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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_self_assess

Read-onlyIdempotent

Assess competency in given domains by showing skill level, confidence, and blind spots. Optionally generates a full self-model report.

Instructions

Self-assessment: how competent am I in given domains? Shows level, confidence, blind spots.

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": "memory_self_assess"
  }
}

Input Schema

NameRequiredDescriptionDefault
conceptsNoDomains/concepts to assess competency for
full_reportNoReturn full self-model report

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds that it shows level, confidence, and blind spots, which is useful but does not detail whether it only reads existing state or computes new assessments. No contradiction with annotations.

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 short and front-loaded. The core purpose appears in the first sentence, and the additional output details are concise. No redundant or irrelevant wording.

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 simple read-only introspection tool with no output schema and annotations covering safety, the description is essentially complete. It explains what the tool does and what it returns at a high level, though it could mention the absence of side effects more explicitly.

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?

The schema descriptions cover both concepts and full_report, so coverage is complete. The tool description adds only minimal meaning to 'concepts' via 'given domains' and nothing extra about full_report. This meets the baseline for schema-covered parameters but does not enrich them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the action (self-assessment) and the target (competency in given domains), plus the kind of output (level, confidence, blind spots). It does not explicitly name a resource like 'self model', but the intent is unambiguous enough.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives such as self_reflect, self_insight, or memory_recall. It lacks context for selecting this self-assessment over related introspection tools.

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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