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run_slop

Run a SLOP script to chain MCP tool calls, transform data, and persist state across executions.

Instructions

Execute SLOP script with access to all registered MCPs. Inline script or file path. Returns final expression value as text.

Call MCP tools as mcp_name.tool_name(param: value). Example patterns:

Chain results between tools: data = api.fetch(id: 42) summary = ai.summarize(text: data["content"]) emit(summary)

Loop and collect: results = [] for id in [1, 2, 3]: results = results + [api.get(id: id)] emit(items: results, count: len(results))

Transform with builtins: repos = github.search(query: "mcp") names = map(repos, |r| r["name"]) emit(join(names, "\n"))

Pipe for chaining transforms (left value becomes first arg): [1, 2, 3, 4, 5] | filter(|x| x > 2) | map(|x| x * 10) data | json_stringify()

Session memory persists across run_slop calls (thread-safe): store_set("key", value) prev = store_get("key")

Persistent memory survives restarts (disk-backed): mem_save("bank", "key", value, description: "what this stores") data = mem_load("bank", "key") entries = mem_list("bank") matches = mem_search("query")

Use recipe parameter: recipe: "list" to see available templates, recipe: "" to load one. Use slop_reference to browse built-in functions (map, filter, reduce, json_parse, regex_match, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scriptNoInline SLOP script
file_pathNoPath to .slop file
recipeNoEmbedded recipe: 'list' to enumerate, or recipe name to load
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly discloses that the tool executes scripts, returns text, has thread-safe session memory, persistent disk-backed memory, and can access registered MCPs. It does not mention rate limits or destructive actions but adequately covers key behaviors.

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

Conciseness4/5

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

The description is lengthy but well-structured: a brief summary followed by categorized examples. It front-loads the core purpose. While some content could be trimmed, the examples are valuable for an agent and justify the length.

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 (arbitrary script execution, multiple features), the description is remarkably complete. It covers return type, memory behaviors, recipe system, and built-in functions. All parameters are documented, and the context with sibling tools is clear.

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

Parameters4/5

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

The input schema covers all three parameters with descriptions. The description adds value by explaining how to use the recipe parameter (e.g., 'list' for templates) and the choice between script and file_path. This enhances understanding beyond the schema alone.

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 clearly states the tool's purpose: 'Execute SLOP script with access to all registered MCPs.' It distinguishes itself from sibling tools like slop_help and slop_reference by focusing on execution, and provides extensive examples of what it can do.

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

Usage Guidelines5/5

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

The description provides explicit guidance on usage patterns: inline script vs file path, calling MCP tools, chaining, loops, transforms, session memory, and recipes. It also advises using slop_reference for built-in functions, helping the agent understand when to use this tool versus others.

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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