Memwyre

save_memory

Save a new memory or document to the Memwyre Vault.
Supports short notes, conversation facts, or full long-form Markdown documents/reports of any length.
The backend automatically chunks, enriches, and indexes long text into vector and graph storage.
DO NOT summarize long reports, architecture specs, or code snippets — pass the full text directly.
Args:
    text: The full content to save. Markdown is fully supported and preserved (tables, code blocks, lists, headers).
    title: Optional title for the memory (e.g., 'Production Retrieval Benchmark' or 'System Architecture'). If omitted, one is auto-extracted from the first heading.
    source: Source of memory (default 'mcp').
    tags: Optional list of tags for categorization (e.g., ['benchmarks', 'architecture', 'database']).
    workspace_name: Optional name of the workspace directory to scope this memory.

Input Schema

TableJSON Schema

Name	Required	Default
`tags`	No
`text`	Yes
`title`	No
`source`	No	mcp
`workspace_name`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden and does a solid job: it discloses automatic chunking, enrichment, vector/graph indexing, Markdown preservation, and auto-extracted titles. It does not mention duplicate handling, permissions, or overwrite behavior, but the core side effects are 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?

The description is front-loaded with the purpose, then covers backend behavior, a critical do-not-summarize instruction, and parameter details. Every sentence adds practical value with no filler or redundancy.

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?

The definition covers content formats, long-document handling, title fallback, source default, tags, and workspace scoping, while the output schema covers return values. The only notable omission is explicit guidance for choosing between save_memory and update_memory/upsert_memory for existing content.

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

Parameters5/5

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

Schema description coverage is 0%, but the Args section compensates fully by explaining all five parameters, including defaults, examples, and behavioral details like auto-title extraction and Markdown support. This gives the agent meaning far beyond the raw JSON schema.

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?

The description states a clear verb and resource: saving a new memory or document to the Memwyre Vault, and lists supported content types. It uses 'new memory' to signal creation, which helps separate it from update_memory, though it does not explicitly name sibling tools to fully differentiate them.

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?

The description gives good content-type guidance, including when long documents should be passed in full without summarization. However, it does not explicitly state when save_memory should be preferred over sibling tools like upsert_memory or update_memory for existing content, leaving that routing implied.

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

A3.8/5.0

Disambiguation4/5

Most tools map cleanly to distinct operations such as semantic search, date search, inbox approval/discard, and tag listing. The main ambiguity is between save_memory and upsert_memory, both of which can create memories with similar arguments; get_memory/get_document and delete_memory/discard_memory are also distinguishable mainly by ID prefix or inbox state.

Naming Consistency4/5

The names are consistently lowercase snake_case and verb-first, which makes the set fairly predictable. Minor deviations include search_by_date breaking the verb_noun pattern, get_inbox/get_all_tags acting as list operations while list_memories uses 'list', and upsert_memory being an unusual verb.

Tool Count5/5

At 13 tools, the surface is well-scoped for a memory/document vault: create, upsert, update, retrieve, list, semantic and date search, tags, and inbox workflow are all represented without excessive granularity or redundant filler.

Completeness4/5

The toolset covers the core memory lifecycle well, including save, upsert, update, get, search, list, delete, and inbox approval/discard. Minor gaps remain: update_memory only accepts mem_ IDs, so documents found via search have no document-specific update path, and workspace management is only handled through optional name strings.

Resources

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