PseudoLife-MCP

Official

by Pseudogiant-xr

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

memory_store

Destructive

Store one durable fact, decision, or observation in long-term memory for later recall across sessions.

Instructions

Store one durable fact, decision, or observation. Use proactively for anything worth keeping — one claim per call, and decide which kind it is: PERSIST what stays true and will be wanted back; leave task-scoped detail in CONTEXT ONLY; RE-VERIFY a fast-changing value at its source, parking it with memory_fact_set(..., freshness_class="volatile") rather than persisting a stale one; ASK when the claim is ambiguous instead of persisting the guess. Near-duplicates are dropped, not erred (stored=False, reason="below_surprise_threshold"). For canonical NOW use memory_fact_set.

Returns: {stored, surprise, reason, cortex_promoted}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tags`	No	Optional labels, e.g. ["decision", "blocker"].
`text`	Yes	The claim to remember.
`origin`	No	Who asserted the claim.
`source`	No	Stable per-project/topic tag for later filtering.	agent
`episode`	No	Episode handle for attribution.
`authority`	No	Speech act: "directive" (an instruction to you) / "quoted" (a doc or third party said it) / "observation"; "auto" infers.	auto
`distortion_tolerance`	No	"constraint" = must survive verbatim, pinned in recall; "auto" infers only that.	auto

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedv0.15.0Sep 3, 2026

addedInput schema / properties / authority

Added value: +{
+  "default": "auto",
+  "description": "Speech act: \"directive\" (an instruction to you) / \"quoted\" (a doc or third party said it) / \"observation\"; \"auto\" infers.",
+  "enum": [
+    "auto",
+    "directive",
+    "observation",
+    "quoted"
+  ],
+  "title": "Authority",
+  "type": "string"
+}

addedInput schema / properties / distortion_tolerance

Added value: +{
+  "default": "auto",
+  "description": "\"constraint\" = must survive verbatim, pinned in recall; \"auto\" infers only that.",
+  "enum": [
+    "auto",
+    "constraint",
+    "procedural",
+    "belief",
+    "preference",
+    "episodic"
+  ],
+  "title": "Distortion Tolerance",
+  "type": "string"
+}

addedInput schema / properties / episode / description
```
Added value: +"Episode handle for attribution."
```
addedInput schema / properties / origin / description
```
Added value: +"Who asserted the claim."
```

addedInput schema / properties / source / description

Added value: +"Stable per-project/topic tag for later filtering."

addedInput schema / properties / tags / description

Added value: +"Optional labels, e.g. [\"decision\", \"blocker\"]."

addedInput schema / properties / text / description
```
Added value: +"The claim to remember."
```

First observedv0.11.0Jul 31, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations declare destructiveHint=true and idempotentHint=false, and the description reinforces this by disclosing that near-duplicates are dropped rather than erred, with stored=False and reason='below_surprise_threshold'. It adds real behavioral context beyond the annotations, though it doesn't spell out what a store overwrites or any permission/rate constraints.

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?

Front-loads the core purpose in the first sentence and then gives a compact decision procedure. It is dense but every clause carries guidance; the parenthetical return-key detail is slightly redundant given the output schema, which keeps it just short of a 5.

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?

With an output schema present, the description need not explain return values, and it still summarizes them. For a seven-parameter mutation tool, it covers purpose, routing, dedup behavior, and the volatile-value caveat adequately, though it omits any note on overwrite/permission semantics.

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%, so the schema documents all seven parameters, and the description does not map its PERSIST/RE-VERIFY guidance onto the authority or distortion_tolerance enums. It even references a freshness_class parameter that lives on memory_fact_set, not this schema, so it adds little beyond the structured fields. Baseline 3 is appropriate.

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 (Store) and resource (one durable fact, decision, or observation) and immediately distinguishes itself from memory_fact_set, which is named as the destination for canonical NOW. An agent can tell what this tool is for and how it differs from the closest sibling.

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?

Gives explicit when-to-use guidance (proactively, one claim per call) and a decision procedure: persist durable claims, leave task-scoped detail in context only, re-verify volatile values, ask when ambiguous. It also names the alternative (memory_fact_set) and the condition that selects it, leaving little to inference.

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