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Murmur hash hex length, input discarded

by io.github.sadri-dridi
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murmur-len

Murmur hash hex length, input 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

D1.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose one notable trait—'input discarded'—but omits whether the tool performs network access, whether hashing is deterministic, what output is returned, and how multiple parameters are handled.

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 short and front-loaded, but it is under-specified rather than genuinely concise. A one-line fragment cannot adequately serve a 10-parameter tool with no output schema.

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 the high parameter count and absence of an output schema, the description is grossly incomplete. It does not state the return type, the relationship between the ten parameters and the hash input, or what 'hex length' refers to in practice.

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?

Schema description coverage is 100%, so the parameters themselves are already documented, and the baseline is 3. The description adds no per-parameter semantics beyond saying input is discarded, leaving the agent to guess whether any one parameter or a combination is the hashed input.

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

Purpose2/5

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

The description is essentially a noun-phrase restatement of the tool name: 'Murmur hash hex length' adds only slightly more specificity than 'murmur-len'. It never states a verb such as 'returns', 'computes', or 'checks', so the actual operation and result semantics remain ambiguous.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool instead of a sibling such as search-query-len, normalize-url, or validate-json. The phrase 'input discarded' does not explain selection criteria, expected input choice among 10 optional parameters, or exclusions.

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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