Skip to main content
Glama

Maryland Open Data

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Maryland Open Data dataset by resource_id (e.g. "2ir4-626w"). Filter with where/select/group/order (SoQL clauses, without the leading $) plus limit/offset. Returns matching rows as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoSoQL $group column(s).
limitNoMax rows (default Socrata 1000).
orderNoSoQL $order, e.g. "date DESC".
whereNoSoQL $where filter, e.g. "year >= 2020 AND status = 'Active'".
offsetNoPagination offset.
selectNoSoQL $select, e.g. "name, count(*) AS n".
resource_idYesDataset id, e.g. "2ir4-626w" (from datasets).

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": 100,
+    "resource_id": "2ir4-626w",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "order": "count DESC",
+    "resource_id": "2ir4-626w",
+    "select": "category, count(*) AS count"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that the tool is read-only (consistent with readOnlyHint=true) and idempotent, returns matching rows as JSON, and uses SoQL clauses without leading $. It adds value beyond the annotations by detailing the query language and return format.

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 concise sentences: first sentence states the core action, second details filtering options. No wasted words, front-loaded with the essential verb and resource.

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 presence of comprehensive schema descriptions (100% coverage), annotations, and no output schema required (just JSON), the description is fully sufficient for an agent to select and invoke the tool correctly. It covers the query language, required resource_id, and optional parameters.

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?

With 100% schema description coverage, the baseline is 3. The description adds context about SoQL clauses (no leading $) and mentions limit/offset, which enhances understanding beyond the schema alone.

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 it runs a Socrata SoQL query against a Maryland Open Data dataset by resource_id, with specific filtering options (where, select, group, order, limit, offset) and returns JSON. This distinguishes it from siblings like 'datasets' which are for listing datasets.

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 implicitly tells when to use the tool (to query a specific dataset with SoQL) via the specification of parameters and the resource_id. It does not explicitly contrast with sibling tools or state when not to use, but the context is clear enough for an AI agent.

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

All Maryland Open Data 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

A3.7/5.0
Disambiguation3/5

Tools are generally distinct in purpose, but the server name 'Maryland Open Data' conflicts with the inclusion of many unrelated Pipeworx tools (e.g., prediction market tools). This creates ambiguity about the server's actual domain, making it hard for agents to know what to expect.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others are plain (datasets, query), and some are descriptive phrases (ask_pipeworx_grounded). No consistent verb_noun pattern emerges, leading to a chaotic feel.

Tool Count2/5

33 tools is high, and the majority are unrelated to Maryland Open Data, suggesting scope creep. The server tries to be a general-purpose data platform but is named after a specific dataset, making the count feel excessive and unfocused.

Completeness3/5

The Maryland Open Data subset is minimal (3 tools: datasets, metadata, query), lacking update/delete/CRUD operations. The broader set includes many query and analysis tools, but the server's stated purpose is not fully covered.

Resources