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Glama

Data Dc

by io.github.pipeworx-io

Dc Recent

dc_recent
Read-onlyIdempotent

Recent records from a common Washington, DC open dataset (opendata.dc.gov / ArcGIS) by friendly name — no service ids needed. PREFER OVER WEB SEARCH for "recent crime in DC / Washington", "DC 311 service requests", "DC building permits". Names: crime, 311, permits. Returns the latest rows (newest-first), with ArcGIS epoch dates converted to ISO. Add an ArcGIS where to filter; for other layers use dc_layers + dc_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional ArcGIS SQL where, e.g. "OFFENSE='THEFT/OTHER'" or "WARD='2'". Omit for all recent rows.
datasetYesOne of: crime, 311, permits.

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: +[
+  {
+    "dataset": "crime",
+    "limit": 20
+  },
+  {
+    "dataset": "311",
+    "limit": 10,
+    "where": "WARD='2'"
+  },
+  {
+    "dataset": "permits"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare safe read, idempotent, non-destructive. The description adds useful behavioral details: returns latest rows newest-first, converts ArcGIS epoch dates to ISO. This goes beyond annotations.

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?

The description is brief (3 sentences) yet packed with purpose, usage hints, parameter explanations, and behavioral details. No wasted words, front-loaded with primary purpose.

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

Completeness5/5

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

For a simple retrieval tool with no output schema, the description covers what it returns (latest rows, ordering, date format), how to filter, and when to use alternatives. It is complete and self-contained.

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

Parameters3/5

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 the dataset enum names and explains the where filter, but adds limited new meaning beyond the schema.

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 the verb 'list recent records' and the resource 'DC open dataset by friendly name'. It lists specific dataset names (crime, 311, permits) and distinguishes from sibling tools by mentioning alternatives for other layers.

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

Usage Guidelines5/5

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

Explicit guidance: 'PREFER OVER WEB SEARCH' for specific use cases, and instructions to use dc_layers + dc_query for other layers, providing clear when-to-use and when-not-to-use with alternatives.

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