Skip to main content
Glama

Arcgis Pflugerville

by io.github.pipeworx-io

Search Datasets

search_datasets
Read-onlyIdempotent

Search City of Pflugerville GIS open geospatial datasets (parcels, zoning, utilities & city services) by keyword. Returns each dataset's name, summary, record_count, owner/org, and its Feature Service url — pass that url to query_layer / layer_info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets (1-50, default 20).
queryNoKeyword(s), e.g. "parcels", "crime", "flood zones".
org_idNoOptional ArcGIS orgId to override the default (City of Pflugerville GIS).

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: +[
+  {
+    "query": "parcels"
+  },
+  {
+    "limit": 10,
+    "query": "zoning utilities"
+  }
+]
  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 this as read-only, idempotent, and non-destructive, so the safety bar is met. The description adds valuable context by specifying the exact return fields (name, summary, record_count, owner/org, url) and mentions the open-world nature implicitly through 'search' and the examples. This adds useful behavioral detail beyond the 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 two sentences long, front-loaded with the main action and scope, and every word adds value. It efficiently communicates purpose, return values, and the connection to sibling tools without redundancy.

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?

Despite no output schema, the description explicitly enumerates the return fields, making the tool's output predictable. It also provides context on how to use the result with sibling tools, making it self-sufficient for an AI agent to invoke and use effectively.

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?

The input schema already provides complete descriptions for all 3 parameters (query, limit, org_id), covering 100% of the schema. The description adds minimal parameter-level detail beyond saying 'by keyword', but since the schema is self-sufficient, a baseline score of 3 is appropriate.

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 tool searches City of Pflugerville GIS open geospatial datasets by keyword, with specific examples of dataset types. It also distinguishes itself from siblings by explaining the returned url can be passed to query_layer / layer_info.

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?

The description provides explicit guidance on when to use the tool (to find datasets by keyword) and what to do next (pass the url to query_layer / layer_info). This differentiates it from related sibling tools and gives a clear usage flow.

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

All Arcgis Pflugerville toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The tool set contains multiple overlapping tools for data querying (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, suggest_questions) and prediction market analysis (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread), making it difficult for an agent to distinguish which tool to use. The GIS-specific tools are few and could be confused with general data tools.

Naming Consistency2/5

Tool names follow no consistent pattern: some use verb_noun or noun_verb (ask_pipeworx, compare_entities, search_datasets), while others are longer phrases (generate_llms_txt, scan_competitor_ai_presence, polymarket_kalshi_spread). Mixed conventions and lack of uniformity reduce predictability.

Tool Count2/5

With 34 tools, the count is high for a server ostensibly focused on ArcGIS Pflugerville. Many tools are unrelated to GIS (e.g., prediction market tools, general Pipeworx utilities), making the tool surface feel bloated and poorly scoped.

Completeness2/5

For an ArcGIS server, the coverage is minimal: only three tools (search_datasets, layer_info, query_layer) directly support GIS operations. Missing typical GIS capabilities like geocoding, spatial analysis, or editing. The inclusion of many non-GIS tools does not compensate for the lack of depth in the core domain.

Resources