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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 5,798 across 1517 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds substantial context beyond that: refusal reason taxonomy, verbatim evidence requirement, strict source-grounded extraction, and the extra LLM call. There is no contradiction with the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and information-rich, front-loading the core behavior before usage and cost guidance. The '5,798 across 1517 sources' detail is slightly extra but supports the grounding claim, and each sentence contributes something actionable.

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?

Although there is no output schema, the description fully specifies both success and refusal return shapes, including all possible refusal reasons. Combined with explicit usage guidance and sibling differentiation, nothing essential is missing for correct selection and invocation.

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%, so the schema already documents the single meaningful parameter and its aliases. The description does not add user-facing parameter details beyond internal orchestration, 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 clearly identifies this as a hallucination-resistant, grounded answer mode that routes through Pipeworx tools and extracts answers only from fetched results. It explicitly contrasts itself with ask_pipeworx by describing its stricter evidence-based behavior, so an agent can distinguish it from siblings without opening schemas.

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: whenever an answer will be quoted, cited, or acted on, and when fact invention is unacceptable. It also names the alternative, ask_pipeworx, and tells the agent to prefer that for casual lookups because of the extra LLM call cost.

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
Disambiguation3/5

Several tools cluster around the same core purpose: the three ask_pipeworx variants, the three census reverse-geocoders, and the six Polymarket analysis tools. Descriptions are detailed enough to disambiguate most choices, but ask_pipeworx_beta is currently identical to ask_pipeworx, creating genuine ambiguity. An agent could easily select the wrong tool in these overlapping families.

Naming Consistency3/5

Names mix verb-initial actions (ask_pipeworx, compare_entities, resolve_entity) with noun-initial compound names (census_block, entity_profile, polymarket_edges). The polymarket_* family is internally consistent, but the set as a whole lacks a uniform verb_noun convention. Single-word verbs like remember, recall, and forget further break the pattern.

Tool Count2/5

At 34 tools, this exceeds the 'too many' threshold of 25 and includes clear redundancy: ask_pipeworx_beta duplicates ask_pipeworx, county_for_point is a thin wrapper over the same service as census_area/census_block, and scan_competitor_ai_presence just loops ai_visibility_check. The broad scope does not justify this many tools, and the set would be better split into focused servers.

Completeness4/5

Each sub-domain has solid lifecycle coverage: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and company research has resolve_entity/entity_profile/compare_entities/recent_changes. Minor gaps exist (e.g., no direct pipeworx:// citation-fetching tool), but no critical dead ends that would cause agent failures.

Resources