Set relevance level of a knowledge entry (higher = more important for the next agent). 0=normal, 1=relevant, 2=important, 3=critical.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | Project ID | |
| relevance | Yes | Relevance level: 0=normal, 1=relevant, 2=important, 3=critical | |
| knowledgeId | Yes | Knowledge entry 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 carries the behavioral disclosure burden. It does disclose a meaningful effect (relevance influences the next agent) and defines the numeric scale, but it does not mention overwrite behavior, persistence, side effects, or required permissions. This is partial transparency.
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 communicates the action, the scale, and the purpose with no filler. The most important qualifier ('more important for the next agent') is 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?
For a simple 3-parameter setter with fully documented parameters and no output schema, the description is nearly complete. A statement about whether the relevance value is overwritten or persisted would make it fully complete.
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 coverage is 100%, but the description adds value by providing human-readable labels (normal, relevant, important, critical) and explaining that higher values mean greater importance for the next agent, extending the bare schema descriptions.
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 ('Set') and resource ('knowledge entry'), and defines the meaning of relevance levels. It is clear on its own, though it does not explicitly distinguish itself from sibling tools like update_knowledge or pin_knowledge.
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 phrase 'more important for the next agent' implies when this tool is useful, but the description does not state when to prefer it over update_knowledge or pin_knowledge, nor any exclusions. Usage guidance is therefore only implied.
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.