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AgentPlaybooks

by ai.agentplaybooks

Search memory

search_memory
Read-onlyIdempotent

Search memories by text, tags, tier, or type. Returns summaries for large memories. Use tags for categorical search; use tier to focus on active vs archived data; use memory_type to find task graphs. Read-only aside from returning matches; it does not write entries. Use read_memory for one key, get_memory_context for a compact tiered view, and get_memory_tree for parent-child task graphs. Pass playbook_id as the UUID or GUID of the playbook this call should target.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by tags (any match)
tierNoFilter by memory tier
searchNoSearch in keys, descriptions, and summaries
statusNoFilter by task status (hierarchical only)
memory_typeNoFilter by memory type
playbook_idYesUUID or GUID of the target playbook
include_childrenNoInclude child memories in results

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations by stating 'Returns summaries for large memories' and confirming 'Read-only aside from returning matches; it does not write entries.' No contradiction; the description complements the annotation safety profile.

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?

The description is three sentences that are front-loaded with the main action and purpose. Every sentence adds value: the first states the function, the second advises on filter semantics, and the third names alternatives and the required parameter. No fluff or redundancy.

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

Completeness5/5

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

For a tool with 7 parameters, high schema coverage, and no output schema, the description is exceptionally complete. It covers what the tool does, when to use it, how to differentiate filters, how it differs from siblings, the required parameter format, and a note on return behavior (summaries). All essential contextual information is present.

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

Parameters4/5

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

Schema description coverage is 100%, but the description enriches parameter meaning: it explains how tags, tier, and memory_type should be used (categorical, archiving, task graphs). It also clarifies playbook_id as 'UUID or GUID', which is not obvious from the schema. This goes beyond the bare schema definitions.

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 description clearly states the tool's purpose: 'Search memories by text, tags, tier, or type' with actionable details. It explicitly distinguishes itself from siblings like read_memory, get_memory_context, and get_memory_tree, making it unambiguous which tool to select for memory search.

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?

Provides explicit when-to-use guidance: 'Use tags for categorical search; use tier to focus on active vs archived data; use memory_type to find task graphs.' It also names alternative tools for specific use cases and specifies the playbook_id parameter format, giving clear direction on invocation.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool targets a distinct entity and action (e.g., delete_memory vs delete_skill vs delete_run), and even similar operations like read_memory vs search_memory vs get_memory_context have clearly differentiated purposes. The descriptions are detailed and explicitly cross-reference other tools to avoid confusion.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (create_*, list_*, update_*, delete_*, read_*, etc.), with plurals used uniformly for list operations (list_playbooks, list_runs, list_secrets). No mixed conventions or ambiguous verbs; the naming is highly predictable and systematic.

Tool Count4/5

With 48 tools, the server covers a broad but coherent set of domains (playbooks, personas, skills, memory, canvas, runs, secrets, MCP servers, and discovery). While this exceeds the typical 3-15 range, each tool serves a distinct and necessary function within the comprehensive playbook management scope, so the count feels justified rather than bloated.

Completeness5/5

The tool surface provides complete CRUD and lifecycle coverage for every entity type: playbooks, personas, skills (including versioning and rollback), memory (including hierarchical tasks and tiering), canvas (with locking and patching), runs, secrets (including rotation and usage), and MCP servers. Additionally, find_tools covers discovery for federated tools, leaving no apparent dead ends.

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