Memro

search_temporal

Search memories with temporal reasoning. Finds relevant memories considering both when the conversation happened and when events actually occurred. Perfect for multi-session synthesis and temporal queries.

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": "search_temporal"
  }
}

Input Schema

Name	Required	Description
`limit`	No
`query`	Yes	What to search for (e.g., 'things I did last summer')
`after_date`	No	ISO 8601 date - only return memories from this date forward
`atomic_only`	No	Only return atomic facts (true) vs raw chunks (false)
`before_date`	No	ISO 8601 date - only return memories before this date
`temporal_weight`	No	How much to prioritize recency (0=semantic only, 1=temporal only)

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 15, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are supplied, so the description must carry the full behavioral burden. It does add real value by disclosing the dual-time model (conversation time vs. event time), which the schema does not express, but it omits whether the tool is read-only, how results are ranked or returned, and any pagination or permission constraints.

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?

Three tight sentences, front-loaded with the verb and resource; nothing is redundant. The closing 'Perfect for' clause is mildly promotional but still carries routing information.

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?

For a six-parameter tool with no annotations and no output schema, the description covers the core concept but leaves behavioral details (result shape, ranking mechanics, read-only nature) unstated. Adequate to call correctly, but not complete enough to predict 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 83%, so the schema already documents nearly every parameter including temporal_weight's 0-1 scale and the date filters. The description adds only conceptual framing ('considering both when the conversation happened and when events actually occurred') without explaining how after_date/before_date interact with that dual-time model.

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?

States a specific verb and resource ('Search memories') plus a differentiating qualifier ('with temporal reasoning'), and clarifies that it considers both conversation time and event occurrence time. It does not name any sibling such as recall or get_timeline, so an agent must infer differentiation from names alone.

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?

'Perfect for multi-session synthesis and temporal queries' implies when to reach for it, but there are no explicit exclusions or named alternatives (e.g., plain semantic recall vs. timeline browsing). The agent gets a use-case signal but no decision rule.

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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