memory_skills_listList all skills (latest version of each) in the caller's org.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag | |
| limit | No | ||
| offset | No | Pagination offset | |
| include_body | No | Include full body_md and tool_hints |
| 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 full behavioral disclosure burden. It usefully discloses that only the latest version of each skill is returned and that the scope is the caller's org. However, it does not mention pagination behavior, the default 50-item limit, or that bodies are omitted unless include_body is true. These details are in the schema, but the description itself adds only minimal behavioral context.
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?
A single sentence that front-loads the core operation and its key scoping nuance, with no filler or redundancy. Every word contributes meaning.
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?
With an output schema present and parameter defaults fully expressed in the schema, the description is sufficient for basic call selection and invocation. It could add guidance about filtering by tag or distinguishing from single-skill retrieval, but these are not critical given the schema coverage and clear purpose.
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?
The input schema covers 3 of 4 parameters (tag, offset, include_body), leaving only limit undocumented, and the description adds no parameter-level clarification. This is adequate but slightly below the high-coverage baseline because the limit parameter has no description in either the schema or the tool description, and the description does not help an agent understand pagination semantics.
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 specific action ('List'), a resource ('all skills'), and a scoping nuance ('latest version of each' in the 'caller's org'). This clearly distinguishes it from related memory_skill_get, memory_skill_set, memory_skill_resolve, and memory_skill_feedback tools.
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?
The description implies the tool is for listing skills by stating its purpose, but it does not explicitly state when to choose this over siblings such as memory_skill_get or memory_skill_resolve, nor does it give exclusion criteria. The 'latest version of each' note provides some context, but no explicit alternatives or when-not-to-use guidance is given.
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.