FIRST TOOL on project start: Returns a structured briefing with project info, agent instructions, open tasks, pinned knowledge, and recent activity — all in one call. Sets up the session workflow: call add_knowledge() immediately when you discover something important (gotcha, decision, non-obvious insight), and call end_session() when done.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | Project ID |
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 bears the full disclosure burden. It discloses what the call returns (a structured briefing with specified components) and that it sets up a session workflow linked to add_knowledge() and end_session(). It stops short of stating whether start_working mutates session state or what errors can occur, but the core behavior is clearly conveyed.
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?
Three purposeful sentences with the key information front-loaded: tool role, return contents, and follow-up workflow. There is no redundant material or restatement of the schema.
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?
Given no output schema and no annotations, the description compensates by enumerating the briefing contents and prescribing the follow-up workflow. It leaves minor gaps like error behavior and exact response shape, but an agent has enough context to call it correctly with the required projectId.
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 covers the only parameter, projectId, with a 'Project ID' description at 100% coverage. The tool description adds no further syntax, format, or semantics for projectId, so it does not improve on the schema baseline.
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?
States a specific action and resource: it is the FIRST TOOL on project start and returns a structured briefing enumerating project info, agent instructions, open tasks, pinned knowledge, and recent activity. The 'all in one call' phrasing differentiates it from granular retrieval siblings such as get_project, list_tasks, get_knowledge, and get_activity_feed.
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?
Explicitly says to invoke it at project start and defines the session workflow by directing the agent to call add_knowledge() on important discoveries and end_session() when done. It does not explicitly name alternatives to avoid, but the first-tool positioning and workflow pairing make the intended usage clear.
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.