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Search Datasets

search_datasets
Read-onlyIdempotent

Search City of Branson 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 Branson GIS).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context by stating that the tool returns metadata including a Feature Service URL intended for use with query_layer/layer_info, which establishes a clear workflow. This goes beyond the safety profile provided by 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: the first states the purpose and examples, the second enumerates return fields and the follow-up tool usage. It is concise, front-loaded, and every sentence adds value 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?

Given the simple search tool with full schema descriptions and rich annotations, the description adequately covers what the tool does, what it returns (name, summary, record_count, owner/org, Feature Service url), and how to integrate with sibling tools. No output schema exists, but the return fields are explicitly listed, making it complete.

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 100% coverage for all three parameters (query, limit, org_id) with clear descriptions. The description's mention of 'by keyword' aligns with the query parameter but adds no new meaning beyond the schema. Thus, the baseline 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 opens with a specific verb ('Search') targeting a defined resource ('City of Branson GIS open geospatial datasets') and includes concrete examples ('parcels, zoning, public works & city services'). It also differentiates itself from sibling tools by instructing the agent to pass the returned Feature Service URL 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 Guidelines4/5

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

The description provides clear usage context: search datasets by keyword for discovery, then pass the returned URL to query_layer/layer_info for actual data. This implicitly directs when to use the tool. However, it does not explicitly state when not to use it or name alternatives like search_within or discover_tools, so it stops short of a 5.

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

A3.9/5.0
Disambiguation5/5

All 33 tools have clearly distinct purposes, even those that seem related like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case and behavior. Memory tools (remember/recall/forget) and subscription tools (subscribe/unsubscribe/list_subscriptions/recent_alerts) are similarly distinct.

Naming Consistency3/5

All tools use snake_case, but naming patterns are mixed: some are single verbs (forget, recall), some verb_noun (query_layer, search_datasets), some noun_noun (entity_profile, layer_info), and some longer phrases (polymarket_kalshi_spread, scan_competitor_ai_presence). While readable, the inconsistency makes the set feel less coherent.

Tool Count2/5

With 33 tools, the surface is overly broad for a server named after a specific ArcGIS dataset. Many tools are unrelated to the core purpose (e.g., polymarket tools, npm scanning, AI visibility), making the count feel bloated and unfocused.

Completeness2/5

For the stated ArcGIS Branson focus, only 3 tools (search_datasets, query_layer, layer_info) are relevant, offering only read access. The rest are a miscellaneous collection from the Pipeworx ecosystem and other domains, leaving obvious gaps in GIS functionality and no write or analysis capabilities.