Skip to main content
Glama

cogvault_recall

Search an agent's persistent memory with a natural-language query. Returns the most relevant memory snippets using hybrid semantic and keyword recall.

Instructions

Search this agent's persistent memory. Pass a natural-language query; returns the most relevant memory snippets (hybrid semantic + keyword).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOnly recall cards of this frontmatter type (e.g. user, feedback, project, reference)
limitNo
queryYesWhat to recall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.2

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It usefully discloses the retrieval mechanism ('hybrid semantic + keyword') and the return shape (snippets), but omits safety-relevant and operational traits like pagination, ordering, or whether results are scoped/searched exhaustively.

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 tight sentences, front-loaded with the purpose and then the return/mechanism. Every clause earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter read tool with no output schema, the description covers purpose and return format adequately, but leaves the `type` filter and `limit` behavior to the schema and gives no guidance on result volume or refinement.

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 67% (query and type documented; limit not). The description adds the useful semantic that the query is natural-language, but says nothing about the `type` filter or how `limit` bounds results, so it does not fully compensate for the gap.

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 specific verb and resource ('Search this agent's persistent memory') and even describes the result ('most relevant memory snippets'). It is clearly distinct in intent from the sibling cogvault_record, but it never explicitly contrasts itself with that write-oriented sibling, so it stops short of a 5.

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?

It gives one concrete usage cue ('Pass a natural-language query'), which implies how to call it, but offers no when-to-use/when-not framing and never names the alternative cogvault_record for storing memories. Usage is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools