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Glama

Victorian Complaint Generate

victorian_complaint_generate
Read-onlyIdempotent

Any complaint, maximum Victorian indignation. The wifi being slow has never been taken more seriously.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lengthNo
complaintYes
recipientNo
indignationNo
complaint_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
lengthNoThe length of the generated complaint
complaintNoThe generated Victorian complaint text
recipientNoThe recipient of the complaint
complaint_typeNoThe category of complaint
indignation_levelNoThe level of indignation expressed

Schema Changelog

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

  1. Added
  2. Removed
  3. First observed

TDQS

B3.2/5.0
Behavior4/5

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

Annotations already declare this as read-only, non-destructive, and idempotent. The description adds behavioral context by indicating it accepts any complaint and applies maximum Victorian indignation, giving a sense of the output tone. It does not describe output structure, but that is covered by the output schema.

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 concise and front-loaded, using two punchy sentences that are memorable. However, it sacrifices functional detail for humor, so while it is efficient, it is not maximally informative.

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

Completeness2/5

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

For a tool with five parameters and no schema descriptions, the description is far too thin to be complete. It does not specify how parameters influence output, what inputs are required beyond 'complaint', or any edge cases, making it inadequate for reliable invocation.

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

Parameters1/5

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

With 0% schema description coverage, the burden falls entirely on the description, which does not explain any parameter. The agent is left guessing the meaning and effect of 'length', 'indignation', 'recipient', and 'complaint_type', making this dimension severely lacking.

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's function: transforming any complaint into an over-the-top Victorian-style expression of indignation. The example about slow wifi reinforces this purpose, and it is easily distinguished from all sibling tools, which are research/analytics oriented.

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 guidance is provided on when to use this tool, when not to use it, or alternatives. Although the tool is unique, the description lacks explicit usage scenarios or prerequisites, leaving the agent to infer applicability solely from the whimsical tone.

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

A3.6/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily: three ask_pipeworx variants, five polymarket_* tools, and two AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence) could easily be misselected. While descriptions are detailed, the boundaries between search/research/bet/compare tools are blurry enough to cause agent confusion.

Naming Consistency4/5

Tool names follow a consistent lowercase snake_case pattern, and most use a verb-first or noun-based descriptive style (ask_pipeworx, bet_research, entity_profile, validate_claim). Minor deviations like deep_research or process_v2 are absent here; the set is largely predictable and readable.

Tool Count2/5

At 32 tools, the server exceeds the 25-tool threshold for heaviness. Many tools are edge-case variants or meta-features (pipeworx_feedback, pipeworx_trending, suggest_questions) that could be consolidated. The server's stated identity as 'Victorian Complaint' also clashes with this scale, making the count feel excessive for the apparent core purpose.

Completeness4/5

The tool set provides broad coverage for data research, entity resolution, comparison, memory, subscriptions, and prediction-market analysis. It supports query, research, discover, validate, and monitor workflows with few dead ends. Minor gaps like a generic 'get_entity' or direct data-writing tools exist, but they are not core to the implied domain.