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Glama

Bhagavad Gita

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").

TDQS

A4.6/5.0
Behavior5/5

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

The description goes far beyond annotations by detailing internal behavior: fan-out to SEC EDGAR, GDELT→GNews fallback with conditions, USPTO soft-fail due to API sunset, return format (changes grouped by source, total_changes count, citation URIs). Annotations only indicate read-only, idempotent, non-destructive.

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 detailed but efficiently front-loaded with example queries. Every sentence adds value (sources, fallback, return format, sibling distinction). Could be slightly shorter, but no waste.

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 complexity (multiple data sources, time windows, fallback logic), the description is fully complete. It explains all parameters, behavior under failures, return structure, and provides an alternative tool. No output schema but the description covers what to expect.

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%, but the description adds practical guidance: for 'since' it gives relative shorthand examples (7d, 30d, 3m, 1y) and recommends '30d'/'1m'; for 'value' it clarifies ticker or CIK format. This enhances usability beyond the schema definitions.

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 using multiple example queries ('What's new with X', 'latest on Y') and explicitly identifies the target resource (company) and time window. It distinguishes itself from the sibling tool 'entity_profile' by contrasting dynamic changes vs 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 Guidelines4/5

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

The description provides explicit guidance on when to use this tool (for recent changes over a window) and when not to (for static profiles, use entity_profile). Example queries clarify context. No explicit exclusions for other scenarios.

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

A3.6/5.0
Disambiguation4/5

Individual tools have detailed descriptions and mostly distinct purposes, but the mix of domains (scripture, finance, data lookup) could confuse an agent about which tool to choose for a given task. However, within each subdomain, tools are clearly differentiated.

Naming Consistency2/5

Tool names follow no consistent pattern: some are verb_noun (ask_pipeworx, compare_entities), some are noun_verb (entity_profile, recent_changes), and some are single verbs (forget, recall). The mix of snake_case with varied starting parts makes naming unpredictable.

Tool Count1/5

With 23 tools but only 3 related to the server's stated purpose (Bhagavad Gita), the tool count is severely mismatched. The vast majority belong to data analytics and finance, making the set feel bloated and mis-scoped.

Completeness1/5

For a Bhagavad Gita server, only list_chapters, get_chapter, and get_verse are provided, missing obvious features like search, commentary comparison, or multiple translations. The other tools are irrelevant to the domain, leaving it extremely incomplete.

Resources