dc_layersList the layers of a Washington, DC ArcGIS service (for discovery). Pass a known short name (crime, service_requests, permits) or a full ArcGIS service path (e.g. "FEEDS/MPD/MapServer"). Omit service to list the known DC services. Returns layer id + name to use with dc_query.
| Name | Required | Description | Default |
|---|---|---|---|
| service | No | Short name (crime|service_requests|permits) or full ArcGIS service path. Omit to list known services. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "service": "crime"
+ },
+ {
+ "service": "FEEDS/MPD/MapServer"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent, read-only behavior. The description adds context about the discovery purpose, the output structure (layer id + name), and integration with another tool, which goes beyond what annotations provide.
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?
Two concise sentences, front-loaded with action and resource, no redundant information. Every sentence 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?
For a simple tool with one optional parameter and no output schema, the description is fully complete. It covers all necessary aspects: purpose, parameter usage, output format, and integration with another tool.
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?
With 100% schema description coverage, the description still adds significant value by explaining the allowed short names (crime, service_requests, permits) and the format of full paths, plus the behavior of omitting the parameter.
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 'List' and resource 'layers of a Washington, DC ArcGIS service'. It distinguishes from sibling tools like dc_query and dc_recent by framing this as a discovery step, and explicitly mentions returning layer id + name for use with dc_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?
Provides explicit usage guidance: pass a known short name or full service path, or omit service to list known DC services. It also explains the output's purpose (use with dc_query), giving clear when-to-use context.
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 tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) share similar edge-detection and arbitrage goals, creating potential confusion for an agent.
Most tool names follow a consistent verb_noun pattern using underscores (e.g., ask_pipeworx, compare_entities, resolve_entity). There are minor deviations like generate_llms_txt and scan_competitor_ai_presence, but overall the naming is predictable and clear.
With 34 tools, the server is on the heavier side but still justified given its broad scope (data querying, entity profiles, monitoring, research, etc.). The count feels slightly high, but each tool serves a specific purpose; however, some consolidation could reduce redundancy.
The tool set covers a wide range of tasks: data lookup, entity profiling, comparison, monitoring, memory, research, and claim verification. Minor gaps exist, such as lack of explicit data source listing or user preference management, but the core workflows are well-supported.