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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 6,783 across 1784 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.
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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations cover only the safety/idempotency profile; the description adds substantial behavior beyond them: full success return shape ({answer, evidence, confidence, source, fetched_at}), every refusal_reason enum value with meaning, and the cost tradeoff (one extra LLM call). This is exactly the context annotations cannot provide.

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 purpose, then mechanism, return shape, refusal cases, usage conditions, and cost tradeoff — nearly every sentence carries load. The 'picks the right tool from 6,783 across 1784 sources' clause is mild marketing filler, otherwise dense and well-ordered.

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?

No output schema exists, so the description correctly carries the full return-shape burden, including success and refusal payloads. For a single-required-parameter tool, nothing an agent needs to invoke or interpret it 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 description coverage is 100%, so the schema already documents the question field and all seven aliases. The description adds no parameter-level syntax or format detail, so baseline 3 is 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?

States a specific verb+resource ('Hallucination-resistant answer mode') plus the mechanism (same routing as ask_pipeworx but EXTRACTS the answer using ONLY the tool result). Explicitly distinguishes itself from the sibling ask_pipeworx, so an agent can tell the two apart without opening either 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 explicit when-to-use ('whenever an answer will be quoted, cited, or acted on... must not invent facts') with concrete examples (financial verdicts, legal claims, medical lookups), and names the alternative with its selection condition ('prefer ask_pipeworx for casual lookups'). Nothing left to inference.

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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