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Ted Eu

by io.github.pipeworx-io

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 5,798 across 1517 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.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations by explaining that the answer is extracted using ONLY what the tool result contains. It also discloses the exact success response shape and five explicit refusal reasons, which is essential behavior because there is no output schema. No contradiction with the readOnly/openWorld/idempotent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence earns its place: purpose, mechanics, return contract, refusal reasons, usage context, cost tradeoff, and alternative tool. The key discriminator, grounded vs. non-grounded, is front-loaded, and the length is justified by the lack of an output schema.

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?

The description fully compensates for the missing output schema by spelling out both success and refusal response shapes. It also covers the routing behavior, source breadth, high-stakes use cases, and the tradeoff against ask_pipeworx, leaving nothing essential for an agent to infer.

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 covers 100% of parameter semantics, including the main question parameter and all accepted aliases, so the description does not need to add parameter details. The description adds useful routing context but no specific question-format guidance; baseline 3 is appropriate because the schema carries the parameter burden.

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 opens with 'Hallucination-resistant answer mode for high-stakes reads,' which clearly states a specific purpose and resource. It distinguishes itself from ask_pipeworx by explicitly describing the grounded behavior and calling out the same routing with a different extraction guarantee. There is no ambiguity about what this tool does.

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?

Usage guidance is explicit: use when an answer will be quoted, cited, or acted on and facts must not be invented, with concrete examples like financial verdicts and legal claims. It also provides the negative rule, 'prefer ask_pipeworx for casual lookups,' and discloses the cost tradeoff of 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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose described in detail. Even tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by hallucination resistance, account requirements, and use cases. Polymarket tools are each specialized (arbitrage, edges, fill risk, etc.). No two tools appear to do the same thing.

Naming Consistency5/5

All tool names follow snake_case consistently. They use descriptive verb-noun patterns (e.g., ask_pipeworx, bet_research, compare_entities, subscribe). No mixing of conventions or ambiguous names.

Tool Count4/5

32 tools is on the higher side but justified by the server's broad scope covering company research, prediction markets, data lookups, memory, subscriptions, and more. Each tool serves a distinct function, though the count might feel slightly heavy for a single server.

Completeness4/5

The toolset covers core workflows for company analysis, prediction market operations, data retrieval, and system management (memory, subscriptions). Minor gaps exist (e.g., no direct tool for non-company entity profiles beyond drugs), but the overall surface is comprehensive for the intended multi-purpose assistant.

Resources