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Mirsad

Mirsad

by amkkoussay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

recall

Retrieve relevant facts, decisions, and raw episodes from shared AI memory using hybrid search to answer queries with contextual evidence.

Instructions

Hybrid recall across facts, decisions and raw episodes.

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": "recall"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
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 and does not meet it. It never states that this is a read-only operation, how results are ranked or merged across the three sources ('hybrid'), whether a limit or pagination exists, or what the response shape looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with no filler, which is structurally clean. However, its brevity reflects under-specification rather than efficiency: there is no front-loaded explanation of what a recall call returns or when to prefer it.

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 memory-retrieval tool with zero annotation coverage, no output schema, and an undocumented parameter, the description is too thin. An agent cannot predict result format, ranking behavior, or scope limits before calling it, and there is no output schema to fill the gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for the single parameter, so the description must explain what 'query' accepts. It says nothing about whether the query is natural language, keywords, or structured, nor how the hybrid matching treats it, leaving the only input effectively undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a retrieval action ('recall') and the resources it spans (facts, decisions, raw episodes), so the general domain is clear. But 'hybrid recall' is unexplained jargon, and nothing distinguishes this general lookup from siblings such as current_fact, timeline, or why, which all appear to retrieve memory content.

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 gives no when-to-use guidance, no exclusions, and no pointer to any alternative among the ten siblings. An agent has to guess whether this is the right entry point or whether current_fact / why should be preferred for a given question.

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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