Engram

mcp_engram_verify_behavior

Confirm empirical results of a hypothesis: reporting success crystallizes it into praxis; reporting failure reduces its CRS and can eventually scar it.

Instructions

TRIGGER: Call this after any hypothesis is confirmed to work OR fails in practice. Reports empirical success/failure data against a specific ZEDOS_HYPOTHESIS block. WHAT HAPPENS ON SUCCESS: Consistent successes promote the block from ZEDOS_HYPOTHESIS to ZEDOS_PRAXIS (crystallized, pinned, CRS=1.0). WHAT HAPPENS ON FAILURE: CRS is penalized. Accumulate enough failures and the block is automatically scarred. EXAMPLES: After a code fix works — verify_behavior(concept, success=true). After a fix fails — verify_behavior(concept, success=false), then consider mcp_engram_scar.

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": {
      "concept": "<concept>",
      "success": "<success>"
    },
    "name": "mcp_engram_verify_behavior"
  }
}

Input Schema

Name	Required	Description	Default
`concept`	Yes	The concept name of the hypothesis to verify
`success`	Yes	True if the behavior or rule worked successfully, false if it failed

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 24, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Without annotations, the description fully discloses behavioral consequences: on success it promotes the block from ZEDOS_HYPOTHESIS to ZEDOS_PRAXIS with crystallized, pinned, CRS=1.0; on failure it penalizes CRS and can lead to auto-scarring. This is detailed and honest about the tool's effects.

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?

The description is well-structured and concise, using bold labels for sections and front-loading the key trigger and effects. Every sentence adds value, with no redundant information.

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

Completeness5/5

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

Given the complexity of the tool (behavioral side effects, hypothesis system integration, and no output schema), the description covers trigger, success/failure outcomes, examples, and even directs to a sibling tool for further action. It provides a complete mental model for correct usage.

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 coverage is 100% with clear descriptions for both parameters. The description repeats the schema information ('concept' as name, 'success' as boolean) but adds minimal extra context, such as implicit use through examples. Baseline 3 is appropriate since the description does not significantly enhance understanding beyond the schema.

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 clearly states the trigger condition ('after any hypothesis is confirmed to work OR fails in practice') and explicitly defines the tool's purpose: 'Reports empirical success/failure data against a specific ZEDOS_HYPOTHESIS block.' It distinguishes itself from the sibling `mcp_engram_scar` by suggesting its use after failure, providing clear differentiation.

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 description provides explicit guidance on when to call the tool ('after any hypothesis is confirmed to work OR fails in practice') and what to do in the failure case ('then consider mcp_engram_scar'). It also includes concrete examples ('After a code fix works — verify_behavior(concept, success=true)'), making usage clear.

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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