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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?

Beyond the readOnly/idempotent annotations, it discloses refusal behaviors with exact refusal_reason values, the evidence-quote return field, the extra LLM call cost, and the constraint that it uses only tool-result content. This is rich behavioral context the annotations do not provide.

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 sentence earns its place: core mode, routing mechanism, return/refusal shapes, use cases, and cost trade-off. It is front-loaded with the most important phrase and avoids filler.

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 compensates fully: it enumerates the success shape, failure shape, refusal reasons, usage context, and cost comparison. An agent has everything needed to select and invoke the tool 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?

The input schema already has 100% coverage, describing 'question' and all aliases. The description does not add new parameter-level meaning; it focuses on internal behavior. Baseline 3 is appropriate because the schema carries the parameter documentation 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 a specific verb and resource: 'Hallucination-resistant answer mode for high-stakes reads' and explains it extracts answers only from tool results. It explicitly distinguishes itself from ask_pipeworx by the grounded extraction behavior and names the sibling alternative.

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives a clear exclusion and alternative: 'prefer ask_pipeworx for casual lookups.' No inference is required.

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes, especially ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), ask_pipeworx_grounded, and deep_research. The Polymarket tools are more distinct, but the boundary between Maven search tools and broader discovery tools like search, search_by_coords, discover_tools, and suggest_questions is not always obvious.

Naming Consistency2/5

Naming is a mix of snake_case actions, branded prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*, plus inconsistent patterns like ai_visibility_check vs scan_competitor_ai_presence. Some clusters are internally consistent, but the overall set follows no predictable convention.

Tool Count2/5

35 tools is heavy for a server named 'Maven Central', and only a handful actually relate to Maven artifacts. The rest are Pipeworx research, prediction-market, memory, subscription, and utility tools, making the set feel sprawling rather than purpose-scoped.

Completeness3/5

For the Maven Central domain, search, coordinate lookup, version listing, and latest-version retrieval cover core read-only needs. However, there is no direct artifact metadata/POM/dependency inspection, and the unrelated Pipeworx and Polymarket tools dilute the surface without filling obvious gaps in the stated domain.

Resources