metadataGet a Colorado Information Marketplace dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "4ykn-tg5h".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "4ykn-tg5h". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "4ykn-tg5h"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true. Description adds specific metadata fields returned, enriching context beyond annotations. 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?
Single sentence, no wasted words, front-loaded with action and return details. 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?
Simple tool with rich annotations; description covers what, input, and output. No output schema needed as description lists returned fields. Complete for its purpose.
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 100% for the only parameter. Description adds an example value (resource_id: '4ykn-tg5h'), providing concrete context for correct usage.
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 'Get a Colorado Information Marketplace dataset's schema + metadata' with specific metadata fields (columns, types, etc.) and parameter 'resource_id'. Distinguishes from sibling 'datasets' which likely lists datasets.
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 usage: when you need dataset schema/metadata by ID. Provides example resource_id. No explicit when-not or alternatives, but context is clear for this focused tool.
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.
The tool set has several overlapping clusters: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, entity_profile / recent_changes / compare_entities, and a half-dozen prediction-market tools. The descriptions are unusually detailed and mostly steer an agent correctly, but ask_pipeworx_beta is currently identical to ask_pipeworx and the prediction-market tools still require careful reading to pick the right one.
All names are lowercase snake_case, but the conventions vary: many are verb_noun (resolve_entity, compare_entities, validate_claim), some are bare nouns (datasets, metadata, query), some are bare imperatives (remember, forget, subscribe), and there are separate prefix families like polymarket_* and pipeworx_*. It is readable and consistent in style, but not a single predictable pattern.
34 tools exceeds the 25+ threshold for 'too many,' and the set is not tightly scoped: only datasets, metadata, and query directly relate to the stated Colorado Information Marketplace purpose. The bulk are Pipeworx data-research, prediction-market, memory, and subscription utilities, making the server feel like a broad platform bolted onto a state-data catalog.
For the core read-only lifecycle of the Colorado data catalog, search (datasets), schema inspection (metadata), and data retrieval (query) are covered. Minor gaps exist elsewhere: there is no explicit tool for fetching a pipeworx:// citation record directly, and some utilities like generate_llms_txt or scan_dependency are unrelated to the server's stated purpose, but most cited workflows can still complete.