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Glama

Apology

by io.github.pipeworx-io

Apology Generate

apology_generate
Read-onlyIdempotent

Generate a tailored apology given an offense description, relationship (partner/friend/boss/ex/self/etc.), sincerity tone (genuine/performative/tactical/none/desperate), and delivery medium (text/email/in_person/voicemail). Returns ready-to-use apology text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mediumNo
offenseYes
sincerityNo
relationshipNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
apologyYesGenerated personalized apology text

Schema Changelog

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

  1. Added
  2. Removed
  3. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint true, idempotentHint true, and destructiveHint false, so the description's claim that it 'returns ready-to-use apology text' adds context but does not contradict. No additional behavioral traits disclosed beyond annotations.

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?

Two sentences: first sentence defines purpose and all parameters, second sentence states output. No extraneous text, front-loaded with core function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all parameters and output type. Minor gap: it does not note that only offense is required. Otherwise, sufficiently complete for a tool with rich annotations and no output schema needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description explicitly lists all four parameters with example values for each enum (relationship, sincerity, medium). This compensates fully, providing meaning the schema lacks.

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 states the tool generates a tailored apology, with specific verb 'generate' and resource 'apology'. It lists all input parameters and distinguishes it from sibling tools which are unrelated (e.g., research, betting, visibility).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use or when not to use this tool. It does not mention alternatives or prerequisites, relying on the tool's name and description to imply usage.

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

All Apology toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates with beta explicitly identical to the stable version, causing potential misselection. ai_visibility_check and scan_competitor_ai_presence overlap heavily, and resolve_entity/discover_tools/ask_pipeworx all serve lookup purposes. Many tools are distinct, but the boundaries around the core query tools are blurry.

Naming Consistency2/5

Naming is inconsistent: mostly snake_case but mixed verb styles (ask_pipeworx vs pipeworx_feedback vs resolve_entity), brand prefixes applied irregularly, and no uniform convention (e.g., subscribe/unsubscribe/list_subscriptions vs forget/remember/recall vs polymarket_arbitrage/edges/edge_tracker). Some names are descriptive, but the set lacks a predictable pattern.

Tool Count2/5

32 tools is above the 25 threshold for a heavy surface, and the server mixes unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, apology generation). While a large data platform could justify many tools, the random inclusions (apology_generate, generate_llms_txt, scan_dependency) suggest a lack of scoping. Several tools could be consolidated without loss.

Completeness4/5

For the dominant data-research/prediction-market domain, coverage is strong: lookup, grounded verification, research, entity resolution, comparison, arbitrage scanning, fill risk, subscriptions, memory, and discovery are all present. Minor gaps exist (no direct account management beyond subscriptions, no tool to modify stored memories), but agents can mostly achieve their goals. The stray non-domain tools do not hurt completeness of the core platform.

Resources