get_knowledge
Get a single knowledge entry with full content and links to other entries
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | Project ID | |
| knowledgeId | Yes | Knowledge entry ID |
Get a single knowledge entry with full content and links to other entries
| Name | Required | Description | Default |
|---|---|---|---|
| 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It communicates that this is a read operation ('Get') and indicates the return payload at a high level (full content plus links). However, it does not disclose error behavior, authorization requirements, or the structure/format of the returned links, so the disclosure is only partial.
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 with no filler. It front-loads the action and resource, and every phrase ('single', 'full content', 'links to other entries') adds useful meaning rather than restating the tool name or 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?
The tool is simple: two required, fully documented parameters and no output schema. The description provides the core semantics and a high-level description of the return value, which is mostly sufficient for correct invocation. It is slightly incomplete because it omits output structure details and error behavior, but given the low complexity, this is a minor gap.
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?
The input schema already covers both parameters with descriptions ('Project ID', 'Knowledge entry ID'), so schema description coverage is 100%. The description adds no extra parameter-level detail such as constraints, relationships, or defaults beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 ('Get') on a specific resource ('a single knowledge entry') and adds meaningful scope ('with full content and links to other entries'). This clearly distinguishes it from sibling tools like list_knowledge and search_knowledge, which retrieve collections or search results rather than one fully populated entry.
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 implies when to use the tool: when you need one complete knowledge entry by its ID. However, it does not explicitly state when not to use it or name alternatives such as search_knowledge or list_knowledge, leaving the routing decision mostly to inference.
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.