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memory_tools_catalog

Discover teamshared MCP tools for the current turn.

Returns protocol (every-turn loop), chooser (need → tool), never (hard constraints), and grouped tools with when / avoid / copy-paste example. Pass need= when choosing a tool mid-conversation. Also returns tool_recipe_shapes, aliases (procedure_* → playbook_*), and mcp_list (the live tools/list tier gate and how to advertise extended / alias tools).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
needNoConversation router: short intent (e.g. 'share a file', 'live slack', 'create a task'). Returns matching chooser rows plus those tools' when/avoid/example. Omit to browse.
tierNoOptional filter: core, extended, or human
scopeNomemory, work, or all tool groupsall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are present, so the description carries the disclosure burden. It does so by detailing what is returned, including the need-to-tool chooser mapping, hard constraints, the live tools/list tier gate, and alias normalization. It does not explicitly say 'read-only,' but 'Discover... Returns' makes the non-mutating query nature reasonably clear.

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 short paragraphs with the one-line purpose front-loaded, followed by a compact inventory of return fields and a usage cue. Every sentence contributes information, with no filler or redundant restatement of the tool name.

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?

Given an output schema exists, the description need not explain all return values, and it covers purpose, invocation mode, and key output structures well. Minor gaps are unexplained terms such as tool_recipe_shapes and 'advertise extended / alias tools,' but these are left for the output schema to clarify.

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 input schema already fully explains need, tier, and scope. The tool description adds only a light usage note about need= and does not meaningfully extend the schema's parameter semantics, keeping this at the baseline 3.

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?

Opens with a specific verb and resource: 'Discover teamshared MCP tools for the current turn,' then enumerates concrete returned categories (protocol, chooser, never, grouped tools with when/avoid/example). This clearly differentiates it from the many sibling memory_*/work_* tools, since it is the catalog/discovery tool among 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?

Provides an explicit usage trigger: 'Pass need= when choosing a tool mid-conversation,' which tells an agent when to supply the key parameter. It does not name exclusions or alternative tools, but the sibling list contains no direct rival for this catalog function, so the context is clear.

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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