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Glama

Data Batonrouge

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Baton Rouge Open Data dataset by resource_id (e.g. "fabb-cnnu"). 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. "fabb-cnnu" (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": 50,
+    "resource_id": "fabb-cnnu",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "order": "total DESC",
+    "resource_id": "fabb-cnnu",
+    "select": "category, count(*) AS total"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds 'Returns matching rows as JSON', which is useful but minimal beyond 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 purpose and example, no unnecessary words.

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?

Given 7 parameters, full schema coverage, and rich annotations, description explains query language and return format. Could mention default limit of 1000, but otherwise complete.

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%. Description adds crucial context: SoQL clauses should be used without leading $, and explains limit/offset. Adds meaning beyond schema.

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 states specific verb 'Run a Socrata SoQL query', resource 'Baton Rouge Open Data dataset by resource_id', and provides an example. Distinguishes from sibling tools like 'datasets' and 'metadata'.

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?

Explains how to filter with SoQL clauses and pagination parameters. Does not explicitly state when not to use or alternatives, but context is clear.

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

Many tools are clearly distinct, but the ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are near-identical routers (beta currently matches stable exactly), and several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping use cases. Detailed descriptions mitigate but don't fully eliminate the risk of an agent picking the wrong member of a cluster.

Naming Consistency4/5

Names are consistently snake_case and mostly follow a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, subscribe). Minor deviations like bare nouns (datasets, metadata) and bare verbs (remember, recall, forget), plus the branded ask_pipeworx variants, keep it from a perfect pattern but the style remains predictable.

Tool Count2/5

34 tools is well above the 25+ threshold and feels heavy even for a broad research platform; many are meta/utility tools (pipeworx_feedback, pipeworx_trending, suggest_questions, discover_tools) tangential to the core data-access purpose. The server name suggests a small Baton Rouge Open Data server, making the actual count a poor match for that name.

Completeness4/5

Within its actual scope, coverage is strong: universal query, grounded mode, deep research, entity/comparison profiles, entity resolution, claim validation, prediction-market edge/arbitrage/fill-risk, memory, and subscriptions all have lifecycle-appropriate tool sets. Obvious gaps are hard to find; the main issue is that the set is over-inclusive rather than incomplete.

Resources