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emberverse

by ai.emberverse
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save_session

Save traversal results at the end of a productive session. Combines record_trace and save_crossing in one call: records the path taken as a trace and saves one cached crossing per insight. Call this after a productive traversal so future consult() calls benefit from your findings.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>",
      "insights": "<insights>",
      "problem_domain": "<problem_domain>"
    },
    "name": "save_session"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesOrdered sequence of piece IDs you traversed (minimum 2)
agent_idNoOptional: identify yourself
insightsYesOne seed per insight. Either a plain reframing sentence (seed spans the whole path), or an object {from_piece, to_piece, tension, reframing} (seed lands between those two pieces when both exist).
problem_domainYesThe kind of problem this traversal helped with (falls back to an insight object's problem_domain)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses the composite behavior: records the path taken as a trace and saves one cached crossing per insight, which is useful beyond the name. It does not cover permissions, reversibility, or error conditions, but for a save operation it is reasonably transparent.

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 the combined mechanics, then a clear usage recommendation. Every sentence earns its place with no redundant or filler text.

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?

Given no annotations and no output schema, the description is complete enough for a save tool. It ties together the composite actions, usage timing, and benefit to future calls. It omits failure modes or edge cases, but the schema already provides required parameters and constraints.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining the effect of parameters: 'path taken as a trace' and 'one cached crossing per insight', clarifying how insights map to crossings. This goes beyond the schema's field-level descriptions.

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 'Save' and resource 'traversal results' at the end of a productive session, and explicitly distinguishes itself from siblings record_trace and save_crossing by saying it combines them in one call. An agent can immediately grasp the composite nature and purpose without opening the schema.

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?

Clearly states when to use: 'Call this after a productive traversal so future consult() calls benefit from your findings.' This gives context and a reason. However, it lacks an explicit when-not or a direct alternative like 'if the traversal was not productive, use X', so it stops short of full guidance.

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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