Hebbrix MCP Server

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hebbrix_search

Read-onlyIdempotent

Retrieve semantically relevant memories before answering questions that rely on prior context, decisions, or user preferences.

Instructions

Semantic search over memories. Always call this BEFORE answering questions that depend on prior context, decisions, or user preferences.

Zero-relevance padding rows are always dropped. If the fast API returns only uncalibrated nearest-neighbour candidates with no lexical anchor, Hebbrix automatically verifies them with calibrated retrieval and suppresses noise. Raise min_score (0.0-1.0) to request an explicit absolute relevance floor.

Returns {"query", "count", "results": [{"id","content","score"}]}.

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": {
      "query": "<query>"
    },
    "name": "hebbrix_search"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`min_score`	No
`collection_id`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.3.20Aug 2, 2026
- addedInput schema / properties / min_score
```
Added value: +{
+  "default": 0,
+  "title": "Min Score",
+  "type": "number"
+}
```
First observedv0.3.3Jul 9, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Even though annotations already declare readOnlyHint=true and destructiveHint=false, the description adds significant behavioral detail: zero-relevance padding rows are always dropped, automatic verification with calibrated retrieval is disclosed, and the meaning of the min_score parameter is explained ('absolute relevance floor'). This goes well beyond what annotations provide.

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?

Three well-structured paragraphs flow from top-level purpose to behavior to parameters, then output format. Every sentence adds value with the most critical usage directive positioned first after the summary. Zero wasted words.

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

Completeness5/5

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

The description fully compensates for the 0% schema coverage by documenting the key parameter (min_score), return format, edge behavior (zero-relevance row dropping, noise suppression), and use case. With annotations confirming read-only, non-destructive behavior and an output schema present, the description fills all remaining gaps.

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 compensate. It does explain min_score ('Raise min_score (0.0-1.0) to request an explicit absolute relevance floor'), but doesn't elaborate on query, limit, or collection_id. The return format is documented, and the 0% coverage means the description carries the full burden, which it mostly does for the most important parameter.

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 specific verb+resource ('Semantic search over memories') and clearly differentiates from siblings by stating when to use it: BEFORE answering questions depending on prior context, decisions, or preferences. This strongly distinguishes it from sibling tools like hebbrix_history, hebbrix_get, and hebbrix_list.

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

Usage Guidelines5/5

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

The description explicitly says 'Always call this BEFORE answering questions that depend on prior context, decisions, or user preferences.' This is a clear when-to-use directive. It also contrasts with alternatives by noting this is the primary semantic search entry point among many siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.