work_moveMove a task to a section and/or reorder it within a project.
| Name | Required | Description | Default |
|---|---|---|---|
| agent | No | Override agent identity | |
| work_id | Yes | Work item UUID | |
| project_id | Yes | Project UUID | |
| section_id | No | Target section UUID | |
| sort_order | No | Fractional rank within section |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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 provided, the description carries the full burden of behavioral disclosure. It honestly conveys that this is a mutating operation affecting section membership and ordering, but it omits side-effect context an agent would want before invoking it: whether the task is removed from its previous section, what happens when section_id is null, or whether reordering shifts other tasks' sort_order.
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 tightly worded sentence front-loads the verb and covers both supported operations ('to a section and/or reorder') with zero filler. Every word earns its place, and the structure is easy to parse at a glance.
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?
The description plus a fully documented schema and an output schema cover the 'what' and 'how' of the operation adequately. However, for a mutating tool with no annotations, the definition is missing usage routing against sibling tools and side-effect disclosure, which an agent would benefit from before calling it.
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 every parameter (work_id, project_id, section_id, sort_order, agent) already carries a meaningful description. The tool description adds no parameter-level detail beyond the schema, which fits the baseline-3 case where the schema does the heavy lifting.
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 ('Move') and names distinct resources ('task', 'section', 'project'), clearly communicating the core operation. It differentiates from siblings like work_add_to_project and work_remove_from_project by scoping the action to repositioning within a project, though it doesn't explicitly name those alternatives.
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 use case — relocating a task to a section or reordering it within a project — which hints at when it applies. However, it provides no explicit routing guidance, exclusions, or comparisons to closely related siblings such as work_add_to_project, work_update, or project_section_add, leaving the agent to infer the boundary.
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.