicting them.
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 sentence that efficiently conveys purpose, inputs, and outputs. The example is helpful but could be trimmed for even greater conciseness. No structural issues.
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 explains return values (columns, types, row count, category, last-updated). Annotations cover behavioral aspects. The tool is simple with one parameter, and the description is complete for an agent to use correctly.
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 description coverage is 100%; the single parameter 'resource_id' is clearly described in the schema. The description repeats the example but does not add new semantic meaning beyond the schema. Baseline score of 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 tool gets a dataset's schema and metadata, specifying exact fields (columns, types, row count, category, last-updated) and provides an example resource_id. It distinguishes itself from sibling tools like 'datasets' by focusing on a single dataset's metadata.
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 when to use the tool (when metadata for a specific dataset is needed) but does not explicitly state when not to use it or provide alternatives. For a simple lookup tool, this is adequate but lacks explicit guidance.
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 have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded reuses the same router, and deep_research overlaps with ask_pipeworx for multi-part questions. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, entity_profile, compare_entities, recent_changes, and resolve_entity all cluster around overlapping research/comparison tasks despite detailed descriptions.
Names are consistently lowercase snake_case, but the naming pattern is mixed: some are verb_noun (ask_pipeworx, validate_claim, resolve_entity), some are bare nouns (datasets, metadata, polymarket_edges), some are imperative verbs (remember, forget, query), and some are adjective_noun (recent_alerts, recent_changes). The polymarket_* and pipeworx_* prefixes help, but the overall convention is not uniform.
With 34 tools, the server exceeds the 25+ threshold and feels overstuffed for a coherent single-purpose MCP server. It spans unrelated domains: Sonoma County open data, a general structured-data router, prediction-market analysis, memory, subscriptions, npm dependency checking, and llms.txt generation—each could reasonably be its own smaller server.
The core research workflow is well covered: routing, grounded verification, entity resolution, entity profiles, comparisons, recent changes, claim validation, memory, subscriptions, and prediction-market execution checks are all present. Minor gaps exist, such as no subscription update/edit, no general pipeworx:// record-reader tool, and a read-only open-data surface, but agents can usually work around these.