Pin a knowledge entry as important — pinned entries appear first in the start_working briefing
| Name | Required | Description | Default |
|---|---|---|---|
| pinned | Yes | true = pin, false = unpin | |
| 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, the description carries the behavioral burden. It goes beyond 'pin' by disclosing the persistent user-visible effect (entries appear first in the start_working briefing), which tells the agent the operation changes ordering, not just metadata. It does not discuss permissions or failure modes, but for a simple boolean pin toggle the key behavior is stated.
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 that front-loads the action and packs the meaningful behavioral consequence after the em dash. No filler, no repetition 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?
For a three-parameter boolean toggle with all parameters documented and no nested objects or output schema, the description plus schema is enough to invoke it correctly. The only small gap is not stating explicitly that pinned=false reverses the effect, though the schema already conveys that.
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%; projectId, knowledgeId, and pinned are already documented in the schema. The description adds the real-world effect of pinned but no additional parameter-level meaning, so the baseline 3 applies.
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 opens with a specific verb and resource ('Pin a knowledge entry') and defines the intent ('as important') plus a concrete consequence: pinned entries appear first in the start_working briefing. This clearly distinguishes it from sibling knowledge tools like set_knowledge_relevance or link_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 briefing-order effect implies when this tool is useful, but the description never says 'use this when...' or contrasts it with alternatives such as set_knowledge_relevance. An agent can infer usage but gets no explicit routing 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.