metadataResource metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Resource identifier | |
| name | No | Resource name | |
| columns | No | Column definitions | |
| description | No | Resource description |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "a9u6-brt9"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Resource metadata from Socrata",
+ "properties": {
+ "columns": {
+ "description": "Column definitions",
+ "items": {
+ "description": "Column metadata",
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "description": {
+ "description": "Resource description",
+ "type": "string"
+ },
+ "id": {
+ "description": "Resource identifier",
+ "type": "string"
+ },
+ "name": {
+ "description": "Resource name",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations indicate a safe, read-only operation, the description adds no behavioral context beyond that. It does not explain what metadata entails or any side effects, but does not contradict 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?
The description is extremely short, but this is under-specification rather than conciseness. It lacks any useful structure or front-loading of information.
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 the single required parameter and presence of an output schema, the description is wholly incomplete. It does not inform the agent about what the tool returns or how to effectively use it.
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?
The description does not mention the resource_id parameter or its purpose. With 0% schema description coverage, the description fails to add any meaning beyond the schema's minimal definition.
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 'Resource metadata.' is vague and lacks a verb. It does not specify whether the tool retrieves, updates, or performs another action. It provides minimal purpose clarity.
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 guidance is provided on when to use this tool versus alternatives such as query or entity_profile. The description offers no context for appropriate usage.
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.
Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/research; polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_edge_tracker all analyze prediction markets; entity_profile, compare_entities, and recent_changes overlap on company research. An agent could easily select the wrong one.
All names are lowercase snake_case, but the verb-noun convention is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some noun-first (entity_profile, polymarket_edges, recent_changes), and some are bare nouns (query, metadata, datasets). The pattern is readable but not uniform.
34 tools is excessive for the apparent scope, especially given the server name suggests a Chicago city-data focus while the vast majority of tools are generic data-research, prediction-market, and memory utilities. This feels like a kitchen-sink bundle rather than a focused, well-scoped toolset.
The tool surface is a disjointed collection covering querying, research, memory, subscriptions, and prediction markets, but it lacks coherent lifecycle coverage for any single domain. For the named 'Cityofchicago' purpose, there is almost no city-specific functionality, and even as a general data tool, obvious gaps remain (e.g., no direct dataset management or update/delete operations).