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Data Nashville

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds essential context about the output: it 'Returns category-bucketed example questions (company financials, drugs & clinical trials, ...) each with the exact tool + argument shape.' This goes beyond annotations and fully discloses behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one long sentence with a parenthetical list, which is somewhat dense but efficient. It front-loads the core purpose and includes all necessary details without redundancy. Could be broken into a clearer list, but scores well for informativeness.

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 has only one optional parameter and no output schema, the description thoroughly explains what it returns (category-bucketed example questions with tool+argument shapes), when to use it (first for onboarding), and how to narrow by topic. It is fully complete for its complexity level.

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% with a description for the single parameter `topic`. The description adds value by providing concrete examples ('e.g. "finance", "pharma", "betting"') and clarifying that omitting it gives a cross-category spread. This extra semantic context justifies a 4.

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 as an onboarding entry point that returns categorized example questions with exact tool and argument shapes. It distinguishes itself from sibling tools by being the 'FIRST' tool to use when unfamiliar with Pipeworx.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

The description explicitly says to use this tool 'FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains that you can narrow by topic using the `topic` parameter. This provides clear and actionable 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

A3.8/5.0
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical in current behavior, and ask_pipeworx_grounded, validate_claim, and deep_research all overlap with the same underlying routing. The six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) share domain and response fields, making misselection likely despite detailed descriptions.

Naming Consistency3/5

Snake_case is used throughout and prefix families (ask_pipeworx_*, nashville_*, polymarket_*, pipeworx_*) are consistent within themselves. However, the overall set mixes verb-first names (ask, compare, remember, subscribe) with noun/adjective-first names (entity_profile, recent_alerts, recent_changes, bet_research), so no single predictable pattern governs all tools.

Tool Count2/5

At 34 tools, the server exceeds a coherent surface, especially for a server named 'Data Nashville' where only 3 of 34 tools actually serve Nashville data. The count is inflated by several largely unrelated feature families (AI visibility, prediction markets, memory, subscriptions), making the set feel like an aggregation of multiple products rather than one focused server.

Completeness3/5

Individual families are fairly complete: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and Nashville has discovery/query/recent access. But the overall domain is unclear, and the Nashville-specific surface is thin (no search across datasets, no non-ArcGIS sources), leaving notable gaps relative to the server name.

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