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Jina Reader

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 5,767 across 1506 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?

Even with readOnlyHint, idempotentHint, openWorldHint, and destructiveHint annotations, the description adds substantial behavioral detail: it explains the grounded extraction process, the explicit refusal mechanism with specific refusal_reason values, and the additional LLM cost. This goes well beyond what annotations convey and does not contradict 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?

The description is dense but every clause earns its place: purpose, routing behavior, output contract, refusal contract, use cases, and cost tradeoff are all covered without redundancy. Information is front-loaded with the core purpose before the return shapes and usage guidance.

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?

For a tool with no output schema, the description fully specifies the success and refusal response shapes, the conditions triggering refusal, and when to choose this tool over its sibling. It also clarifies routing behavior and cost, making the tool self-sufficient for an agent to invoke 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 description coverage is 100%, and the schema already documents that all parameters are aliases for the question string. The description does not add parameter-level details, so the baseline of 3 is appropriate given the schema handles the semantics fully.

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 answer mode for high-stakes reads, with a specific verb and resource. It explicitly differentiates itself from ask_pipeworx by noting it extracts answers using only tool result content and returns refusal reasons rather than inventing facts.

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 an answer will be quoted, cited, or acted on... must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'). It also explains the cost tradeoff of one extra LLM call, giving the agent clear decision criteria.

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

A4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and specialized tools like entity_profile or validate_claim that can answer similar questions. This creates ambiguity for an agent trying to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, with most using a verb_noun structure (e.g., ask_pipeworx, compare_entities, resolve_entity). There are no mixed conventions or chaotic naming.

Tool Count3/5

With 31 tools, the server is on the heavy side. While each tool has a distinct purpose, the number is borderline for a coherent set and could be streamlined, especially given the overlapping functionality.

Completeness3/5

The tool set covers a wide range of query and analysis tasks, including data lookup, comparison, betting research, memory, and subscriptions. However, there are notable gaps (e.g., no update/delete for most data, no user management) and some tools seem out of place (e.g., generate_llms_txt).

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