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Glama

Data Nashville

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already confirm read-only, open-world, idempotent, non-destructive behavior. The description adds valuable context: for companies, it pulls latest 10-K data with proper fiscal year handling; for drugs, it pulls FAERS data. It also discloses sorting by primary metric. No contradiction.

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 front-loaded with key trigger phrases and usage instructions. Each sentence adds value, though the list of example phrases could be slightly trimmed. Still efficient for its length.

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 simple input schema (2 params, no output schema), the description sufficiently covers return format (paired data, citation URIs) and behavioral details (fiscal year handling, sorting). No gaps for this tool's complexity.

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?

Schema coverage is 100%. The description elaborates on parameters: type enum explained with examples, values array with specific formatting (tickers for company, names for drug) and constraints (2-5 items). This adds meaning beyond the 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?

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs, providing specific trigger phrases ('compare X and Y', 'which is bigger', etc.). It distinguishes from sibling tools like entity_profile by emphasizing multiple entities in one call.

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?

Explicitly recommends preferring this tool over sequential single-entity lookups when comparing entities. It also explains when to use company vs drug type, giving clear usage context.

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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