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raggy_recall

Find relevant past memories using semantic search when initial context yields nothing or for a follow-up lookup. Filter by tags or source app to pinpoint answers.

Instructions

Targeted semantic search over memories. Only use AFTER raggy_context at session start has returned nothing relevant, or for a specific follow-up lookup mid-session. Do NOT call twice per question. For time-based queries use raggy_timeline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter to sources with any of these tags
limitNoMax results (default: 5)
queryYesWhat do you need to remember?
source_appNoFilter by source app (e.g., "claude-code", "hermes", "openclaw")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. 'Semantic search' implies a non-mutating retrieval, and the call-count guardrail is useful, but the description does not say what a result contains, whether results are ranked, or whether any state changes occur. This is adequate but not fully transparent.

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?

Four short sentences, each carrying distinct information: function, trigger conditions, call-frequency limit, and sibling routing. The most important scoping information is front-loaded.

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 low-complexity search tool with a fully described schema, the description is nearly complete: it covers purpose, when to use it, when not to use it, and an alternative. The only gap is the absence of any note about the return format, and there is no output schema to fill that gap.

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 description coverage is 100%, so the baseline applies and the description does not need to re-explain parameters. The prose adds no parameter-level detail beyond the schema, but none is required because every parameter already has a clear description in the schema.

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 first phrase, 'Targeted semantic search over memories,' names a specific verb and resource and conveys the tool's exact function. The description then explicitly distinguishes it from raggy_context and raggy_timeline, so an agent can select it correctly among siblings.

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 gives explicit conditions: use it only after raggy_context returns nothing relevant, or for a mid-session follow-up lookup. It also provides a hard exclusion ('Do NOT call twice per question') and routes time-based queries to raggy_timeline, leaving no ambiguity about when this tool applies.

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