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Glama

Arcgis Longview

by io.github.pipeworx-io

Search Datasets

search_datasets
Read-onlyIdempotent

Search City of Longview GIS open geospatial datasets (parcels, zoning, public works & 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 Longview 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 flood"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare the tool safe (readOnly, openWorld, idempotent). The description adds useful behavior context: returns each dataset's name, summary, record_count, owner/org, and the Feature Service url, plus a usage workflow. No contradiction with 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?

Two sentences, front-loaded with the core purpose, includes examples and return details, and provides actionable follow-up. No wasted words.

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?

Given no output schema, the description fully discloses return fields and usage flow, and includes domain context. It covers all key aspects an agent needs to select and invoke the tool correctly.

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 schema has 100% coverage for all 3 parameters with clear descriptions, so the baseline is 3. The description doesn't add new parameter semantics beyond 'keyword' which aligns with the query param, but it also doesn't need to.

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 uses a specific verb ('Search') and resource ('City of Longview GIS open geospatial datasets') with examples of content, and distinguishes itself from sibling tools like query_layer and layer_info by explicitly stating it returns the Feature Service url for those tools to use.

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?

It clearly states the tool is for searching datasets and provides follow-up guidance ('pass that url to query_layer / layer_info'), indicating when to use this tool vs. alternatives. It doesn't explicitly say when not to use it, but the context is strong.

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

A4/5.0
Disambiguation3/5

Descriptions are unusually explicit about when to use each tool (single lookup vs grounded vs deep research), but the set still contains several genuinely overlapping tools: ask_pipeworx_beta is explicitly identical to ask_pipeworx, scan_competitor_ai_presence wraps ai_visibility, and six polymarket_* tools share the same 'edge/arbitrage' conceptual space. An agent can usually pick the right tool but faces real ambiguity in several clusters.

Naming Consistency4/5

Names are uniformly snake_case and readable, and there are coherent prefix families (ask_pipeworx_*, polymarket_*, pipeworx_*). However the verb placement is inconsistent: verb_noun (ask_pipeworx, validate_claim, discover_tools) coexists with noun-first names (recent_changes, entity_compare, layer_info, polymarket_edges), and bet_research sits outside the polymarket_* family despite being a prediction-market tool.

Tool Count2/5

34 tools is well past the 'feels heavy' threshold, and more importantly the set mixes what looks like three different servers: a tiny ArcGIS/Longview GIS slice (layer_info, query_layer, search_datasets), a massive general-purpose data-research platform from Pipeworx, and a Polymarket prediction-market toolkit. Most tools earn their place for the platform, but far too few belong to the named domain.

Completeness4/5

The research surface is remarkably complete: a router, grounded and beta variants, deep multi-source research, claim verification, entity/profile/change resolution, discovery and suggestion helpers, memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/recent_alerts), and feedback — no obvious lifecycle dead ends. Minute gaps exist on the GIS side (no dataset editing, no metadata browsing, no named export/view ops) and a few nooks like screen- leisure tools have no progress/status endpoints, but these are workaroundable.

Resources