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Glama

Data Dc

by io.github.pipeworx-io

Dc Query

dc_query
Read-onlyIdempotent

Query any Washington, DC ArcGIS layer by service path + layer id. Full ArcGIS query: where, out_fields, order_by, limit. Use dc_layers to find a service/layer, or dc_recent for the common ones. Epoch dates are converted to ISO.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
layerYesLayer id within the service (from dc_layers), e.g. 39.
limitNoMax rows (default 100, max 2000).
whereNoArcGIS SQL where (default "1=1").
serviceYesArcGIS service path, e.g. "FEEDS/MPD/MapServer" (or a short name: crime|service_requests|permits).
order_byNoSort clause, e.g. "REPORT_DAT DESC".
out_fieldsNoComma-separated fields, or "*" (default).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "layer": 0,
+    "limit": 50,
+    "service": "crime",
+    "where": "OFFENSE='THEFT/OTHER'"
+  },
+  {
+    "layer": 39,
+    "limit": 100,
+    "order_by": "REPORT_DAT DESC",
+    "out_fields": "WARD,REPORT_DAT,STATUS",
+    "service": "service_requests"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint. The description adds that epoch dates are converted to ISO, a behavioral detail not in 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences, front-loaded with the primary purpose. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description implies return of query results. Could mention pagination or default limit more explicitly, but overall provides sufficient context for a query tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description reinforces parameter roles by mentioning 'Full ArcGIS query: where, out_fields, order_by, limit' and adds the epoch date conversion detail, which exceeds schema info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it queries DC ArcGIS layers by service path and layer id, with full ArcGIS query parameters. It differentiates from sibling tools dc_layers and dc_recent by specifying their complementary roles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells users to use dc_layers to find a service/layer or dc_recent for common ones, providing clear guidance on when to use alternatives. Could be improved by stating when not to use this tool, but it's adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

Resources