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Glama

Tankerkoenig

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

A4.8/5.0
Behavior5/5

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

Annotations already mark readOnlyHint, idempotentHint, destructiveHint false. Description adds significant detail: fans out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, and return structure. No contradictions.

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 thorough but front-loaded with query examples. Each sentence adds value: sources, fallback, return format, alternative. Slightly long but justified by 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 3 required params, no output schema, and complex multi-source behavior, the description covers all needed aspects: time window usage, source details, failure modes, return format, and sibling differentiation.

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 good descriptions. Description adds practical guidance for 'since' (examples, recommended values like '30d'), and clarifies 'type' is restricted to company. Exceeds baseline of 3.

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 uses explicit verb-resource pairs ('change feed for a company') and multiple example queries. It clearly distinguishes from sibling 'entity_profile', which provides static profile vs. temporal changes.

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?

Provides explicit use cases ('What's new with X', etc.) and a clear alternative ('Use entity_profile instead for static profile'). Also mentions internal fan-out and fallback behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Tankerkoenig toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools occupy nearly identical roles: ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), ask_pipeworx_grounded, and deep_research all answer research questions; polymarket_edges, bet_research, and polymarket_arbitrage overlap heavily on prediction-market opportunities; entity_profile, compare_entities, and recent_changes overlap on company research. The detailed descriptions help, but the clusters create real misselection risk.

Naming Consistency4/5

Nearly all tools follow a readable snake_case convention, many with verb_noun structure (resolve_entity, list_subscriptions, validate_claim, scan_dependency). Minor deviations exist: tankerkoenig_stations_nearby plural vs tankerkoenig_station_details/prices singular, plus noun-style names like pipeworx_feedback and pipeworx_trending, but the overall pattern is predictable.

Tool Count2/5

34 tools is heavy, and the problem is compounded by the server being named Tankerkoenig: only 3 of the 34 tools relate to German fuel prices while the other 31 are an unrelated Pipeworx/Polymarket/memory/subscription toolkit. This is a sprawling, unfocused surface rather than a well-scoped set.

Completeness3/5

For the nominal Tankerkoenig domain, stations_nearby + station_details + prices cover core lookups, though station search by name and price history are missing. For the broader bundled data/prediction-market domain, coverage is extensive but has notable gaps such as no trade execution, no general web search, and several redundant access paths that complicate the surface.

Resources