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trivia

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?

Beyond the annotations (read-only, idempotent), the description discloses the exact success and refusal return shapes, the specific refusal reasons, and the extra LLM call cost. It also clarifies that the tool uses only the tool result for the answer, which is not visible in the annotations and is essential for safe invocation. No contradiction with 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?

The description is dense but every clause earns its place: mode, routing behavior, return contract, refusal reasons, usage guidance, and cost tradeoff. The core purpose is front-loaded and the details are all decision-relevant for an agent.

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?

There is no output schema, but the description fully compensates by listing the success fields, refusal fields, and possible refusal reasons. Combined with annotations and parameter schema, the agent has everything needed to decide when to call this tool and what to expect from it.

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% coverage of the single logical question parameter and its aliases. The description adds context that the question is routed across many sources and tools, but it does not add much semantic detail beyond what the schema already documents, so the baseline of 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?

The description clearly identifies this as a hallucination-resistant, grounded answer mode for high-stakes reads, and explicitly contrasts it with ask_pipeworx. It names a distinct behavior (extraction only from tool results, with explicit refusals) that separates it from siblings and from casual lookup tools.

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?

The description states exactly when to use this tool: whenever the answer will be quoted, cited, or acted on and factual invention is unacceptable. It also gives the alternative by saying to prefer ask_pipeworx for casual lookups, citing the extra LLM call cost as the tradeoff.

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

A3.6/5.0
Disambiguation2/5

Several tools have notably unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and polymarket_edges, polymarket_arbitrage, and bet_research heavily overlap in surfacing betting opportunities. Entity_profile, recent_changes, and compare_entities also share overlapping research scope, making misselection likely.

Naming Consistency3/5

Most names use snake_case and a roughly readable verb_noun style (resolve_entity, compare_entities, list_categories), but conventions vary: some are bare nouns (entity_profile), some are plain verbs (remember, forget), and ask_pipeworx/pipeworx_* break the pattern. It is readable overall, but not a consistent scheme.

Tool Count2/5

34 tools is far more than the 'trivia' name implies, and most of them (Pipeworx research, Polymarket analysis, memory, subscriptions) are unrelated to trivia. The set reads as an entire platform bundled together rather than a purpose-scoped server.

Completeness2/5

For the stated trivia purpose, the surface is missing core lifecycle features like quiz sessions, answer validation, or scoring; the few trivia tools are just category/reference lookups. As a general data/research server it is broad, but there are significant gaps and no coherent domain model tying the tools together.

Resources