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Deezer

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?

Beyond the annotations, the description discloses the refusal behavior, exact refusal reasons, return shape including evidence and fetched_at, and the extra LLM call cost. It also clarifies that answers are grounded only in the tool result, adding meaningful 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.

Conciseness4/5

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

The description is front-loaded with the core purpose and then delivers actionable details about output, refusal, and when to use it. It is dense but each clause contributes useful guidance, though it could be tightened slightly without losing value.

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?

The tool has no output schema, so the description compensates by specifying the success and refusal response shapes. It also covers routing, when to prefer the alternative, cost implications, and the grounding guarantee, making it complete for correct 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 coverage is 100%, and all parameters are documented aliases for the natural-language question. The description does not add parameter-level detail beyond that, but it is not required to because the schema fully specifies the input contract.

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 verb and resource: an answer mode that extracts answers only from tool results, returning evidence and confidence. It explicitly differentiates from the sibling ask_pipeworx by noting the same routing but grounded extraction behavior.

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 the tool: when answers will be quoted, cited, or acted on in high-stakes contexts, and when not to: prefer ask_pipeworx for casual lookups. It also names the alternative and the cost tradeoff, leaving no ambiguity.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap in purpose (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research all route queries to structured data), and the set mixes Deezer music tools with an unrelated Pipeworx/Polymarket suite. An agent would struggle to pick the right tool for a given request.

Naming Consistency2/5

Naming is mixed: single-word nouns (album, artist, track, chart), verb_noun snake_case (list_subscriptions, resolve_entity), and verbose multi-concept names (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern across the set.

Tool Count1/5

37 tools for a server named 'Deezer' is far beyond a music API scope; the overwhelming majority are unrelated data-research, prediction-market, and utility tools. This is an extreme mismatch between the server name and the tool surface.

Completeness2/5

For the Deezer music domain, the surface is incomplete (no playlists, user library, lyrics, or radio), while the many unrelated tools each cover only fragments of their domains. The overall set lacks coherent coverage of any single purpose.

Resources