Skip to main content
Glama

whitespace token count, text discarded

by io.github.sadri-dridi
OverviewDiscussionsAdmin

token-n

whitespace token count, text discarded

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check
websiteNoPublic https website (homepage domain) to check for agent readiness: robots.txt, llms.txt, sitemap.xml, extractable HTML. Bounded, identified, robots-respecting.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / website
      Added value: +{
+  "description": "Public https website (homepage domain) to check for agent readiness: robots.txt, llms.txt, sitemap.xml, extractable HTML. Bounded, identified, robots-respecting.",
+  "type": "string"
+}
  2. First observed

TDQS

C2.3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing side effects and behavior. 'Text discarded' is a useful privacy-relevant disclosure, and counting implies a read-only operation, but the description does not state what is returned, whether multiple parameters are accepted simultaneously, or any constraints on input handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely terse and front-loaded, which is structurally efficient. However, it sacrifices necessary guidance for a tool with 10 optional parameters and no output schema, so this is under-specification rather than effective conciseness.

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

Completeness1/5

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

Given 10 optional parameters, no output schema, and no annotations, the description is severely incomplete. An agent has no way to know which parameter to pass, what the returned value looks like, or how token counting behaves across the diverse input types (URLs, JSON, timezones, file paths, etc.).

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 input schema has 100% description coverage across all 10 parameters, so the schema already explains each field. The description adds a global hint about whitespace tokenization and discarding, but it does not connect the behavior to any specific parameter or explain which field should be used.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description communicates that the tool counts whitespace-delimited tokens and discards the input text, so a basic purpose is inferable. However, it never states what input is being counted—there is no explicit resource or parameter linkage, and it does not differentiate from sibling counting tools like search-query-len or memory-key-count.

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 given about when to use this tool versus alternatives. The sibling list contains several vaguely similar tools (e.g., search-query-len, hn-front-count, memory-key-count), and the description offers no conditions, exclusions, or examples to steer an agent's choice.

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

All whitespace token count, text discarded toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources