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Glama

Gov Auctions

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Even with strong annotations like readOnlyHint and idempotentHint, the description adds substantial behavioral detail: it performs an extra LLM call, returns evidence as verbatim quotes, and provides explicit refusal reasons for cases where the source does not answer. It also discloses the exact success and refusal response shapes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence carries essential information: behavior, return format, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the key differentiator and avoids filler.

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?

Despite no output schema, the description fully specifies the return shape and all possible refusal reasons. It also covers routing behavior, cost implications, and use-case boundaries, making it complete enough for an agent to invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all 6 parameters with 100% coverage, including the aliases for `question`, so the baseline is 3. The description clarifies the overall natural-language query behavior but does not add syntax or format details beyond what the schema already provides.

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 identifies this as a grounded, hallucination-resistant answer mode that extracts answers only from fetched tool results. It explicitly distinguishes itself from the sibling tool ask_pipeworx while stating its specific responsibility for high-stakes factual reads.

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?

The description gives explicit when-to-use guidance: use whenever an answer will be quoted, cited, or acted on and facts must not be invented. It also names the alternative ask_pipeworx and says to prefer it for casual lookups, providing a clear decision rule.

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

All Gov Auctions toolsInspect
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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