linear_searchSearch Linear issues by keyword or text. Returns matching issues with ID, title, state, priority, and URL.
| Name | Required | Description | Default |
|---|---|---|---|
| first | No | Number of results to return (default 20, max 50) | |
| query | Yes | Search query text |
| Name | Required | Description | Default |
|---|---|---|---|
| issues | Yes | Search results matching query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive (readOnlyHint, idempotentHint, destructiveHint). The description adds no further behavioral context beyond the stated return fields, but does not contradict annotations.
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 two sentences, each serving a clear purpose: first sentence states the action and resource, second lists return fields. No extraneous words, well 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?
Given the presence of an output schema and annotations, the description covers core functionality and return structure. Minor gaps exist (e.g., result ordering, case sensitivity), but overall it is complete for a straightforward search tool.
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%, so the description adds no additional meaning beyond what the schema provides. The description mentions 'by keyword or text' which aligns with the query parameter, but does not elaborate on the first parameter or provide new semantic context.
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 tool searches Linear issues by keyword or text, and lists the return fields (ID, title, state, priority, URL). This distinguishes it from related tools like linear_get_issue, but does not explicitly differentiate from linear_list_issues, which also returns issues.
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 on when to use this tool versus alternatives such as linear_list_issues or linear_get_issue. The description does not mention any conditions, exclusions, or preferences, leaving the agent to infer usage context.
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 have clearly distinct purposes, especially within their domains (e.g., Polymarket tools are well-separated). However, a few tools like ask_pipeworx, deep_research, and suggest_questions could cause minor confusion, as they all deal with querying data.
Tools from the same service use consistent prefixes (linear_, polymarket_, pipeworx_), but the overall naming style is mixed: some are verb_noun (linear_create_issue), some are noun_verb (bet_research), and some are single words (remember). This inconsistency reduces predictability.
With 35 tools, the server covers a broad range of functionality (data query, prediction markets, memory, etc.). While not excessive, the count is on the higher side, and the server name 'Linear' suggests a narrower focus, which may mislead expectations.
The tool set covers core data querying, research, entity profiles, prediction market analysis, and memory operations comprehensively. Minor gaps exist (e.g., limited Linear CRUD), but the overall surface feels complete for its intended use as a data assistant.