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synapse_record_episode

Record episodic learning memories with situation, action, outcome, and lessons so agents can avoid repeating past mistakes.

Instructions

Record an episodic learning memory (Situation -> Action -> Outcome -> Lesson) so agents never repeat past mistakes.

Input Schema

NameRequiredDescriptionDefault
titleYesShort concise episode title.
outcomeYesFinal result of the action.
salienceNoImpact factor (default 0.9).
situationYesContext or problem description.
action_takenYesAction executed to address or resolve it.
lessons_learnedYesKey takeaways or rules discovered.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It discloses that this is a persistence operation, but says nothing about whether entries are deduplicated, embedded/indexed for later recall, mutable or overwritable, or what happens when salience is changed. The 'salience' impact factor behavior is also unexplained.

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?

A single front-loaded sentence with no filler; the verb and resource lead and the rationale trails. Nothing is wasted.

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

Completeness2/5

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

A six-parameter, five-required mutation with no annotations and no output schema needs more than one sentence. Nothing tells the agent what the call returns, whether the record is immediately recallable via synapse_recall, or how salience affects later retrieval.

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 all six parameters are already documented in the schema, establishing a baseline of 3. The description's 'Situation -> Action -> Outcome -> Lesson' framing only restates the parameter names in sequence and adds no format, length, or content guidance beyond 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?

States a specific verb ('Record') and a specific resource ('episodic learning memory'), plus the narrative structure (Situation -> Action -> Outcome -> Lesson). It is clearly distinguishable from synapse_recall in intent, but it never names siblings or explicitly contrasts with synapse_retain, which is the closest functional neighbor.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The clause 'so agents never repeat past mistakes' hints at motivation but gives no condition for when to call this versus synapse_retain (both are write-side tools) or when to skip recording. No prerequisites, no exclusions, no alternatives are named.

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