auctions_coverageWhich government-auction data is currently available: per-source active lot counts and data freshness (when each source was last refreshed). Use to gauge coverage and how current the data is before relying on it.
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the exact nature of the output (per-source counts and last-refreshed timestamps), which adds behavioral detail beyond the annotations' read-only/open-world hints. No contradiction with annotations exists, and the description enriches the agent's understanding of what the tool returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that front-load the purpose and include the key output fields. Every word contributes to understanding the tool, with no redundant or missing content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description fully explains what the tool does and when to use it. The tool's simplicity means the description covers all necessary context, making it complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool accepts no parameters, and the empty schema is fully documented. Since there are no parameters to explain, the description need not provide additional semantics, and the baseline for zero parameters is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides an overview of government-auction data availability, including per-source active lot counts and data freshness. It uses specific nouns to distinguish this from sibling tools like auctions_search and auctions_closing_soon, which focus on searching or time-sensitive listings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs when to use the tool: to gauge coverage and data currency before relying on it. This provides clear context, though it does not mention when not to use it or name alternative tools, which prevents a top score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.