Cachly — AI Cognitive Brain

recall_at

Retrieve the history of any topic up to a specific date to see how knowledge evolved. Understand why old code decisions were made by viewing entries stored before that date.

Instructions

Brain Archaeology — see what a lesson looked like at a specific point in time. "What did we know about deployments 3 months ago?" Returns the history of a topic filtered to entries before the given date. Shows how the lesson evolved: failure → partial → success. Also useful to understand WHY old code decisions were made.

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": {
      "instance_id": "<instance_id>",
      "topic": "<topic>",
      "date": "<date>"
    },
    "name": "recall_at"
  }
}

Input Schema

Name	Required	Description
`date`	Yes	ISO date string (e.g. "2026-01-15") — returns entries stored BEFORE this date
`topic`	Yes	Topic slug to look up, e.g. "deploy:api"
`instance_id`	Yes	UUID of the cache instance

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.6.0May 5, 2026

TDQS

A4.6/5.0

Behavior4/5

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

In the absence of annotations, the description provides good behavioral context: it returns filtered history, shows lesson evolution (failure → partial → success), and explains the 'before' date logic. It does not disclose authentication needs or rate limits, but it covers the core behavior well.

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 extremely concise: a tagline, an example question, and a brief explanation of output and utility. Every sentence adds value without redundancy.

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 has three parameters, no output schema, and no annotations, the description is complete enough. It explains what the tool returns (filtered history with evolution stages), when to use it, and parameter formats. An agent can reliably select and invoke this tool.

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 100%, and the description adds meaning beyond schema by explaining topic slug format ('deploy:api'), date format (ISO string), and the 'BEFORE this date' semantics. It also gives an example query, enriching understanding of each parameter.

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: returning the history of a topic before a specified date, showing evolution. It uses a memorable metaphor 'Brain Archaeology' and provides an example question. It distinguishes from siblings like 'global_recall' and 'recall_best_solution' by focusing on temporal point-in-time queries.

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 gives concrete usage scenarios (e.g., understanding old code decisions) but does not explicitly state when not to use it or mention alternatives. It implies context for historical analysis, which is sufficient for an agent to decide.

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

Deploy Server