recordari

history

Read-only

Return memories ordered chronologically. Default (important_only=false) includes all memories ordered by COALESCE(occurred_at, created_at) ASC. Set important_only=true to return only memories with occurred_at set (the curated decision timeline). Pass memory_id instead of domain to scope the timeline to a single memory's neighbourhood (depth 2 by default, domain-clipped) — useful for understanding how a specific workstream evolved. memory_id takes precedence if both domain and memory_id are supplied. Optional from/to date filters apply to the effective date. Optional tags filter uses whole-word matching. Optional node_kind filter (space-separated union) restricts timeline entries to matching kinds. For importance analysis beyond the timeline, use significance. Returns lean results only — id, label, and a truncated why_matters excerpt; call recall(id) for full content. When a list or section has 2 or more results, each is rendered as a single compact text line — "[id] label — excerpt (domain, node_kind)" — instead of a JSON object; exactly one result is returned as a full object. Each line also carries the memory's effective date.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No
`from`	No
`tags`	No
`depth`	No
`domain`	No
`memory_id`	No
`node_kind`	No	Optional filter: space-separated node kinds (union match, same convention as tags). E.g. 'option standing' returns memories whose node_kind is option OR standing. Negation and wildcards are not supported.
`important_only`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedAug 31, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already mark read-only and non-destructive, and the description adds substantial operational detail: precedence of memory_id over domain, depth default, effective-date filtering, whole-word tags matching, node_kind union semantics, lean result shape, and multi-result line rendering. This goes well beyond structured annotations.

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 dense but every sentence adds operational information, with the core behavior front-loaded. While a bulleted structure could improve scannability, there is no filler or repetition.

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?

Given eight optional parameters, no output schema, and no required fields, the description explains result formatting, single vs multi-result rendering, effective dates, and filter interactions. There is no obvious gap an agent would need to make a correct invocation.

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 coverage is only 13% with just node_kind described, so the description carries the weight for all eight parameters. It explains from/to date filtering, tags whole-word matching, important_only semantics, depth, memory_id precedence over domain, and node_kind union behavior—fully compensating for the sparse 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?

States a specific verb ('Return') plus resource ('memories') and specifies chronological ordering. It also distinguishes itself from siblings by describing scope and explicitly routing importance analysis to significance and full content to recall.

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?

Explicitly tells the agent to use recall(id) for full content and significance for importance analysis beyond the timeline. It also clarifies when memory_id is useful for understanding a specific workstream's evolution, giving clear selection context.

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

A4.2/5.0

Disambiguation4/5

Each tool targets a distinct operation—lifecycle (remember/revise/forget), graph edges (connect/disconnect/why_connected), retrieval (recall/search/recent/history/orient/significance)—and the descriptions carefully disambiguate overlaps. However, the read/analysis cluster (recent, history, orient.recent, significance) could still cause initial misselection before reading the detailed descriptions.

Naming Consistency3/5

Names are readable and consistently lowercase, but they do not follow a single convention: most are bare verbs (connect, remember, revise, search, visualise) while others are nouns (domains, history, recent, significance) and two are multiword phrases (suggest_connections, why_connected). This mix is still predictable enough to navigate, but less coherent than a uniform verb_noun surface.

Tool Count4/5

16 tools is slightly above the typical 3–15 band, but the scope of a knowledge-graph memory server justifies the breadth: CRUD, connections, domain admin, and multiple query/analysis tools are all represented. No tool feels redundant enough to cut, though the set is at the heavy end.

Completeness4/5

Core lifecycle is well covered: create/read/update/archive/purge, connection creation/removal, domain admin, and multiple query/analysis modes. The main gap is that bulk domain merge (merge_domains) is referenced in tool descriptions but not exposed as an MCP tool, so agents must direct users to an external admin interface for that operation.

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