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alopez3006

snipara-mcp

by alopez3006

rlm_repl_context

Package project documentation into Python-ready context for REPL sessions, enabling context-aware code execution with helper functions for exploration and analysis.

Instructions

Bridge between Snipara's context optimization and RLM-Runtime's code execution.

PURPOSE: Package project documentation into a Python-ready format that can be injected into an rlm-runtime REPL session for context-aware code execution.

WORKFLOW:

  1. Call rlm_repl_context to get context_data + setup_code

  2. Use set_repl_context(key='context', value=context_data) to inject data

  3. Use execute_python(setup_code) to load helper functions

  4. Use helpers (peek, grep, find_function, etc.) to explore context

  5. Execute code with full documentation context available

USE CASES:

  • Implement features with documentation awareness

  • Debug code with access to related docs

  • Write tests referencing specifications

  • Refactor with architecture docs available

Returns context_data (files + sections), setup_code (helper functions), and usage hints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional query to filter context by relevance. If empty, loads files in order within budget.
max_tokensNoToken budget for file content
include_helpersNoInclude Python helper functions: peek(), grep(), sections(), files(), get_file(), search(), trim(), find_function(), list_imports(), context_summary()
search_modeNoSearch mode when query is providedhybrid
Behavior5/5

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

Despite no annotations, the description fully discloses what the tool returns (context_data, setup_code, usage hints) and the behavioral sequence (call then inject then execute). It mentions the helper functions and their purposes, providing complete transparency about the tool's behavior.

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 clear sections (PURPOSE, WORKFLOW, USE CASES, Returns). Every sentence adds information; no fluff. Front-loaded with the core purpose. Length is appropriate given the complexity – not too long, not too short.

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 4 parameters, no output schema, and no annotations, description is fully complete. It covers purpose, workflow, parameter details, return structure, and usage context. Agent has all necessary information 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?

Schema coverage is 100% (all 4 parameters described). Description adds value beyond schema by explaining how query filters context, max_tokens is a budget, include_helpers lists all helper functions by name, and search_mode enum values. This gives the agent richer understanding beyond the schema definitions.

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's purpose: package project documentation into Python-ready format for RLM-Runtime REPL. It is specific about the verb (package/inject) and resource (documentation context). Distinguishes from siblings as no other tool in the list serves this bridging function.

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?

Provides explicit 5-step workflow and list of use cases, helping AI understand when to use this tool. However, it does not explicitly state when NOT to use it or provide negative examples. Still, the workflow and use cases are clear enough for correct invocation.

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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