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Glama

singularis_dispatch

Run any named Singularis recipe with a JSON payload for numerical tasks not covered by built-in tools; returns the result, its check, and cost.

Instructions

Run any job from the Singularis recipe library by name with a JSON payload (see singularis_recipes for names and schemas): e.g. 'ridge' (ridge regression solve), 'circulant_solve', 'kron_apply'. Use when a numerical task is not one of the named tools. Returns the recipe's result and its check, with cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNo
recipeYes
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return shape ('the recipe's result and its check, with cost') and flags that invocation has a cost, which is genuinely useful. It says nothing about behavior on an unknown/invalid recipe name, size limits, or whether dispatch mutates state, leaving meaningful gaps for a generic executor.

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?

Three tight sentences: what it does, when to use it, what it returns. The examples are compact and load-bearing, and the routing condition is front-loaded. No filler.

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?

An output schema exists, so return values need not be detailed, yet the description still summarizes them. For an open-ended dispatcher with a free-form payload object, pointing at singularis_recipes as the schema source is the right move. The unexplained 'size' parameter is the one remaining hole.

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 description coverage is 0% across three parameters, so the description must compensate. It explains 'recipe' (a name from the library) and 'payload' (JSON payload) and helpfully redirects to singularis_recipes for per-recipe payload schemas. However, the third parameter 'size' is never mentioned or explained anywhere, so the compensation is only partial.

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?

States a specific verb and resource ('Run any job from the Singularis recipe library by name') and immediately grounds it with concrete examples ('ridge', 'circulant_solve', 'kron_apply'). It explicitly separates itself from the named siblings by saying it is for tasks that 'are not one of the named tools', so an agent can route between dispatch and singularis_gemm/singularis_fft/etc. without opening schemas.

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?

Gives a clear selection rule ('Use when a numerical task is not one of the named tools') and points to singularis_recipes for the valid names and schemas. That is actionable routing, though the exclusion is phrased against an unnamed list rather than naming a specific alternative tool, so it falls just short of fully explicit when/when-not guidance.

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