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Memorable MCP Server

Memorable MCP Server

by leouofa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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memorable_recall_context

Recall pinned memories and semantically matched results together in one context payload so agents can retrieve persistent preferences and relevant past context for a query.

Instructions

Recalls standing pinned memories combined with relevant semantic search matches in one unified context payload

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": "memorable_recall_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNoSearch mode: 'auto' (hybrid vector fallback to keyword), 'semantic' (pure pgvector), or 'keyword'
limitNoMaximum search results to return (default 10)
queryNoOptional topic or query to semantically match against memories
include_pinnedNoWhether to include standing pinned memories in the recalled context (default true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It discloses the one genuinely important trait — that results are a merge of pinned entries and semantic matches — but says nothing about ordering, deduplication between the two sources, payload size limits, or what happens when query is omitted.

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?

A single front-loaded sentence with no filler; the composite nature of the result is stated first, which is the most decision-relevant fact.

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?

With four optional parameters and no output schema, the agent still doesn't know the shape of the 'unified context payload' — whether pinned and semantic results are labeled separately, how they are ordered, or how large the result can get. The description covers what is combined but not what comes back.

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 coverage is 100% with per-parameter descriptions for mode, limit, query, and include_pinned, so the schema does the heavy lifting. The description only loosely maps its 'pinned' and 'semantic search' language onto include_pinned and mode without adding format or default detail beyond 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?

Names a specific verb ('Recalls') and a precise composite resource: standing pinned memories merged with semantic search matches into one payload. That composite behavior distinguishes it in spirit from memorable_search or memorable_list_memories, though neither sibling is named explicitly.

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?

There is no statement of when to choose this over memorable_search, memorable_list_memories, or memorable_get_memory. The phrase 'in one unified context payload' hints at a context-bootstrap use case, but the agent must infer that rather than being told.

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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