Gsa Auctions

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 7,397 across 1947 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. An "llm_error" also carries refusal_cause ("wall" = our time budget ended before any model call was made; "route"|"threw"|"unparseable"|"wall_after_failure" = a model call failed); refusal_reason itself is unchanged. 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.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "What was Apple's fiscal 2023 revenue?"
    },
    "name": "ask_pipeworx_grounded"
  }
}

Input Schema

Name	Required	Description
`q`	No	Alias for question.
`ask`	No	Alias for question.
`text`	No	Alias for question.
`input`	No	Alias for question.
`query`	No	Alias for question.
`prompt`	No	Alias for question.
`message`	No	Alias for question.
`question`	Yes	Your question in natural language. Accepts query, q, prompt, text, input, ask, message as aliases.
`prefer_tool`	No	Run this exact tool instead of letting the router choose — use a name from a previous answer's alternatives[]. Arguments are still filled from your question. Unknown names are refused, never guessed.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 25, 2026

addedInput schema / properties / prefer_tool

Added value: +{
+  "description": "Run this exact tool instead of letting the router choose — use a name from a previous answer's alternatives[]. Arguments are still filled from your question. Unknown names are refused, never guessed.",
+  "type": "string"
+}

Changed3 schema fields changedOct 13, 2026

addedInput schema / properties / ask

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / message

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

changedInput schema / properties / question / description

Previous value: -"Your question in natural language. Accepts query, q, prompt, text, input as aliases."New value: +"Your question in natural language. Accepts query, q, prompt, text, input, ask, message as aliases."

Changed1 schema field changedSep 19, 2026

addedInput schema / examples

Added value: +[
+  {
+    "question": "What was Apple's fiscal 2023 revenue?"
+  }
+]

First observedJul 2, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already declare a read-only, idempotent, non-destructive, open-world call, so the safety profile is covered. The description goes well beyond that by enumerating both success and refusal envelopes and the five refusal reasons, but the refusal_cause taxonomy ('wall', 'route', 'threw', 'unparseable', 'wall_after_failure') is dense internal jargon the agent cannot act on, and there is no output schema to anchor it. Valuable, but partly unactionable detail.

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?

Front-loaded with the core differentiator and the use condition, then the return contract. The refusal/refusal_cause sentence is one long parenthetical that could be trimmed without losing decision-relevant information, but overall every sentence carries signal.

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?

For a router-plus-extractor with no output schema, the description supplies the return envelope, the failure envelope, the cost tradeoff, and the selection rule. An agent has everything needed to decide to call it and to interpret both success and refusal responses.

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?

Schema description coverage is 100% and the seven aliases plus prefer_tool are already documented in the schema itself. The description adds no parameter-level detail — notably it never mentions prefer_tool or that arguments are auto-filled — so it neither compensates nor detracts. Baseline 3 applies.

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?

States a specific mode (grounded, verbatim-extraction answering) and explicitly frames it as 'same routing as ask_pipeworx' but with an extraction step constrained to tool results. The agent can tell exactly what this tool does and how it differs from the sibling router without opening any schema.

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?

Gives an explicit use condition ('whenever an answer will be quoted, cited, or acted on... must not invent facts') with concrete examples (financial verdicts, legal claims, medical lookups), plus an explicit when-not ('prefer ask_pipeworx for casual lookups') justified by the extra LLM call cost.

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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