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Glama

Gov Auctions

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?

Adds behavioral context beyond annotations: fans out to multiple sources, includes fallback logic, notes USPTO soft-failure. Describes return structure (changes[], total_changes, citation URIs). No contradiction with annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false).

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?

Well-structured: starts with example queries, then sources, parameters, return structure, and alternative. Every sentence adds value, though slightly lengthy. Front-loaded with 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?

Complex tool with multiple external APIs and no output schema, but description covers return structure, limitations (USPTO sunset), and fallback. All three parameters fully described with examples. Complete for effective use.

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% but description adds significant meaning: explains 'since' accepts ISO date or relative shorthand with examples, 'type' only supports 'company', 'value' accepts ticker or CIK. This goes well beyond schema 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: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It lists example queries and specifies data sources (SEC EDGAR, GDELT→GNews, USPTO), distinguishing it from sibling 'entity_profile' which provides 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?

Explicitly indicates when to use (recent changes over a window) and when not to ('Use entity_profile instead when you want the static profile'). Describes fallback behavior (GDELT→GNews) and soft-failure for USPTO, providing clear context for selection.

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
Disambiguation2/5

Several tools occupy nearly the same question-answering niche: ask_pipeworx, ask_pipeworx_beta (currently identical by its own description), ask_pipeworx_grounded, deep_research, and validate_claim are easy to confuse. The polymarket suite also has overlapping edge/arbitrage/fill-risk boundaries, and discover_tools/suggest_questions both serve onboarding. The verbose descriptions help, but the set as a whole creates real misselection risk.

Naming Consistency4/5

Most tools follow clear snake_case verb_noun or domain-prefix patterns (auctions_search, polymarket_edges, subscribe/unsubscribe, remember/recall/forget). Minor inconsistencies exist: auction_lot_details is singular while the auction group is plural, and polymarket_edges versus polymarket_edge_tracker breaks the prefix pattern slightly. Overall the naming is predictable and readable.

Tool Count2/5

36 tools is well above the 25-tool threshold and feels bloated for a server named 'Gov Auctions': only 5 tools actually concern auctions, while the rest are general Pipeworx research, prediction-market, memory, subscription, and utility tools. Even as a general data platform, the count is heavy and includes several overlapping meta-tools.

Completeness4/5

For the auction-specific surface, the set covers search, lot details, closing-soon, historical sold prices, and data coverage, which is a solid read-only lifecycle. The main gaps are non-critical: no auction-category browser, no auction-specific alert/subscription type, and no bidding workflow. The broad research/esolution tools fill in most adjacent data needs even if they dilute the server's stated focus.

Resources