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Glama

Always Seven

by io.github.pipeworx-io

Always Seven Generate

always_seven_generate
Read-onlyIdempotent

Calls the StupidAPIs always-seven endpoint, which claims to return a random number between 1 and 10 but always returns 7. Optional question and force params are accepted but have no effect on the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoAttempt to force a different number (1-10). It will not work.
questionNoYour question. It will not affect the number.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
numberYesRandom number between 1 and 10 (always 7)

Schema Changelog

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

  1. Added
  2. Removed
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description goes beyond annotations, revealing the always-7 behavior and that parameters have no effect, which is crucial for correct invocation.

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 clear sentences, front-loaded with main purpose, no wasted words.

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?

For a simple, humorous tool with full annotations and output schema, the description adequately covers all behavioral nuances.

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 already describes parameters; the description adds that they are accepted but have no effect, providing critical behavioral context beyond schema.

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 calls an API that 'always returns 7', specifying the verb-resource relationship and the deceptive nature. It distinguishes from siblings by highlighting unique behavior.

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

Usage Guidelines3/5

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

The description implies the tool is for obtaining the number 7 despite claiming randomness, but lacks explicit guidance on when to use vs alternatives or when not to use.

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
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying 5,708-tool catalog, and ask_pipeworx_beta is explicitly identical today. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries — both bet_research and polymarket_edges claim 'should I bet on X', and discover_tools versus suggest_questions both serve discovery. Despite very detailed descriptions, an agent would frequently struggle to pick the correct tool.

Naming Consistency3/5

The naming is mostly snake_case and readable, with coherent micro-families (polymarket_*, pipeworx_*, ask_pipeworx variants, remember/recall/forget). However, patterns are mixed: verb_noun (compare_entities, resolve_entity, generate_llms_txt) sits alongside noun-led names (entity_profile, recent_alerts, pipeworx_trending), and entity-related tools use three different conventions (compare_entities, resolve_entity, entity_profile). It's consistent within families but not across the full set.

Tool Count2/5

32 tools is well past the 25-tool threshold for a coherent set, and the scope is a scattered grab bag: a joke RNG, an AI-visibility probe, a 5,708-tool data router, prediction-market arb analytics, key-value memory, subscriptions, npm dependency scanning, llms.txt generation, and a feedback channel. Some of these are arguably platform additions rather than core tools, but as presented the count feels bloated and unfocused.

Completeness3/5

The major workflow areas are well covered — data lookup has routing, grounded mode, deep research, entity resolution, comparisons, profiles, and change feeds; memory and subscriptions each have full lifecycle coverage. However, the server repeatedly references pipeworx:// citation URIs as fetchable yet provides no record-fetching tool, and the diffuse purpose makes it hard to assess what 'complete' even means. Notable gaps exist around citation resolution and execution of the arbitrage signals the Polymarket tools generate.

Resources