Worms

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 7,197 across 1869 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 18, 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"
+}

Changed4 schema fields changedSep 30, 2026

addedInput schema / examples

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

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."

First observedJun 9, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, it discloses the exact success payload shape, the full refusal_reason vocabulary, the refusal_cause sub-codes for llm_error, and the latency/cost tradeoff of an extra model call. This is behavioral detail the annotations cannot convey.

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-loads the purpose and the sibling tradeoff, then the return contract. Dense but nearly every clause earns its place; the refusal_reason/refusal_cause enumeration is slightly heavy in one sentence but is load-bearing for parsing responses.

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?

With no output schema, the description fully documents both the success and refusal response shapes, including edge-case cause codes, so an agent knows how to interpret every return path. Nothing needed to call or interpret the result is missing.

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 coverage is 100% and the alias fields are already documented, so the schema carries the parameter burden. The description adds only routing context ('fills arguments', 7,197 tools across 1869 sources) and does not clarify prefer_tool's fallback behavior beyond what the schema states.

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 (extractive, hallucination-resistant answering) and the pipeline it runs (route → fill arguments → fetch → extract only from tool result). It explicitly contrasts itself with the sibling ask_pipeworx, so an agent can select between the two without reading the 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 when-to-use rule ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') with concrete example domains, plus a when-not rule ('prefer ask_pipeworx for casual lookups') tied to a stated cost (one extra LLM call).

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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