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Cftc Cot

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 6,997 across 1839 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.
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?

Annotations already cover the read-only/idempotent/open-world safety profile, but the description adds substantial behavior beyond them: the full success return shape (answer/evidence/confidence/source/fetched_at), every refusal reason enum, the extract-only-from-result constraint, and the cost implication of one extra LLM call. This is exactly the context the structured fields do not carry.

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 what and the differentiation, then the return shapes, then the usage rule. Dense but every clause carries decision-relevant content; the only slight cost is the parenthetical-heavy middle section that an agent must parse.

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 specifies return values, the success payload fields, and all five refusal cases, plus the cost tradeoff versus the sibling. Nothing needed to call or interpret this tool 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 question and all aliases plus prefer_tool (including the 'unknown names are refused' rule and alternatives[] source). The description reinforces routing/argument-filling behavior but adds little parameter syntax beyond the schema, so 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?

The description names a specific mode ('hallucination-resistant answer mode for high-stakes reads') and describes the exact mechanism: routing across 6,997 tools from 1839 sources, argument filling, fetching, then extraction restricted to the tool result. It explicitly separates itself from the sibling ask_pipeworx, so an agent can tell them 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 an explicit when-to-use trigger ('whenever an answer will be quoted, cited, or acted on' with concrete domains: financial, legal, medical, public statements) and an explicit when-not ('prefer ask_pipeworx for casual lookups'). The alternative is named with the condition that selects it.

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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