Skip to main content
Glama
danielsimonjr

Enhanced Knowledge Graph Memory Server

format_with_salience_budget

Allocate token budget proportionally across memories based on their salience scores to optimize LLM prompt context usage.

Instructions

Format memories for LLM prompt consumption with proportional token allocation based on salience scores

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headerNoOptional header text to prepend
separatorNoOptional separator between memories (default: newline)
entityNamesYesNames of entities (memories) to format
salienceScoresYesMap of entityName → salience score (0–1) for proportional allocation
totalTokenBudgetYesMaximum total token budget for the formatted output
Behavior2/5

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

No annotations are provided, so description carries full burden. It discloses the core formatting behavior but does not mention whether it modifies state, error conditions, or output format. This is insufficient for full transparency.

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?

Single sentence, no redundancy, directly communicates purpose.

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

Completeness2/5

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

Without an output schema, the description fails to explain the return value (type, structure). It also omits potential errors, token budget edge cases, and prerequisites. For a tool with 5 parameters, this is incomplete.

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 100% with clear parameter descriptions. The tool description adds no additional semantic meaning beyond what the schema provides. Per rule, baseline is 3.

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 formats memories for LLM prompt consumption using salience-based token allocation. It distinguishes from sibling 'format_project_context_for_llm' by specifying it is for memories and uses salience scores.

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

Usage Guidelines3/5

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

No explicit guidance on when to use vs alternatives. The purpose implies usage for formatting memories with a budget, but no when-not or alternatives are mentioned.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/danielsimonjr/memory-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server