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Cataas

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?

Annotations already mark the tool as read-only, idempotent, and open-world. The description goes further by disclosing the refusal mechanism, the fact that answers are extracted only from tool results, and the unsuccessful output shape with specific refusal_reason values. 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?

Four sentences, each earning its place: mode definition, mechanism, return/refusal shape, and usage boundaries. The key differentiator is front-loaded, and the detail about the refusal structure is compact and structured.

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 having no output schema, the description fully specifies the success and failure return shapes, the refusal reasons, the routing behavior, and the cost relative to the sibling. An agent has everything needed to decide when to invoke this tool and what to expect.

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% with aliases fully documented in the input schema. The description adds no parameter-level detail beyond framing the input as a natural-language question, which the schema already conveys. 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?

The description uses specific verbs ('answer', 'extracts') and names the resource ('high-stakes reads'), and clearly distinguishes itself from sibling ask_pipeworx by emphasizing hallucination resistance and explicit refusal behavior. An agent can immediately understand this is a grounded Q&A mode, not just another lookup.

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?

It explicitly states when to use the tool ('quoted, cited, or acted on', high-stakes reads) and when not to (casual lookups should use ask_pipeworx), backed by a concrete cost tradeoff (one extra LLM call). This is exactly the kind of actionable routing guidance that prevents misuse.

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

A3.8/5.0
Disambiguation2/5

Several tools intentionally overlap: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and ask_pipeworx/ask_pipeworx_grounded/deep_research plus discover_tools/suggest_questions sit close together. The long descriptions clarify differences, but an agent still has to choose between near-equivalent entry points.

Naming Consistency3/5

All names are readable snake_case, but there is no single consistent convention: verb-led names like list_tags and resolve_entity sit alongside noun-led names like random_cat, entity_profile, and polymarket_arbitrage. Domain prefixes like polymarket_ and pipeworx_ help, but the mixed grammar makes the surface less predictable.

Tool Count2/5

34 tools is far beyond what a cat-image server needs; only 3 tools relate to Cataas, while the rest form a sprawling Pipeworx data, prediction-market, memory, and subscription suite. The count is in the 'too many' range and most tools are outside the server's apparent stated domain.

Completeness3/5

The cat-image core covers random cats, tag-filtered cats, and tag listing, but omits other Cataas-style operations like fetching by cat ID or creating cat images with text/effects. The embedded Pipeworx side is broad, but it does not fill the gaps in the server's named cat API purpose.

Resources