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mcp-context-dedup

mcp-context-dedup

by prasadabhishek
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compress_mcp_output

Reduce LLM prompt token usage by compressing verbose MCP output: deduplicate repeated log lines and summarize JSON arrays.

Instructions

Compress verbose tool outputs and deduplicate log lines to save LLM prompt tokens.

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": {
      "raw_text": "<raw_text>"
    },
    "name": "compress_mcp_output"
  }
}

Input Schema

NameRequiredDescriptionDefault
raw_textYesRaw verbose stdout/stderr string

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the actions (compress, deduplicate) but does not disclose the output format, whether the compression is lossy, or how it handles edge cases like empty input. Without an output schema or annotations, critical behavioral context is missing.

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?

The description is a single, efficient sentence that is front-loaded with the primary action and includes the motivating purpose. Every word contributes value; no redundancy.

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 simplicity (1 parameter, no output schema, no annotations), the description conveys the core functionality but lacks details about the return value and potential lossiness. It is minimally viable but has clear gaps for a tool that transforms user input.

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% for the single parameter 'raw_text', which is adequately described as 'Raw verbose stdout/stderr string'. The description adds little beyond the schema, but since the schema already covers semantics, the baseline score of 3 is appropriate.

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 uses a specific verb ('Compress') and resource ('verbose tool outputs'), and adds a second distinct action ('deduplicate log lines') with a clear goal ('to save LLM prompt tokens'). This leaves no ambiguity about what the tool does, even without sibling context.

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 clearly implies when to use the tool: when dealing with verbose tool outputs that need token savings. It does not mention explicit exclusions or alternatives, but with no siblings present, the context is sufficient for a 4.

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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