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Glama

Password

by io.github.pipeworx-io

Check Password

check_password
Read-onlyIdempotent

Analyze a password's strength offline (keyless): character-set entropy, a strength rating, estimated crack time (offline fast + online throttled attacker), and specific weaknesses (length, missing character classes, sequences/repeats, common passwords). The password is NOT transmitted or stored — analysis is in-process. Heuristic, not a guarantee.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
passwordYesThe password to analyze.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "password": "MyP@ssw0rd!2024"
+  },
+  {
+    "password": "password123"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses critical behavioral traits: the password is processed entirely in-process without transmission or storage, and the analysis is heuristic rather than a guaranteed assessment. These details significantly aid the agent in understanding side effects and limitations, exceeding what annotations alone provide.

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, front-loaded with the primary action and outcome. The first sentence lists all outputs and key constraints; the second addresses privacy and caveats. Every word earns its place, with no redundancy or filler.

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 single-parameter tool with rich annotations, the description is self-sufficient. It covers inputs, outputs, execution context (offline, in-process), and limitations (heuristic). No output schema exists, but the description enumerates what the agent can expect, making the tool's behavior fully predictable.

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 fully describes the single parameter ('The password to analyze.') with 100% coverage. The description adds context about how the password is used (entropy, crack time, weaknesses), but does not add format constraints or example values beyond the schema's examples. This is a simple parameter, and the baseline of 3 applies because the schema carries the structural meaning while the description enriches the purpose.

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 begins with a specific verb and resource: 'Analyze a password's strength offline'. It enumerates concrete outputs (character-set entropy, strength rating, crack time, weaknesses) and clearly distinguishes itself from sibling tools by focusing on offline password analysis. This leaves no ambiguity about what the tool does.

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

Usage Guidelines4/5

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

The description provides clear context for when the tool is appropriate: offline, keyless, privacy-preserving analysis ('password is NOT transmitted or stored'). It implicitly signals alternatives are not needed for this niche, but it does not explicitly state exclusions or name alternative tools. Absence of explicit alternatives is offset by the tool's unique purpose.

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

A3.9/5.0
Disambiguation2/5

Multiple tools are near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share nearly identical routing, with beta explicitly described as currently identical to stable. The six Polymarket-related tools also form a dense cluster with subtle boundaries, and discover_tools/suggest_questions overlap in onboarding purpose.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (check_password, resolve_entity, compare_entities, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are applied consistently. Minor deviations exist (ai_visibility_check, pipeworx_trending), but the overall convention is predictable.

Tool Count2/5

32 tools is well past the 25+ threshold and the set feels bloated: several ask_pipeworx variants and Polymarket scanning tools could be consolidated, and unrelated utilities (check_password, scan_dependency, generate_llms_txt) are mixed into what is otherwise a data-research platform. The broad scope does not justify this many top-level entry points.

Completeness4/5

The main data-research workflow is well covered: routing, grounded verification, deep research, entity resolution/profiles, comparisons, recent changes, discovery, and feedback are all present. Memory and subscription lifecycles are also complete; minor gaps remain such as the lone password tool lacking generation or breach-checking companions, and no direct raw-fetch tool, but these are workable.

Resources