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Glama

Data Nola

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
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 significant behavioral context: parallel calls to multiple sources, fallback mechanism, soft-fails for USPTO, date format flexibility, and return structure with source-grouped changes and citation URIs.

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?

The description is well-structured: front-loaded with use cases, then explains the parallel call, sources, date format, return format, and alternative tool in a dense but efficient manner. Every sentence serves a purpose.

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?

Despite no output schema, the description explains the return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs) and mentions edge cases (soft-fail for USPTO, fallback). For a 3-parameter tool, this covers input, behavior, and output fully.

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 descriptions for each parameter. The description adds value by explaining relative date shorthand ('30d', '1m') and providing examples ('30d' or '1m for typical monitoring'). It also clarifies 'value' can be ticker or CIK. This goes beyond the schema's basic descriptions.

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: a change feed for a company over a time window, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It explicitly distinguishes from the sibling 'entity_profile', which provides a static profile.

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 provides explicit usage scenarios ('What's new with X', 'latest on Y') and when to use alternatives ('Use entity_profile instead when you want the static profile'). It also explains fallback behavior (GDELT→GNews).

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

A3.9/5.0
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and composite tools like entity_profile, recent_changes, compare_entities, and validate_claim draw on similar company-data sources. However, the descriptions are extremely detailed with explicit usage guidance, so an agent can usually pick correctly despite the overlap.

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, list_subscriptions, validate_claim), and domain prefixes like nola_, pipeworx_, and polymarket_ help group tools. Minor deviations such as entity_profile, recent_changes, and polymarket_edges are still readable and do not undermine the overall pattern.

Tool Count2/5

At 34 tools, the set spans far beyond the 'Data Nola' name: New Orleans open data, general Pipeworx data research, Polymarket arbitrage, npm dependency scanning, AI visibility, memory, and subscriptions. This breadth makes the surface heavy and forces agents to triage a large and heterogeneous toolset.

Completeness4/5

The broader research platform is well covered: question answering, entity resolution, profiles, comparisons, claim validation, subscriptions, memory, and NOLA dataset querying all have solid lifecycle support. Minor gaps exist—no NOLA dataset metadata or write tools, no Polymarket order execution—but these are acceptable for a read-only data server.

Resources