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Save raw evidence

remember

Save raw searchable evidence as durable memory, optionally tagged and namespaced, for later recall across MCP clients.

Instructions

Save raw searchable evidence. Durable intent should use the approval-gated conclusion workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional lowercase-ish tags for later filtering.
textYesMemory text to save.
sourceNoOptional source label, such as repo, thread, or tool name.
expiresAtNoOptional ISO timestamp after which recall excludes this memory.
namespaceNoOptional project, user, or client namespace.

Schema Changelog

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

  1. Changed2 schema fields changedv0.1.7
    • addedInput schema / properties / expiresAt
      Added value: +{
      +  "description": "Optional ISO timestamp after which recall excludes this memory.",
      +  "type": "string"
      +}
    • addedInput schema / properties / namespace
      Added value: +{
      +  "description": "Optional project, user, or client namespace.",
      +  "minLength": 1,
      +  "type": "string"
      +}
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden. It adds useful behavioral context by calling the memory 'raw' and 'searchable' and implying this path is not the approval-gated one. However, it does not disclose return behavior, persistence semantics, idempotency, or whether saves are immediately visible to recall tools.

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 sentences with the core action front-loaded and the routing guidance immediately after. There is no filler or redundant restatement of the title.

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?

For a simple tool with one required parameter and full schema coverage, the description is largely complete. Minor gaps remain around return values and post-save behavior, but the absence of an output schema lowers that requirement. The lack of annotations is partly mitigated by the clear 'save' and 'searchable' framing.

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 schema already documents all five parameters. The description adds only the general 'raw evidence' framing and does not add parameter-specific meaning beyond the schema, matching the baseline of 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?

The description states a specific verb-object pair, 'Save raw searchable evidence,' which clearly identifies the action and resource. It also differentiates from the conclusion workflow, so an agent can tell it apart from siblings like create_conclusion without opening schemas.

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?

The second sentence gives an explicit when-not rule: durable intent should use the approval-gated conclusion workflow instead. This provides clear routing guidance for choosing between this tool and the conclusion-based alternative.

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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