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Data Europa

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

A4.7/5.0
Behavior5/5

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

Discloses that it extracts only from tool result, returns verbatim evidence, provides refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), and costs an extra LLM call. This goes beyond the annotations which only state read-only, open-world, idempotent, non-destructive.

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?

Description is moderately long but front-loads purpose, then explains behavior and usage guidance; each sentence serves a distinct function with no 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?

Explains output format including success and refusal shapes, lists refusal reasons, mentions cost comparison, and gives domain examples (financial, legal, medical, public statements); complete for an agent to decide and use.

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 has 100% coverage with all parameters described as aliases for question; description does not add additional parameter semantics beyond what the schema already states; therefore baseline 3.

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?

Clearly states it is a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishes from ask_pipeworx by noting same routing but grounded extraction, and specifies use cases (quotable, citable, actionable answers).

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?

Explicitly states when to use (when an answer will be quoted, cited, or acted on) and when to prefer ask_pipeworx (casual lookups) due to 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

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded are highly similar; deep_research also overlaps with ask_pipeworx. This makes it hard for an agent to distinguish which to use.

Naming Consistency2/5

Tool names are inconsistent, mixing camelCase (ask_pipeworx, ai_visibility_check) with snake_case (deep_research, compare_entities). Some are verb phrases, others are nouns (groups, tags), with no unified pattern.

Tool Count2/5

With 36 tools covering EU open data, general data retrieval (Pipeworx), and prediction markets (Polymarket), the count is too high for a coherent, focused server. Many tools are redundant or meta-tools.

Completeness2/5

For a server named 'Data Europa', the EU open-data tools are basic (search, package, groups) lacking update/delete or analysis. The additional Pipeworx/Polymarket tools are extensive but unrelated, making the overall surface incomplete for the implied domain.

Resources