update_knowledge
Update an existing knowledge entry
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | ||
| tags | No | ||
| title | No | ||
| status | No | ||
| context | No | ||
| outcome | No | ||
| projectId | Yes | Project ID | |
| knowledgeId | Yes | Knowledge entry ID |
Update an existing knowledge entry
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | ||
| tags | No | ||
| title | No | ||
| status | No | ||
| context | No | ||
| outcome | No | ||
| projectId | Yes | Project ID | |
| 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 provided, the description carries the full burden of behavioral disclosure. It only states 'Update an existing knowledge entry' and does not explain whether updates are partial or full replacements, whether specific permissions are needed, whether past versions are retained, or what the return value will be. For a mutation tool this is a significant 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 single sentence is concise and front-loaded, but given the tool has 8 parameters and no other context, the brevity is under-specification rather than efficient writing. It earns its place as a summary but does not convey the detail needed for correct invocation.
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 is far from complete for a tool with 8 parameters, no output schema, and no annotations. It omits what can be updated, how status/context/outcome relate, and any notes about partial updates or side effects. An agent would be guessing on most non-required parameters.
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 only 25% (only projectId and knowledgeId have descriptions), and the tool description does not compensate. It names no parameters and offers no hints about the meaning of body, tags, title, status, context, or outcome. An agent has almost no guidance on how to populate these fields correctly.
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 ('Update') and resource ('existing knowledge entry'), making it clear this is a modification operation rather than creation. It is distinguishable from add_knowledge and supersede_knowledge at a basic level, though it doesn't list which fields are updatable.
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?
No guidance is provided about when to use this tool versus alternatives. There is no mention of when to prefer add_knowledge, supersede_knowledge, or other update variants, leaving the agent to infer the appropriate context. The description gives no exclusions or alternatives.
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.