Add a new element/component to a project (e.g. hardware component, software library, source, concept)
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Link to documentation, repo, datasheet, etc. | |
| kind | Yes | Type/category, freely chosen (e.g. "hardware", "software", "source", "concept", "service", "tool", "person") | |
| name | Yes | Element name | |
| projectId | Yes | Project ID | |
| properties | No | Flexible key-value pairs for additional properties | |
| description | No | Description |
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 full responsibility for behavioral disclosure. It mentions the mutation ('add') but does not describe side effects, whether duplicates are allowed, required relationship to an existing project, or what happens on success/failure. For a creation tool with no annotations, this is a significant transparency gap.
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 a single sentence that starts with the action and object, then gives illustrative examples. There is no filler or redundant restatement, making it highly efficient and front-loaded.
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 no annotations, no output schema, and a nested properties parameter, the description is too minimal to fully prepare an agent to call the tool correctly. It does not explain what the tool returns, how the project relationship is handled, or how the flexible 'properties' object should be used in practice.
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 parameters are already documented in the schema. The description's examples ('hardware component, software library, source, concept') map to the kind parameter but essentially duplicate the examples already present in the schema. It adds little meaning beyond what the schema provides.
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 clearly states the action ('Add') and resource ('a new element/component to a project'), and provides concrete examples of what counts as an element. This distinguishes it from sibling tools like add_knowledge or create_project, which target different resource types.
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 gives clear context for when to use the tool: when adding an element/component to a project. It does not explicitly state alternatives or when-not-to-use conditions, but the boundary between adding an element and other operations is reasonably clear given the sibling names and the focused description.
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.
Most tools target distinct resources (elements, knowledge, tasks, datasets, snapshots), but a few pairs blur boundaries: create_project/init_project both create projects, and pin_knowledge/set_knowledge_relevance both mark importance for future agents. The descriptions help separate them, but misselection is possible without careful reading.
Tool names consistently use snake_case verb_noun and have solid list_/get_/search_ conventions. However creation verbs are inconsistent (add_element vs create_entry vs save_dataset vs init_project), and deletion mixes delete_entry/delete_file with remove_element, making the naming pattern less predictable than it could be.
48 tools is well above the typical well-scoped range, and the set includes many lifecycle variants (create/init/save/add, delete/remove, update/set) that inflate the count. While the server covers a broad domain, the sheer number makes it heavy and harder for an agent to navigate.
The core surfaces (projects, elements, knowledge, timeline, tasks, chats, datasets, snapshots, files) have solid create/read/update coverage, with search and session-handoff tools. Notable gaps exist: read_file references a download path for binary files that no tool provides, and there is no get_entry or delete/archive for projects, datasets, snapshots, or chat sessions.