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mcp_engram_update

Update a memory with new content, splicing changes into the provenance log to preserve history and avoid destructive overwrites.

Instructions

CRITICAL: Use whenever you change an existing memory. NEVER forget+remember — destroys history. Superposes q + p-momentum + ProvLog splice. Prefer mcp_engram_update_with_tensor_bond for agent edits (recall-first + lineage bond). FEW-SHOT EXAMPLES: (1) Post-edit arc delta: {"concept":"store__fn__update__arc","new_text":"delta: added verify_edit_lineage helper"} (2) Design evolution: {"concept":"design:agent_tool_fidelity_v1","new_text":"Shipped composite safe_edit_and_verify","provlog_mode":"append"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conceptYesThe concept name to update
new_textYesDelta or full source text to encode + splice into provlog
provlog_modeNoOptional — default inferred from concept (append for __arc/trace:*; replace for AST __fn__/* with source-shaped text)
supersedes_ofNoOptional bi-temporal succession: after update, relate(this, old, supersedes) and append invalid_at on old. Append-only — never forgets history. Ritual: process:engram.ritual.bi-temporal-supersedes.
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses key behaviors: append-only history preservation, 'Superposes q + p-momentum + ProvLog splice', and bi-temporal succession via supersedes_of. It also warns that forget+remember 'destroys history,' which is critical safety information.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the critical warning and purpose in the first sentences. The few-shot examples are useful but add length. Some jargon ('q + p-momentum') is cryptic but not redundant. Overall, every sentence earns its place.

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 covers purpose, usage, alternatives, behavioral details, and parameter examples. It lacks explicit return-value information, but for this complex update tool with rich schema and examples, it is sufficiently complete for an agent to select and invoke correctly.

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 covers 100% of parameters, so baseline is 3. The description adds significant value by showing concrete example values for concept and new_text, explicitly explaining provlog_mode's default inference ('append for __arc/trace:*; replace for AST __fn__/*'), and adding ritual context for supersedes_of.

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 tool updates an existing memory with a specific verb+resource and scope ('Use whenever you change an existing memory'). It distinguishes from siblings by explicitly warning against the forget+remember pattern and referencing mcp_engram_update_with_tensor_bond as the alternative for agent edits.

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?

Provides explicit when-to-use ('whenever you change an existing memory'), when-not ('NEVER forget+remember'), and direct alternative ('Prefer mcp_engram_update_with_tensor_bond for agent edits'). Few-shot examples give concrete usage contexts, making selection unambiguous.

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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