Skip to main content
Glama
Sign In
token-optimization-mcp

token-optimization-mcp

by DCx7C5
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

cache_lookup

Retrieve cached LLM responses by prompt text or cache key to avoid redundant processing and save tokens.

Instructions

Look up a cached result by prompt text or pre-computed cache key. Returns {hit: true, result, tokens_saved} or {hit: false}.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "cache_lookup"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptNo
cache_keyNo
client_idNodefault

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses the read-only nature implicitly (lookup returns data or miss) and explains the output format. However, it could explicitly state that no data is modified.

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?

Two sentences with no filler. The verb 'Look up' is front-loaded. Every word serves a purpose.

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?

Given no annotations and presence of an output schema, the description is mostly complete. It could mention that at least one of prompt or cache_key should be provided, but the general behavior is well-covered.

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 0%, so the description must compensate. It explains the two main parameters (prompt and cache_key) and client_id, but does not clarify precedence or exclusivity when both are provided. This ambiguity reduces clarity.

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 clearly states the tool's purpose: 'Look up a cached result by prompt text or pre-computed cache key.' It specifies the return structure and distinguishes it from sibling tools like cache_store and cache_invalidate.

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 implies usage for checking cache hits, but does not explicitly state when to use it vs alternatives (e.g., cache_store for misses). The context is clear enough for an AI agent.

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

Deploy Server

Other Tools