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Glama

Nba

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe, idempotent behavior. Description adds valuable context: multi-source fan-out, GDELT-to-GNews fallback, PatentsView soft-fail, and return structure including pipeworx:// URIs.

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?

Description is a single paragraph but well-structured, starting with purpose and usage examples. Every sentence contributes information; no fluff. Slightly long but appropriate for the tool's complexity.

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 complex multi-source nature and no output schema, the description adequately explains return structure (changes[], total_changes, citations), fallback logic, and limitations (PatentsView soft-fail). Complete for agent use.

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 covers all parameters. Description enhances understanding by explaining accepted formats for 'since' (ISO date or relative shorthand) and providing example values for 'value' (ticker or CIK). Minor extra value.

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 clearly defines the tool as a change feed for companies, listing specific sources (SEC, news, patents) and explicitly distinguishes it from entity_profile, which provides static profile data.

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?

Description provides explicit guidance on when to use this tool (dynamic changes) versus entity_profile (static profile), and includes example queries and fallback behavior explanation.

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

A3.5/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research all provide factual lookups with similar boundaries. NBA-specific tools are mixed with generic Pipeworx utilities, making it hard for an agent to distinguish when to use which.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check), camelCase (generate_llms_txt), and inconsistent verb usage (ask_pipeworx vs. search_players). No clear pattern emerges across the set.

Tool Count2/5

With 34 tools, the server is overpopulated, especially given its 'Nba' focus. Many tools (e.g., remember, forget, list_subscriptions) are generic utilities unrelated to NBA data, inflating the count beyond a coherent scope.

Completeness2/5

For an NBA server, tools are severely incomplete: missing player stats, team stats, season standings, and live game details. While the Pipeworx data tools are extensive, they diverge from the server's apparent purpose, leaving clear gaps.

Resources