Skip to main content
Glama

lean_term_goal

Get the expected type (term goal) at a specified location in a Lean file by providing the file path and line number, with optional column, to see what type is expected at that point.

Instructions

Get the expected type (term goal) at a specific location in a Lean file.

Args:
    file_path (str): Abs path to Lean file
    line (int): Line number (1-indexed)
    column (int, optional): Column number (1-indexed). Defaults to None => end of line.

Returns:
    str: Expected type or error msg

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lineYes
columnNo
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description bears the full burden of disclosing behavior. It states the return type and that errors produce messages, but it does not mention potential prerequisites (e.g., a running Lean server), side effects, or failure modes beyond a generic error string. This is adequate but not rich.

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?

The description is compact and well-organized with Args/Returns sections. Every sentence adds value, and the information is front-loaded with a one-sentence purpose statement followed by concise parameter details. No wasted words.

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

Completeness5/5

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

Given the tool's simplicity (3 flat params, no nesting), the description covers all necessary aspects: purpose, parameters with defaults, and return behavior. An output schema exists, so the return format is additionally structured, making this sufficiently complete for an agent to invoke correctly.

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

Parameters5/5

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

The description explains every parameter in detail, including path type, 1-indexing for line/column, and the column default behavior (None => end of line). Since schema description coverage is 0%, this fully compensates and adds critical meaning beyond the bare schema.

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?

The description uses a specific verb ('Get') and resource ('expected type (term goal) at a specific location in a Lean file'). It clearly differentiates from siblings like lean_goal by naming the term goal concept and location-based query, making the tool's purpose unambiguous.

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

Usage Guidelines3/5

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

The description implies usage by specifying it operates at a file/line/column location, but it does not explicitly state when to choose this tool over similar siblings (e.g., lean_goal, lean_hover_info). No exclusions or alternative guidance is provided, so usage context is only implied, not explicit.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/project-numina/lean-lsp-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server