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Glama

Xero

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 5,767 across 1506 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?

Adds substantial behavioral context beyond readOnlyHint and idempotentHint: it discloses refusal behavior, exact refusal_reason values, no-invention guarantees, and return payload shape. These details align with the annotations and tell the agent what can and cannot happen.

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?

Every clause earns its place: purpose, mechanics, return/refusal contract, use cases, and trade-off are packed into a compact description. The opening phrase immediately signals the tool's value proposition without needing to read the schema.

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?

Covers all decision-relevant information: mode definition, routing mechanism, response shape, refusal enum, high-stakes use cases, and comparison to the sibling tool. With no output schema, the explicit return payload and refusal reasons are necessary and sufficiently provided.

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 schema already documents the single question parameter with 100% coverage, including its natural-language nature and all aliases. The description adds no field-level semantics beyond what the schema provides, so baseline 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?

States a specific mode — grounded, hallucination-resistant Q&A — and explicitly mentions identical routing to ask_pipeworx. It clearly distinguishes itself by promising evidence-grounded extraction and explicit refusal when the data doesn't answer.

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?

Provides explicit use guidance: prefer grounded mode when answers will be quoted, cited, or acted on, and prefer ask_pipeworx for casual lookups. The extra LLM call cost is an explicit trade-off to help the agent decide.

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

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap (e.g., ai_visibility_check and scan_competitor_ai_presence are related; memory tools remember/recall/forget form a clear subgroup). Descriptions are detailed enough to differentiate, but the broad scope may cause occasional mis-selection.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (list_accounts, get_profit_and_loss), others are single words (forget, recall), and some use snake_case with mixed verbs (ai_visibility_check, ask_pipeworx, bet_research). No uniform pattern makes the set harder to navigate.

Tool Count3/5

25 tools is high but not extreme given the broad scope (accounting, betting, data queries, npm, memory, etc.). However, the server tries to cover too many domains, making it feel bloated. Each tool is individually useful, but the count is borderline excessive for coherence.

Completeness2/5

The Xero accounting subset is incomplete: only list and get operations, no create/update/delete for invoices, contacts, or accounts. Other domains (betting, npm) are covered well, but the core accounting purpose has significant gaps that will hinder agents.

Resources