Mnemento

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record

Save structured or free-text facts to Mnemento, resolve entity targets, and flag ambiguities, duplicates, or invalid entries before storage.

Instructions

Record a fact. Either structured or free text.

    Structured: entity_type + kind (created | updated | status_changed | corrected | retracted)
    + payload, and the target by entity_id or match (field values, e.g. {"platform": "saramin",
    "posting_id": "10000001"}). Reference fields may hold a name (e.g. a company name).
    Free text: text="사람인 10000001 가상테크 열람" — the Keeper structures it.
    at: when it happened (ISO 8601 with offset); defaults to now. at_precision: time | date (only the
    day is known) | unknown. by: your agent name.
    evidence: why you believe it (quote, mail subject...).
    Fields or values the dictionary does not know are stored as given (drafts, queryable at once); the
    reply lists them under `drafts` and may ask under `questions` whether a new name means an existing one.
    New kind of record (entity_type not in list_schemas, kind created): it is stored at once under a new draft
    type. Send schema_draft with it — the schema you suggest: {"description", "keywords", "fields": {name:
    {"type", "description", "enum", "labels", "required", "indexed", "format"}}}; you know best what each field
    means now. The reply carries a proposal under `proposals`: ask the user, and on their yes call
    apply_schema_proposal. If the name looks like an existing type you are asked first (new_type=true confirms).
    Returns status:
      recorded — stored (check `drafts` and `questions`);
      clarify  — nothing stored: the target is ambiguous (several matches), a referenced name (e.g. a
                 company) is unknown or only similar, the record looks like a duplicate, or it contradicts
                 the current state; ask the user and call again;
      rejected — nothing stored: a registered field is violated (required field missing, wrong type, bad
                 date) or the target was looked up by an unregistered field;
      error    — free text could not be structured (no LLM configured or the call failed).

Input Schema

TableJSON Schema

Name	Required	Default
`at`	No
`by`	Yes
`kind`	No
`text`	No
`match`	No
`payload`	No
`evidence`	No
`new_type`	No
`entity_id`	No
`entity_type`	No
`at_precision`	No	time
`schema_draft`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 7, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it enumerates all four status outcomes (recorded/clarify/rejected/error) and states precisely what is and isn't stored in each, plus side effects like drafts, questions, and proposals. That is exactly the mutation/side-effect disclosure an agent needs before calling.

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?

The content is dense and each block (modes, drafting, status codes) earns its place, and the core purpose is front-loaded. The formatting is somewhat run-on and the nested parentheticals make it harder to scan than it needs to be.

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 12-parameter tool with zero schema descriptions and no annotations, this is about as complete as a description can be: input modes, edge cases, draft/proposal workflow, and the full status taxonomy are all covered. The output schema exists, so return-shape detail is appropriately not duplicated.

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%, so the description must compensate, and it does: it documents entity_type, kind (with its five enum values), payload, entity_id, match (with an example), text, at (ISO 8601 with offset), at_precision (time|date|unknown), by, evidence, new_type, and even the schema_draft shape. Nearly every one of the 12 parameters gains meaning absent from the 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 opening 'Record a fact. Either structured or free text.' states a specific verb and resource, and the two modes are laid out precisely. It references siblings (list_schemas, apply_schema_proposal) but never contrasts itself with record_series, the closest sibling, so the differentiation is good but not complete.

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?

Strong conditional guidance: structured vs free-text input, when to send schema_draft, when the reply produces a proposal, and to call apply_schema_proposal on user confirmation. It also tells the agent to ask the user and re-call on 'clarify'. What's missing is any explicit when-not-to-use or alternative-tool routing.

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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