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spatial-memory-mcp

spatial-memory-mcp

by arman-tech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

nearby

Retrieve memories similar to a given memory ID to explore related knowledge within your spatial memory graph.

Instructions

Find memories similar to a specific memory.

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": {
      "memory_id": "<memory_id>"
    },
    "name": "nearby"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of neighbors (default: 5)
projectNoProject scope for this operation. Omit to auto-detect from environment. Use "*" to search across all projects.
_agent_idNoOptional agent identifier for request tracing and per-agent rate limiting.
memory_idYesThe ID of the reference memory
namespaceNoFilter neighbors to specific namespace

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.11.4

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Find memories similar to a specific memory,' which implies a read operation but does not disclose result ordering, pagination behavior, what similarity is based on, or whether any side effects occur. This is a significant gap for a tool with no annotation safety profile.

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 a single, front-loaded sentence with no wasted words. It states the action and the target resource directly, which is appropriate for a concise tool description.

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?

For a tool with five parameters, no output schema, no annotations, and many retrieval-related siblings, the description is incomplete. It explains only the core purpose and omits usage guidance, behavioral details, and any indication of what the response will look like, leaving an agent under-equipped to invoke it correctly.

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 description coverage is 100%, so the input schema already documents all five parameters. The description adds no additional meaning beyond the schema, such as how limit interacts with similarity ranking or how project/namespace scoping affects results. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 states a specific verb ('Find') and resource ('memories similar to a specific memory'), making the core operation clear. It does not explicitly contrast with sibling tools like recall or hybrid_recall, but the phrase 'similar to a specific memory' distinguishes it from generic retrieval.

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 implies a usage context: use it when you need semantic neighbors of a known memory. However, it provides no explicit guidance on when to prefer this over recall, hybrid_recall, or discover_connections, and no exclusions or prerequisite conditions are stated.

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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