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Electricity Maps

by io.github.pipeworx-io
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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 7,293 across 1902 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

NameRequiredDescriptionDefault
qNoAlias for question.
askNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
messageNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input, ask, message as aliases.
prefer_toolNoRun 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.

  1. Changed1 schema field changed
    • 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"
+}
  2. Changed4 schema fields changed
    • 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."
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses how the answer is produced, that it uses ONLY the tool result, the exact return fields, and the five refusal reasons. It also explains the llm_error refusal_cause distinctions and the extra LLM-cost tradeoff, which are behavioral details not captured by annotations.

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?

The description is front-loaded with the core value proposition and proceeds through usage, return shape, refusal modes, and cost tradeoff. It is dense but mostly purposeful; the detailed refusal and error-cause enumeration adds length but supports correct interpretation.

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 complex routing-and-extraction tool, the description is complete: it explains the routing behavior, grounding constraint, success response shape, refusal modes, error causes, and when not to use it. Although there is no output schema, the return structure and failure semantics are described explicitly.

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 input schema has 100% description coverage, including all question aliases and prefer_tool behavior, so the schema already carries parameter meaning. The description adds some context about routing and argument filling but does not add syntax or semantics beyond what the schema provides, making the baseline of 3 appropriate.

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 states a specific operation: a hallucination-resistant answer mode that routes to a tool, fetches data, and extracts an answer only from the tool result. It explicitly distinguishes itself from ask_pipeworx by describing grounded extraction and its refusal behavior, so an agent can identify the tool's role without opening schemas.

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?

It gives explicit use conditions: use whenever an answer will be quoted, cited, or acted on and facts must not be invented, with examples such as financial verdicts, legal claims, medical lookups, and public statements. It also names the alternative and the tradeoff: prefer ask_pipeworx for casual lookups because grounded mode costs 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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