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linear-mcp-lean

by wiklob

Linear GraphQL (escape hatch)

linear_graphql

Run custom GraphQL queries and mutations against Linear's API to retrieve raw data when default fields are insufficient.

Instructions

Run an arbitrary GraphQL query or mutation against Linear's API (https://api.linear.app/graphql) and return the raw result. For the rare need neither the lean default nor full:true covers. Example: linear_graphql({query: "query($id:String!){ issue(id:$id){ identifier subscribers{ nodes{ name } } } }", variables: {id: "ENG-123"}}). Bearer-gated like every tool; errors surface, never swallowed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesA GraphQL document (query or mutation)
variablesNoVariables object for the document
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that the tool can run both queries and mutations, returns raw result, is bearer-gated like other tools, and errors are surfaced. This provides good transparency, though it could mention potential side effects of mutations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with three sentences plus an example. Information is front-loaded with purpose first. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity as a GraphQL escape hatch, the description covers purpose, usage, example, and error handling. It does not specify output format, but that is implicit in 'raw result'. Could mention that mutations can modify data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds an example usage but does not provide additional semantic meaning beyond the schema. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool runs arbitrary GraphQL queries/mutations against Linear's API and returns raw results. It distinguishes itself from sibling tools by noting it's for rare needs not covered by default or full:true.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description gives context for when to use: 'for the rare need neither the lean default nor full:true covers.' This implies it's an escape hatch. However, it does not explicitly list alternatives or state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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