Skip to main content
Glama
Sign In
engram-global

engram-global

by ricoaiproject-cmd
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

consolidation_candidates

Surface clusters of similar older episode memories at session end, identifying those ready to compress into knowledge or project memories.

Instructions

Return clusters of episode memories that are candidates for consolidation.

Call this before ending a session (at session end), to surface clusters of similar older episodes that are candidates for compressing into knowledge or project memories. The LLM generates the summary, then calls mark_consolidated to complete the consolidation.

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": "consolidation_candidates"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden. It clarifies that the tool only returns candidates and does not perform the actual consolidation, and it names the follow-up action (mark_consolidated), which adds valuable behavioral context beyond a simple 'return clusters' statement.

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 three concise sentences: purpose, timing, and next-step workflow. Every sentence earns its place with no redundancy or fluff.

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?

For a zero-param tool with no output schema, the description adequately covers purpose, usage, and subsequent actions. It could describe the exact return format in more detail, but the essential context is present and the workflow is clear.

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

Parameters4/5

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

The tool has zero parameters, so the schema is trivially complete. The description adds no parameter semantics because none are needed; the baseline for zero-parameter tools is 4.

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 returns clusters of episode memories for consolidation, using a specific verb and resource. It distinguishes itself from sibling tools like mark_consolidated and skill_candidates by focusing on candidate generation for consolidation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to call before ending a session and outlines the workflow with mark_consolidated. This provides clear when-to-use context and distinguishes from alternatives by describing the intended place in the consolidation process.

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