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llmslim

llmslim

by Thanatos9404
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recall

Read-onlyIdempotent

Searches earlier conversations for messages that answer a query, returning dated, relevant excerpts within a token budget.

Instructions

Find what earlier conversations said about a question: the stored messages that answer it, under their dates and whole where they fit (the most relevant parts of longer ones), within a token budget.

    Args:
        query: The question, e.g. the user's latest message.
        budget: Most tokens to return. Defaults to the server's budget.
        session: Optional conversation id to search only that conversation.

Input Schema

NameRequiredDescriptionDefault
queryYes
budgetNo
sessionNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, covering the safety profile. The description adds useful behavioral context: results are returned under dates, longer messages are excerpted to relevant parts, and output is bounded by a token budget.

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 purpose is front-loaded in the first sentence, followed by compact Args documentation. The phrasing around dates and excerpts is slightly awkward, but overall it is efficiently sized with little waste.

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?

With an output schema present, the description does not need to explain return values in depth, but it still describes result shape and budgeting. It is nearly complete, though it could more explicitly position the tool against siblings like list_memories.

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 description coverage is 0%, so the description carries the parameter burden and does so well. It explains query as the question, budget as the maximum tokens returned with a server default, and session as an optional conversation id filter.

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 states a specific verb and resource: find stored messages from earlier conversations that answer a question. It distinguishes this retrieval action from siblings like remember, forget, and list_memories, and mentions token budgeting and relevance trimming.

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?

It gives clear context for using the tool, including that the query is typically the user's latest message and that session can limit search to one conversation. It does not explicitly name alternatives or when not to use this tool versus list_memories.

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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