WritersLogic verifiable AI agents

compressibility-rank

Rank the most compressible (cliched) phrases in a chapter — Finds the phrases in a passage that compress most against a reference corpus of generic prose, so a writer can target the low-entropy spans instead of guessing. For each sentence (default) or sliding n-gram, computes the marginal compressed-byte contribution per character with zlib level 9 and a built-in reference corpus of common narrative clichés; the lowest-scoring spans are the most predictabl

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "compressibility-rank"
  }
}

Input Schema

Name	Required	Description
`mode`	No	sentence (default) scores at sentence boundaries; ngram scores every sliding window of ngramSize words.
`text`	Yes	The chapter or passage to score.
`topN`	No	How many lowest-score spans to return (default 10).
`minChars`	No	Minimum characters per candidate span (default 18).
`ngramSize`	No	Words per window when mode is ngram (default 5).
`reference`	No	Optional reference corpus to prepend before compression. When absent, the built-in 50 KB generic-prose corpus is used unless useBuiltInReference is false.
`useBuiltInReference`	No	Pass false to score with no reference corpus (local redundancy only). Default true.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 15, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations available, the description carries the full behavioral burden and does substantial work: it discloses the scoring mechanism (marginal compressed-byte contribution per character), the compression method and level (zlib level 9), the two scoring granularities, and the existence of a built-in reference corpus of narrative clichés. It does not mention runtime cost for the 200,000-character input limit or how ties/ordering are handled, which keeps it short of a 5.

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

Conciseness3/5

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

The definition is front-loaded with the verb and resource, which is good, but the second half is a long semicolon-chained sentence that mixes purpose, mechanism, and implementation detail, and it terminates mid-word. The truncation is a structural defect that costs a point despite the otherwise efficient opening.

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

Completeness4/5

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

For a read-only analytical tool with seven parameters fully described in the schema and no output schema, the description supplies enough to call it correctly: what is computed, at what granularity, with what corpus, and that the lowest-scoring spans are returned. The missing pieces are output field shape beyond 'lowest-scoring spans' and any performance caveat for large inputs, plus the truncated closing clause.

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 coverage is 100%, so every parameter is already documented, and the description largely echoes that surface (sentence vs sliding n-gram, built-in reference). It adds one genuinely useful framing detail — that the built-in corpus is specifically 'common narrative clichés' — but does not clarify topN/minChars interaction or what a negative or zero marginal contribution means. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose4/5

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

The description names a specific verb and resource: 'Rank the most compressible (cliched) phrases in a chapter,' and then restates the operation as finding phrases that 'compress most against a reference corpus of generic prose.' That is far more concrete than the tool name alone, though the final clause is cut off mid-word ('most predictabl'), leaving the purpose statement visibly truncated rather than fully closed.

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?

Use is framed through intent — 'so a writer can target the low-entropy spans instead of guessing' — which implies the authorial-editing scenario. However, there is no explicit when-to-use condition, no exclusion of cases (e.g. very short passages, non-prose text), and no named alternative among the many sibling tools, so the agent must infer the trigger.

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

All WritersLogic verifiable AI agents tools Inspect

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

Need Help?