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verifiable-thinking-mcp

verifiable-thinking-mcp

by CoderDayton
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

compress

Compress text while preserving query-relevant content using CPC-style sentence-level compression, maintaining coreference chains and causal relationships for accurate information retention.

Instructions

CPC-style sentence-level compression. TF-IDF + NCD scoring, coreference/causal chains, filler removal. 10× faster than token-level LLM compression. Keeps query-relevant sentences.

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": {
      "context": "<context>",
      "query": "<query>",
      "target_ratio": "<target_ratio>",
      "boost_reasoning": "<boost_reasoning>",
      "use_ncd": "<use_ncd>",
      "enforce_coref": "<enforce_coref>",
      "enforce_causal": "<enforce_causal>",
      "remove_fillers": "<remove_fillers>"
    },
    "name": "compress"
  }
}

Input Schema

NameRequiredDescriptionDefault
contextYesText to compress
queryYesFocus query
target_ratioYesTarget ratio (0.5=50%)
max_tokensNoMax tokens (alternative to ratio)
boost_reasoningYesBoost reasoning keywords
use_ncdYesUse NCD (gzip) scoring
enforce_corefYesKeep pronoun antecedents
enforce_causalYesKeep causal premises
remove_fillersYesRemove filler phrases

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context about the compression method (TF-IDF + NCD scoring) and performance (10× faster), but lacks details on permissions, rate limits, error handling, or output format. For a tool with 9 parameters and no annotations, this is a moderate gap, scoring at the baseline of adequate.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with key information in the first part. It uses technical terms efficiently (e.g., 'CPC-style,' 'TF-IDF + NCD scoring') without unnecessary elaboration. However, it could be slightly more structured (e.g., separating performance claims from functional details), keeping it from a perfect 5.

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

Completeness3/5

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

Given the complexity (9 parameters, no annotations, no output schema), the description is moderately complete. It covers the compression approach and performance but lacks details on output format, error cases, or integration context. For a tool with rich input schema but no other structured data, this is adequate but has clear gaps.

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 schema already documents all parameters thoroughly. The description doesn't add any specific parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'target_ratio' interacts with 'max_tokens' or detail the algorithms). With high schema coverage, the baseline is 3, and the description doesn't compensate further.

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 clearly states the tool performs 'CPC-style sentence-level compression' with specific techniques mentioned (TF-IDF + NCD scoring, coreference/causal chains, filler removal). It distinguishes the tool by mentioning it's '10× faster than token-level LLM compression' and 'Keeps query-relevant sentences,' giving a clear sense of what it does. However, it doesn't explicitly differentiate from sibling tools (which appear unrelated to compression), so it doesn't reach a perfect 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions it's faster than 'token-level LLM compression,' which implies a comparison, but doesn't name specific alternatives or provide explicit when/when-not scenarios. With no usage context provided, this falls to a minimal score.

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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