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AiAgentKarl

shared-context-cache-mcp-server

cache_store

Store computed results in a shared cache so other AI agents can reuse them, reducing redundant computation and token usage.

Instructions

Store a computed result in the shared cache so other agents can reuse it.

After computing an expensive result (web search, analysis, API call), store it here. Other agents will find it via cache_lookup or cache_search.

The entry starts with trust_score=1 (you as the first confirmer). Other agents can use confirm_entry to increase the trust score.

Args: key: Unique cache key (e.g. 'weather:berlin:2026-03-28', 'summary:arxiv:2501.00001') value: The result to cache (JSON string, text, or any serializable content) ttl_seconds: Time-to-live in seconds (default: 86400 = 24h, max: 604800 = 7 days) tags: Comma-separated tags for discovery (e.g. 'weather,berlin,temperature') agent_id: Your agent identifier for attribution (e.g. 'weather-agent-v2')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYes
valueYes
ttl_secondsNo
tagsNo
agent_idNomcp-agent

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It explains trust_score initialization, ttl behavior, and that other agents can increase trust. It lacks disclosure on any potential side effects, but caching is generally benign.

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 well-structured with a purpose statement, usage guidance, and a labeled Args section. It is slightly verbose but every sentence adds value.

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

Completeness5/5

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

Given the tool's complexity (5 parameters, output schema exists), the description covers purpose, when to use, parameter details, and behavioral notes. It is self-contained and leaves no major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It provides detailed explanations for all 5 parameters including examples for key, accepted formats for value, default and max for ttl_seconds, comma-separated for tags, and attribution for agent_id.

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: 'Store a computed result in the shared cache so other agents can reuse it.' It distinguishes from sibling tools like cache_lookup and cache_search by specifying the action of storing.

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?

Provides explicit when to use: 'After computing an expensive result...' and mentions related tools (confirm_entry). However, it does not explicitly state when not to use.

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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