Skip to main content
Glama
juyterman1000

entroly-context-engine

by juyterman1000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

resume_state

Recover lost task state by restoring a local checkpoint after a restart or context loss. Use an optional query or project to pick the most relevant checkpoint, or leave empty for the latest.

Instructions

Restore a local checkpoint into the current engine and return continuity metadata.

Use this after a restart or context loss when prior task state should be restored. Supplying query selects a relevant checkpoint; leaving it empty loads the latest checkpoint. project narrows relevance matching. The tool changes in-memory Entroly state and reads the local checkpoint store, but does not edit source files or call a model. It returns resumed, no_checkpoint_found, or no_relevant_checkpoint_found with bounded metadata and recovery context.

Use checkpoint_state to create a checkpoint and recall_relevant when you only need read-only fragment search without restoring state.

Args: query: Optional task description or terms for relevance matching. project: Optional project scope for relevance matching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional task terms used to select the most relevant checkpoint; empty loads the latest checkpoint.
projectNoOptional project path or identifier used to scope relevance matching.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.64
    • addedInput schema / properties / project / description
      Added value: +"Optional project path or identifier used to scope relevance matching."
    • addedInput schema / properties / query / description
      Added value: +"Optional task terms used to select the most relevant checkpoint; empty loads the latest checkpoint."
  2. Changed2 schema fields changedv1.0.47
    • addedInput schema / properties / project
      Added value: +{
+  "default": "",
+  "title": "Project",
+  "type": "string"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "default": "",
+  "title": "Query",
+  "type": "string"
+}
  3. First observedv1.0.42

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden and does well: it discloses that it changes in-memory state, reads the local checkpoint store, does not edit source files, and does not call a model. It also lists possible return outcomes. The only gap is that it doesn't specify whether restoring overwrites or merges with current engine state, which would matter for a destructive operation.

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 well-structured with a clear lead sentence, usage guidance, behavior, and alternatives. However, the Args section duplicates schema descriptions almost verbatim, adding redundancy. It's not bloated overall, but the repetition keeps it from being maximally tight.

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?

For a tool with two optional parameters and an output schema, the description covers everything an agent needs: what it does, when to use it, side effects and non-effects, return outcomes, and alternatives. The output schema handles detailed return structures, so nothing critical is missing.

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%, and the description's Args section largely paraphrases the schema (e.g., 'query selects a relevant checkpoint; empty loads the latest'). It adds marginal context like 'narrows relevance matching' but doesn't meaningfully extend what the schema already documents, so 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?

The description opens with a specific verb and resource: 'Restore a local checkpoint into the current engine and return continuity metadata.' It clearly differentiates itself from siblings by naming checkpoint_state and recall_relevant as alternatives, so an agent can tell which tool to pick without inspecting schemas.

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?

Explicitly states when to use it ('after a restart or context loss when prior task state should be restored') and names alternatives with their conditions: checkpoint_state for creating checkpoints and recall_relevant for read-only fragment search. This gives unambiguous routing guidance.

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

Deploy Server

Other Tools