memory_ontology_rekind_entityChange the object kind of an existing ontology entity.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Alias for kind_name | |
| slug | Yes | Existing entity slug | |
| agent | No | Override agent identity | |
| kind_name | No | Registered object kind to assign, e.g. Person |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral consequences on its own. It communicates that an existing entity is mutated, but omits ownership details such as whether the previous kind is overwritten, whether the change is reversible, whether permissions are required, or what happens if kind and kind_name are both null. This is a material gap for a write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one front-loaded sentence with a clear verb and object and no redundant words. It is appropriately compact for the surface operation, and any missing content belongs to other evaluation dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an unannotated mutation tool with four parameters and an ambiguously related sibling, the description is too thin. It does not clarify side effects, null parameter behavior, or how rekind_entity differs from memory_ontology_object_kind_set, so an agent lacks enough context to select and invoke it confidently despite the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents slug, kind, kind_name, and agent. The description does not add parameter-level insight beyond the high-level intent, and it leaves the kind/kind_name alias semantics to the schema. That matches the baseline for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concise, specific operation: changing the object kind of an existing ontology entity, so the core behavior is clear. However, it does not distinguish this tool from the very similarly named sibling memory_ontology_object_kind_set, which appears to target the same concept and would confuse tool selection without further context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is given. The description does not explain when to use this tool instead of sibling alternatives such as memory_ontology_object_kind_set, memory_ontology_propose_entity, or memory_entity_view. There are no conditions, prerequisites, or exclusions to steer an agent toward the correct choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 104 tools across many domains (memory, work, projects, files, agents, context, strategic, ontology), the use of clear prefixes (memory_, work_, project_, file_, agent_run_, context_) makes most tools distinct. However, there are some potential confusions between memory_session_* vs memory_state_*, and memory_recall vs memory_think vs memory_assemble_context, though descriptions clarify their specific purposes. Aliases like memory_playbook_get for memory_procedure_get are explicit and reduce ambiguity.
Tool names follow a highly consistent pattern: prefix_domain_action (e.g., file_create, work_update, memory_recall, agent_run_start). All use snake_case, with verbs consistently placed after the domain prefix. Even less common tools like account_brief and attention_snapshot fit the overall naming scheme, making the set predictable and easy to navigate.
At 104 tools, this is an exceptionally large surface area, far exceeding the 25+ threshold that feels heavy. However, the server covers an extensive domain (organizational memory, work management, project tracking, file sharing, agent orchestration, and strategic planning), which justifies a large count. Still, the sheer number may overwhelm agents, and some tools could be consolidated (e.g., many memory_session_* and memory_state_* variants).
The tool surface is remarkably complete for its stated purpose, covering CRUD operations for files, work items, projects, and memory, plus lifecycle management for agents, sessions, and strategic plans. Minor gaps exist (e.g., no direct memory_item_get by ID, no section removal in projects), but agents can work around these using existing tools like memory_recall or work_create with parent_id. Overall, the set minimizes dead ends.