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words

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 5,721 across 1497 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 adds substantial behavioral context beyond the readOnly/idempotent annotations: it details the exact success response shape, lists explicit refusal reasons, explains that answers are grounded only in tool results, and discloses the extra LLM call cost. This fully informs the agent of the tool's operational behavior.

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: purpose, mechanism, return contract, use cases, and cost tradeoff are all covered without redundancy. It is well-structured, with the core purpose front-loaded.

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 one required parameter and a fully described schema, the description covers all necessary context: return format, refusal behavior, use-case boundaries, and cost comparison with the sibling tool. There is no missing information an agent needs to invoke 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 schema already documents the question parameter and all aliases. The description adds no additional parameter semantics, 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 identifies a specific purpose: a hallucination-resistant, grounded answer mode for high-stakes reads. It clearly differentiates from ask_pipeworx by explaining that it extracts the answer using ONLY the tool result, which distinguishes it from sibling 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?

Explicit when-to-use guidance is provided: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and gives the exclusion condition: 'prefer ask_pipeworx for casual lookups.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All words toolsInspect
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TDQS

A3.6/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is overlap between query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, and between prediction market tools like bet_research and polymarket_edges. Descriptions help differentiate, but the boundaries are not always clear.

Naming Consistency2/5

Tool names are all snake_case but lack a consistent pattern. Some start with verbs (ask, compare, find), others are nouns (autocomplete, entity_profile), and many are long phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). The naming feels ad-hoc and not easy to predict.

Tool Count2/5

At 35 tools, this server is over-packed for a server named 'words'. Many tools are unrelated to words (e.g., prediction markets, subscriptions, entity profiles). The scope is too broad, making it feel like a catch-all rather than a coherent set.

Completeness3/5

The word tools are limited (only 6), leaving obvious gaps for a word-focused server (e.g., no dictionary lookup, no word definitions). However, the server covers a wide range of data domains through meta-tools like ask_pipeworx, which compensates but makes the purpose unclear.

Resources