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Glama

anamnesis_recall

Search persistent long-term memory by semantic cue to recall past decisions, patterns, and solutions across projects when facing familiar or non-trivial problems.

Instructions

Search your long-term memory by semantic similarity. Use this at the start of any non-trivial problem, when the current context feels familiar, or when the user asks what you remember about a topic. Pass a natural-language description of what you're working on or looking for.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cueYes
top_kNo
projectNo
thresholdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It implies a read-only recall operation and supplies usage context, but never explicitly confirms no side effects, nor does it explain ranking, scoping, or result-volume behavior.

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?

Three sentences, front-loaded with purpose, then triggers, then the call pattern. Each sentence carries distinct information and nothing is padding.

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?

The output schema exists, so return values need not be described. Still, for a four-parameter tool with no annotations and no schema descriptions, the definition leaves the numeric parameters and the overlap with anamnesis_search unexplained.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must carry parameter meaning, yet it only explains cue (natural-language query). top_k, project, and threshold — including what a 0.3 threshold means or how project scopes results — are left entirely undocumented.

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?

States a specific verb (search) plus resource (long-term memory) and mechanism (semantic similarity), which is more than a restatement of the name. However, it never distinguishes itself from the sibling anamnesis_search, leaving the agent to guess which search flavor applies.

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?

Gives three concrete trigger conditions — start of a non-trivial problem, context feels familiar, user asks what you remember — which is unusually actionable. It omits when NOT to use it and never names anamnesis_search as the alternative for keyword/other lookups.

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