Save a complete conversation as a new chat session (title + messages as batch)
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Session title, e.g. "API design discussion" | |
| source | No | Source: "claude", "chatgpt", "slack", "meeting", etc. | |
| messages | Yes | Array of messages in chronological order | |
| metadata | No | Additional metadata (model, session ID, etc.) | |
| 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 must carry the full behavioral burden. It conveys that a new session is created and that the payload is the complete conversation, but it does not disclose expected return behavior, validation constraints, duplicate handling, or side effects beyond saving. For a mutating tool, this leaves important behavior undocumented.
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 front-loaded sentence with no redundant words; it states the action, resource, and payload shape efficiently. Every element earns its place.
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 tool has five parameters, including a nested messages object, and no output schema or annotations, so more context would help. The schema covers parameter semantics, but the description lacks explicit guidance on when to choose save_chat_session over append_chat_messages and what to expect after saving.
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 schema already documents all parameters thoroughly. The description adds only the batch/complete-conversation framing, which is useful but not necessary to understand the parameters.
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 names a specific verb ('save'), resource ('chat session'), and scope ('complete conversation', 'as a new chat session'). The 'new' and 'batch' phrasing clearly distinguishes it from sibling append_chat_messages, which would extend an existing session.
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 wording implies use when persisting a full conversation as a new session and contrasts with append_chat_messages, but it never explicitly names alternatives or states when not to use the tool. There is a clear implied context, yet no explicit exclusionary guidance.
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.