Full-text search across all knowledge entries of a project (searches title, body, context, outcome)
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Restrict to one type | |
| query | Yes | Search term | |
| 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 carries the behavioral disclosure burden. It does disclose meaningful behavior by specifying that the search is full-text and covers specific fields, which helps set expectations. However, it does not mention result format, pagination, ordering, case sensitivity, or read-only guarantees, so some behavioral context is still missing.
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 efficient sentence with no filler. It front-loads the core action and scope, then adds the searched fields as useful detail. Every word contributes value.
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?
Given the tool's low complexity, complete parameter schema, and absence of nested objects, the description is largely sufficient for an agent to understand what to invoke. The main gap is the lack of usage guidance relative to sibling tools and the absent return-value description, but these are minor for a straightforward search operation.
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 input schema already explains projectId, query, and type. The description adds a little semantic context by clarifying that the query searches across title, body, context, and outcome, but it does not meaningfully expand on parameter formats or constraints beyond what the schema provides.
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 clearly states the operation ('Full-text search'), the resource ('all knowledge entries of a project'), and the searchable fields ('title, body, context, outcome'). It is specific enough to convey what the tool does, though it does not explicitly distinguish itself from the similarly named sibling tools search_entries and search_project.
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 given about when to use this tool instead of alternatives like search_entries, search_project, get_knowledge, or list_knowledge. The description implies a general search use case but provides no exclusions, conditions, or preferred-alternative routing, leaving the agent to infer eligibility.
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.