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Data Mo

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Missouri Open Data dataset by resource_id (e.g. "gfq7-aa86"). 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. "gfq7-aa86" (from datasets).

TDQS

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds useful behavioral details: the SoQL syntax (without leading $) and the output format (JSON). This goes beyond annotations without contradicting them.

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: one for purpose and filtering options, one for output format. Front-loaded with the verb and key information. No unnecessary 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 the tool's moderate complexity (7 parameters, no output schema), the description fully covers the input (resource_id, SoQL clauses) and output (JSON rows). The examples further clarify usage. No gaps for effective invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 7 parameters are described in the schema (100% coverage). The description adds critical nuance: SoQL clauses should be without the leading $, and it provides concrete usage examples. This significantly aids correct invocation 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 the tool's purpose: running a Socrata SoQL query against a Missouri Open Data dataset. It specifies the verb 'run a query', the resource (dataset by resource_id), and provides an example resource_id. This distinguishes it from siblings like 'datasets' or '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?

The description explicitly mentions filtering capabilities (where/select/group/order, limit/offset), telling the agent when to use the tool. However, it does not mention when not to use or suggest alternatives, though the context of sibling tools provides some implicit guidance.

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

A4.1/5.0
Disambiguation3/5

Several tool pairs have blurred boundaries: 'ask_pipeworx', 'ask_pipeworx_beta', and 'ask_pipeworx_grounded' serve overlapping routing purposes with minor differences in grounding or versioning, which can confuse an agent. Similarly, 'pipeworx_feedback' and the user feedback mechanism inside other tools lack clear tool-level distinction. Most other tools are distinct but the cluster of ask_pipeworx variants lowers overall clarity.

Naming Consistency4/5

Tool names largely follow a consistent verb_noun or prefix_noun pattern (e.g., ask_pipeworx, resolve_entity, scan_dependency). Some names like 'bet_research' and 'datasets' deviate from this pattern but remain readable. No chaotic mixing of conventions like camelCase and snake_case is present, so consistency is high overall.

Tool Count3/5

With 34 tools, the count is on the higher side for a single server, yet the tool set covers a broad domain of data access, analysis, and monitoring (data pipelines, prediction markets, compliance scans). Given the variety of distinct capabilities offered, 34 is borderline but not excessive enough to drop to a 2, as each tool addresses a concrete need.

Completeness5/5

The tool set offers a remarkably complete lifecycle for data operations: discovery (suggest_questions, discover_tools, datasets), entity resolution (resolve_entity, metadata), querying and retrieval (ask_pipeworx, deep_research, query), analysis and comparison (compare_entities, entity_profile, validate_claim), memory (remember, recall, forget), monitoring (subscribe, recent_alerts, polymarket_edge_tracker), and feedback (pipeworx_feedback). Niche tools like bet_research, scan_dependency, and generate_llms_txt further fill domain-specific gaps. No obvious missing operations for the stated purpose.

Resources