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spatial-memory-mcp

spatial-memory-mcp

by arman-tech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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corpus_bridges

Find semantically similar memories across different namespaces to uncover hidden knowledge links or duplicate entries in the spatial memory corpus.

Instructions

Find cross-namespace bridges in the memory corpus. Discovers memories in different namespaces that are semantically similar -- potential knowledge links or duplicates.

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": "corpus_bridges"
  }
}

Input Schema

NameRequiredDescriptionDefault
_agent_idNoOptional agent identifier for request tracing and per-agent rate limiting.
max_bridgesNoMaximum bridges to return
min_similarityNoMinimum similarity for a bridge
namespace_filterNoOnly consider these namespaces

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.11.4

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden. It communicates a discovery-style behavior ('Find', 'Discovers') and defines what a bridge is, but it does not explicitly state that the operation is read-only, describe side effects, or mention any behavioral caveats beyond what the schema already encodes.

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?

Two short sentences front-load the main action and then explain the bridge concept clearly. There is minor redundancy between 'cross-namespace bridges' and 'memories in different namespaces,' which keeps it from a perfect score.

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 definition is adequate for a simple read-only discovery tool: purpose is clear, parameters are fully described by the schema, and the return concept ('bridges') is explained. It lacks richer context only in that it gives no output-structure details and does not relate this tool to sibling discovery tools.

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 schema fully documents max_bridges, min_similarity, namespace_filter, and _agent_id. The description adds no parameter-level detail, which is acceptable under the baseline for full schema coverage.

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 uses a specific verb ('Find') and resource ('cross-namespace bridges in the memory corpus'), then clarifies the semantics: semantically similar memories across namespaces, flagged as potential links or duplicates. This scope distinguishes it from sibling tools like nearby or recall.

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 core use case is inferable: use this when you want cross-namespace semantic links or duplicates. However, there is no explicit guidance about when to prefer this over sibling tools such as discover_connections or recall, and no when-not-to-use 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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