memory_agent_getRead another agent's (or your own) org-shared ontology memory.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Harness label. Omit together with slug/id to fetch the caller. | |
| slug | No | Ontology slug, e.g. agent-cursor or agent-bc-… | |
| cursor_agent_id | No | Cursor cloud agent id (bc-…) |
| 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?
No annotations are provided, so the description carries the burden. It clearly states this is a read operation and that the memory is org-shared, which conveys an access boundary, but it does not mention permissions, side effects, failure modes, or what happens for missing agents.
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 short, front-loaded sentence with no redundant or filler words. Every word adds scope: read, audience, and resource type are all present.
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 a read-only lookup with zero required parameters and an output schema present, the description plus detailed parameter descriptions is largely sufficient for an agent to select and call the tool. It lacks only explicit alternative-tool routing and behavioral caveats, which are minor for a simple read operation.
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%, with each parameter meaningfully documented as a Harness label, ontology slug, or Cursor cloud agent id. The tool description itself adds no parameter-level meaning beyond the schema, which aligns with the baseline of 3.
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 uses a specific verb, 'Read', and a precise resource, 'another agent's (or your own) org-shared ontology memory.' This scope separates it from memory_agent_list, memory_agent_set, and content-specific getters like memory_skill_get or memory_state_get.
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?
Usage is implied rather than explicit: the tool reads another agent's shared ontology memory, and the schema notes that omitting identifiers fetches the caller. However, the description gives no direct when-to-use guidance, no exclusions, and no alternatives such as memory_recall or memory_entity_view.
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.