bluecolumn-mcp

recall

Query persistent memory with natural language to retrieve past information, get context from previous sessions, and receive AI-synthesized answers with source citations.

Instructions

Query BlueColumn memory using natural language. Returns an AI-synthesized answer with source citations. Use when the agent needs to retrieve past information, answer questions about stored content, or get context from previous sessions.

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": {
      "q": "<q>"
    },
    "name": "recall"
  }
}

Input Schema

Name	Required	Description	Default
`q`	Yes	Natural language query to search memory

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0Aug 4, 2026

TDQS

A4/5.0

Behavior4/5

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

No annotations are provided, so the description carries the transparency burden. It discloses a key behavioral trait: 'Returns an AI-synthesized answer with source citations.' It implies a read-only query operation, which is sufficient for a retrieval tool, but does not discuss auth, rate limits, or potential side effects.

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?

Two sentences, front-loaded with the core function and output. Each sentence earns its place: the first defines the operation and result, the second lists use cases. No fluff or redundancy.

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

Completeness4/5

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

For a simple one-parameter tool with no output schema, the description adequately covers purpose, usage, and return type ('AI-synthesized answer with source citations'). It falls short of detailing the citation structure or any failure modes, but is sufficient for this level of complexity.

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%, so the baseline is 3. The description adds no additional meaning beyond the schema's 'Natural language query to search memory.' No examples or format details are provided.

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 action ('Query BlueColumn memory') and notes the AI-synthesized answer with citations, making the tool's purpose clear. It implicitly differentiates from siblings like 'remember' (likely write) and 'audio_recall' (audio-specific), but does not explicitly name alternatives or contrast them.

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

Usage Guidelines4/5

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

Explicitly states when to use: 'when the agent needs to retrieve past information, answer questions about stored content, or get context from previous sessions.' This is clear context, though it does not mention exclusions or alternative tools.

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

Deploy Server