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Caniuse

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 5,743 across 1500 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.

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses behavior beyond annotations: it returns a structured success object or an explicit refusal with a refusal_reason enum, uses ONLY the tool result, and costs one extra LLM call. Annotations already mark it read-only and idempotent, and the description adds valuable operational context without contradicting them.

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?

Although detailed, every sentence serves a purpose: purpose, mechanism, return/refusal shape, when-to-use, and cost trade-off. The description is front-loaded with the core distinction and structured with concrete examples.

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?

Despite no output schema, the description fully documents the return shape and refusal reasons, making the tool's behavior predictable. It also covers the routing mechanism, cost implication, and selection criteria, so an agent has everything needed to call it correctly.

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 coverage is 100% and the only semantic parameter is 'question' with multiple aliases. The description adds no parameter-specific meaning beyond what the schema already documents, so the 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?

Description states a specific purpose: a hallucination-resistant answer mode that extracts answers only from tool results, and it distinguishes itself from the sibling ask_pipeworx by emphasizing evidence and explicit refusals. The verb 'extracts' and resource 'answer mode' make the function clear.

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?

Explicitly states when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups') due to the extra LLM call cost. This provides clear decision criteria relative to the sibling tool.

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

A3.9/5.0
Disambiguation2/5

The tool set has significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and deep_research, entity_profile, compare_entities, recent_changes, and validate_claim all retrieve structured data with overlapping capabilities. The five Polymarket-oriented tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) further blur boundaries. Agents will struggle to select the right tool without reading very long descriptions.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (ask_pipeworx, compare_entities, validate_claim), and the polymarket_* cluster is consistently prefixed. However, a few tools are bare nouns (feature, support, search) and the remember/forget/recall trio deviates from the dominant pattern, creating minor inconsistency.

Tool Count2/5

35 tools is excessive for a server named 'Caniuse' — only 4 tools actually pertain to browser compatibility (feature, support, search, list_browsers), while 31 are Pipeworx data tools. The server name misrepresents the content, and the sheer number overwhelms rather than scopes the surface.

Completeness3/5

For the caniuse domain, coverage is complete (search, feature, support, list_browsers). The Pipeworx side includes meta-tools (discover_tools, suggest_questions), retrieval, memory, subscriptions, and feedback, but some tools require accounts and there are gaps like no direct way to list all data sources without discover_tools. The overall surface is broad but lacks obvious missing operations for any single coherent domain.

Resources