Skip to main content
Glama

intent_execute

Routes natural language math requests to the correct symbolic computation tools. Handles derivation, simplification, and solving intents.

Instructions

    🎯 Natural-language intent router — map a request to the right tool chain.

    Understands common math/derivation intents and returns the recommended
    tool(s) to call. The agent can then execute the recommended tool(s) directly.

    Args:
        intent: Natural language request
                (e.g., "derive NS equations", "simplify this", "verify derivative", "solve for x")
        expression: Optional expression to operate on
        variable: Optional variable for differentiation/solving
        session: Whether to use session-based derivation (default True)

    Returns:
        intent_type, recommended tool chain, and examples

    Example:
        intent_execute("derive the temperature corrected elimination rate",
                       expression="C0 * exp(-k*t)")
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYes
sessionNo
variableNo
expressionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. Describes return values but does not disclose side effects, mutability, or safety. Minimal behavioral disclosure beyond core functionality.

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?

Well-structured with overview, Args, Returns, and Example. Every sentence is informative and 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 complexity and lack of schema descriptions, the description fully covers purpose, parameters, usage, and return values with an example.

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?

Schema coverage is 0%, but description provides detailed explanations for all four parameters, including examples, defaults, and optionality, adding significant value.

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?

Clear verb 'map' and 'recommend', specific resource 'tool chain', and distinguishes from sibling tools like derive and tool_recommend by being a natural-language intent router.

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?

Implies usage for natural language intents and executing recommended tools, but does not explicitly state when not to use it or differentiate from alternatives like tool_recommend.

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/LBurny/symkit-mcp'

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