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by LanGuo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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graph_walk

Traverse the session graph from a seed memory ID to find related memories by BFS depth, helping agents surface connected cross-session context.

Instructions

Walk the session graph from a seed memory to find related memories.

Args: seed_memory_id: Full memory ID to start from depth: BFS depth (1 = direct neighbours, 2 = neighbours of neighbours)

Returns: JSON list of related memories

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": {
      "seed_memory_id": "<seed_memory_id>"
    },
    "name": "graph_walk"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNo
seed_memory_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the traversal semantics and that the result is a JSON list of related memories, but says nothing about read-only safety, performance or cost characteristics, or traversal limits — meaningfully incomplete for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The Args/Returns structure is front-loaded and each line carries information; there is no padding. It is slightly verbose in restating the seed parameter already visible in the schema, but overall efficient.

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?

An output schema exists, so the Returns line is somewhat redundant, and the core traversal mechanics are explained. What is missing is sibling differentiation and any behavioral caveats (annotations are absent), leaving the agent without guidance on when this tool beats recall.

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?

Schema description coverage is 0%, so the description must carry the parameter burden, and it does: seed_memory_id is described as the full memory ID to start from, and depth is explained as BFS depth with '1 = direct neighbours, 2 = neighbours of neighbours.' This adds real meaning beyond the bare titles. It omits the default value (2) listed in the schema.

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?

States a specific verb and resource: 'Walk the session graph from a seed memory to find related memories.' An agent can immediately tell this is a graph-traversal tool, but the description never differentiates it from siblings recall and chunk_search, which also surface related content.

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?

Usage is only implied by the phrase 'to find related memories' — there is no explicit statement of when to prefer graph traversal over recall or chunk_search, nor any exclusion criteria. An agent must infer the selection conditions.

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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