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Recipes Agentic Standards Crosswalk

recipes_agentic_standards_crosswalk

Return standards-to-evidence mappings for agentic AI, MCP, and prompt-injection guidance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
source_idNo
control_idNo
standard_idNo
capability_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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 implies a read-only retrieval ('Return'), but says nothing about whether the five filter parameters are combined with AND or OR, what happens when none are supplied (does it return everything?), pagination, or result size — all material for a catalog-style lookup tool.

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?

A single front-loaded sentence with no filler, and the resource is stated before the qualifier. It is efficient, though its brevity reflects under-specification that is penalized in other dimensions rather than verbosity problems here.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-format explanation is correctly omitted. However, with no annotations, no schema descriptions, and five undocumented filter parameters, the definition leaves the agent without enough information to invoke the tool correctly or predict its filtering behavior.

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

Parameters2/5

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

Schema description coverage is 0% for five optional parameters (status, source_id, control_id, standard_id, capability_id), and the description mentions none of them. The parameter names are somewhat self-explanatory, but there is no indication of accepted values or filter interaction, so the description does not compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb ('Return') and a specific resource ('standards-to-evidence mappings') scoped to three named domains (agentic AI, MCP, prompt-injection guidance). The purpose is clear on its own, but nothing distinguishes it from the many similarly named agentic/secure-context sibling packs (e.g. recipes_agentic_control_plane_blueprint, recipes_secure_context_evidence_contract).

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

Usage Guidelines2/5

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

There is no guidance on when to call this tool versus its many siblings, no prerequisites, and no stated alternative for the reverse lookup (evidence-to-standards). The agent must infer usage entirely from the name and description.

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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