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memory-mcp

memory-mcp

by jmars
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

add_observations

Add new observations to existing knowledge graph entities by providing an entity name and content list, enabling incremental knowledge updates.

Instructions

Add new observations to existing entities in the knowledge graph.

Each item must have: entityName (str), contents (list[str]).

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": {
      "observations": "<observations>"
    },
    "name": "add_observations"
  }
}

Input Schema

NameRequiredDescriptionDefault
observationsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the action but does not disclose side effects (e.g., appending vs overwriting), error conditions (e.g., missing entity), permissions, or return behavior. This is sparse for a mutation tool.

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 two sentences, front-loaded with the primary action and immediately followed by required item structure. Every word earns its place, with no filler or redundancy.

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

Completeness3/5

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

The tool is simple with a single parameter and an output schema present, so the description need not explain return values. However, it lacks details on entity existence prerequisites, failure modes, and mutation semantics, making it adequate but not complete given the lack of annotations.

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 input schema has 0% coverage because the inner objects are not defined. The description adds essential meaning by specifying each item must have 'entityName' (str) and 'contents' (list[str]), significantly compensating for the schema gap.

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 the specific verb 'Add' and resource 'observations to existing entities in the knowledge graph', clearly distinguishing it from siblings like create_entities (adds entities) and create_relations (adds relations).

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?

The description implies usage for adding observations to existing entities, and the phrase 'existing entities' hints at a prerequisite. However, it does not explicitly mention when not to use it or name alternatives, leaving usage guidance implied rather than explicit.

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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