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Glama

Query Layer

query_layer
Read-onlyIdempotent

Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like where, comma-separated out_fields, order_by, limit, offset. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFeature/Map Service layer url ending in /FeatureServer/<n> or /MapServer/<n>.
limitNoMax features (1-2000, default 50).
whereNoSQL where clause, e.g. "STATE = 'CA' AND YEAR >= 2020". Default "1=1".
offsetNoPagination offset.
order_byNoe.g. "POP DESC".
out_fieldsNoComma-separated field names, or "*" for all (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: +[
      +  {
      +    "limit": 50,
      +    "out_fields": "*",
      +    "url": "https://services.arcgisonline.com/arcgis/rest/services/City_of_Abbotsford/FeatureServer/0",
      +    "where": "1=1"
      +  },
      +  {
      +    "limit": 100,
      +    "out_fields": "PARCEL_ID,OWNER,VALUE",
      +    "url": "https://services.arcgisonline.com/arcgis/rest/services/City_of_Abbotsford/FeatureServer/2",
      +    "where": "ZONING = 'Residential'"
      +  }
      +]
  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 cover safety (read-only, idempotent, non-destructive). The description adds beyond annotations: it returns attribute rows and geometry, supports SQL-like where clauses and pagination, and clarifies the sampling approach. This is useful behavioral context.

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: the first states purpose and key parameters; the second gives a concrete usage tip. Front-loaded, no redundant content, every clause earns its place.

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?

For a tool with no output schema, it adequately explains input origin (url from search_datasets), query capabilities, return shape (attribute rows + geometry), and a practical usage tip. It doesn't mention caveats like field syntax or results limits beyond schema, but the schema already covers those.

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 adds value by noting the SQL-like nature of 'where', that results include geometry, and the '1=1'/'*' sampling idiom — info not fully captured in the schema definitions.

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?

Description clearly states the tool's function: 'Query an ArcGIS Feature Service / Map Service layer by its url' — a specific verb and resource. It also references the source ('from search_datasets'), which distinguishes it from sibling tools like search_datasets and 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?

It implies a workflow: first find a dataset via search_datasets, then query it. The tip 'Use where="1=1" + out_fields="*" to sample' provides practical usage guidance. However, it doesn't explicitly name alternatives or note when not to use this tool.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all provide data lookup/research. The PolyMarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also creates boundary confusion despite varied signals.

Naming Consistency3/5

Most tools use snake_case, but the naming pattern is mixed: some are imperative verb phrases (query_layer, validate_claim), while others are noun phrases (entity_profile, recent_alerts, layer_info). Descriptions are readable overall, but there is no consistent verb_noun convention across the set.

Tool Count2/5

34 tools is high and the vast majority are unrelated to the server's stated ArcGIS Abbotsford purpose. The set appears to be a generic Pipeworx data/prediction-market toolkit with only a few GIS-specific tools, making the count excessive for the declared scope.

Completeness2/5

For the ArcGIS Abbotsford domain, the surface is severely incomplete: only search_datasets, query_layer, and layer_info cover GIS functionality, lacking update/delete/create operations or broader dataset management. For the actual Pipeworx domain, coverage is decent, but that does not match the server name.