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zenskar

Zenskar MCP Server

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by zenskar

createContractPrompt

createContractPrompt

Saves a reusable AI prompt for extracting contract data from raw text, enabling versioned instructions for parsing fields like dates, products, and pricing.

Instructions

Save a reusable AI extraction prompt that drives contract data extraction (used by extractContractFromRaw). Use this to create / update / version the instructions the AI follows when parsing raw contract text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prompt_nameYesIdentifier for this prompt template (e.g., 'Standard Contract Extraction', 'SaaS Subscription v2'). Used to look up / version-track the prompt.
prompt_textYesAI instructions that tell the model how to extract contract fields from raw text (e.g., what to do with dates, products, pricing tiers, billing cadence). This is the system/user prompt body the extractor will run against.
prompt_schemaNoOptional schema name pinning the expected output shape (e.g., 'billing_schema', 'subscription_schema'). Null lets the AI infer.
__userContextNoInternal user context for multi-tenant authentication and approval workflow
Behavior3/5

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

No annotations provided, so description carries full burden. Mentions versioning but does not detail update behavior (overwrite vs new version) or any side effects (destructiveness, authentication beyond schema). Adequate but not thorough.

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?

Two concise sentences front-loaded with purpose. Parenthetical note efficiently connects to sibling tool. No wasted words.

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?

Adequate for a 4-param tool with no output schema. Lacks explicit behavior on duplicate prompt_name (create vs update vs version). __userContext is well-documented in schema. Minor gap for completeness.

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 coverage is 100%, so baseline 3. Description adds no extra meaning beyond schema descriptions for parameters. Does not clarify parameter usage or format.

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?

Clearly states verb (save/create/update) and resource (reusable AI extraction prompt). Explicitly mentions sibling tool extractContractFromRaw to distinguish usage. No ambiguity.

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?

Implies when to use (for managing extraction prompts) and mentions the consumer (extractContractFromRaw). However, lacks explicit when-not-to-use or alternatives beyond the named sibling.

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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